# Olakai --- ## Home Source: / [![Backed by AI Fund](https://olakai.ai/wp-content/uploads/2026/05/Ai-Fund-Logo-Trimmed.svg)](https://aifund.ai/) # The Enterprise AI System of Record Olakai captures and measures every AI interaction and outcome across your organization, from coding agents to assistants to autonomous agents, down to the token and its cost. AI ROI · FinOps · AI Governance · AI Engineering Productivity [Try it live See instant demo](#magic-link) [Book 15 minutes Talk to an expert](https://olakai.ai/schedule-a-demo/) > “We set a token budget late last year and blew past it by May. Everyone wanted AI, but no individual developer realized how much they were burning. Now we can see it by developer and model, and set budgets with alerts before we overrun again.” CFO · Fortune 500 financial services > “Claude Code went usage-based and our token bill 5x’d in a quarter. I had no idea who was burning tokens, let alone any way to put limits and controls in place.” VP of Engineering · Global software company > “We finally have one view of every AI tool in the company, what it touches and where the risk is, before it becomes an incident.” CISO · Healthcare enterprise > “For the first time, we walked into the board with a number, not a hope. It turned ‘we think AI is helping’ into ‘here’s exactly what it returned.’” Chief AI Officer · Global manufacturer [Latest from the Olakai Blog](/blog/) - [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) — AI Strategy · An AI system of record is the authoritative record of what your… - [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) — Industry Analysis · Video · From the AI ROI Series, recorded 9 September 2026. Two stories broke… - [ClaudeForce, and the One AI Input You Cannot…](https://olakai.ai/blog/claudeforce-ai-system-of-record/) — Industry Analysis · Video · From Enterprise AI Weekly, recorded 28 August 2026. Three things happened in… - [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) — AI Strategy · Video · From the AI ROI Series, recorded 25 August 2026. Anthropic is going… - [The Cache Tax: Where DeepSeek’s Price Increase Is…](https://olakai.ai/blog/the-cache-tax/) — Industry Analysis · From Enterprise AI Weekly, recorded 14 August 2026. On Tuesday I said… - [Four Agents Carried 70% of the Return](https://olakai.ai/blog/four-agents-70-percent-of-the-return/) — AI Strategy · From the AI ROI Series, recorded 11 August 2026. Two headlines that… - [Industry Analysis](https://olakai.ai/blog/uber-ai-budget-blowout/) — Uber Blew Through a Year of AI Budget… · Before Uber capped anything, it did the opposite. The company encouraged employees… - [AI Strategy](https://olakai.ai/blog/build-or-buy-self-hosting-ai-cost/) — Video · Build or Buy: The Arithmetic on Running Your… · From the AI ROI Series, recorded 28 July 2026. Two things happened… - [AI Strategy](https://olakai.ai/blog/custom-kpis-ai-measurement/) — Custom KPIs: The Four-Layer System Behind Olakai’s Metrics · “Custom KPIs” sounds like a settings screen — pick a formula, name… Trusted across the modern AI stack ChatGPT · Claude · Perplexity · DeepSeek · Notion AI · Figma · Grammarly · DeepL · Suno · Runway · Replit · Devin · HuggingChat · Synthesia · Descript · Jasper · Semrush · Typeform · Webflow · Otter.ai · Framer · Glean · Fireflies.ai · NotebookLM · Google Veo · Copy.ai · QuillBot · Kling AI · Pika · Krea AI · Recraft · Murf AI · PhotoRoom · remove.bg · D-ID · Writesonic · Wordtune · Tabnine · CodeRabbit · Bolt.new · Lovable · Clay · Krisp · OpusClip · AutoGPT · Apify · Lindy AI · Blackbox AI · Qodo · Granola · NovelAI - See. — Your AI ROI. · Every AI interaction and outcome measured, down to the token and its cost, across coding agents, assistants, and autonomous agents. What each dollar returns, traced to the interactions and tokens behind it. Measure AI with evidence. - Forecast. — Allocate and govern the budget. · Every AI dollar budgeted and projected to month-end at a confidence range, so the plan and the actuals reconcile. Allocated by individual, team, project, and model. AI FinOps at scale. - Control. — Govern your AI program. · Risk, policy enforcement, sensitive-data detection, Shadow AI exposure, and an exportable audit trail across every AI vendor. Right-size the models, measure productivity, and act on insights. ### ONE PLATFORM. ONE SOURCE OF TRUTH. TWO LENSES. Olakai Agentic Product ### Prove every AI coding tool's ROI. Preserve the budget. Engineering teams are spending real money on Claude Code, Cursor, Copilot, and Codex — and most leaders can't tell which tools move the business or which licenses sit idle. Olakai Agentic reads your provider data and turns it into the answer. - **Prove the ROI.** Measure what every AI coding tool and agent returns in dollars, hours, and outcomes — before and after. - **Govern the spend.** Set budgets, forecast month-end costs, and catch runaway agents or tools before they burn the budget. [Explore Olakai Agentic](https://olakai.ai/coding-iq/) ![Coding IQ cost analysis — AI spend by day, run-rate forecast, and 30-day spend simulation](/wp-content/uploads/2026/06/coding-iq-budgets-platform.webp) ![Coding IQ PR analysis — PR mix of fully agentic, human + AI assisted, and non-AI pull requests, with volume and size trends](/wp-content/uploads/2026/06/coding-iq-productivity-platform.webp) ![Coding IQ spend simulation — cumulative AI spend and a run-rate projection for the next 90 days by model](/wp-content/uploads/2026/06/coding-iq-simulate-platform.webp) ![Coding IQ developer view — per-developer tokens, real-time estimated cost, and coding-agent activity](/wp-content/uploads/2026/06/coding-iq-single-developer-platform.webp) Olakai Assistive Product ### Prove what your workforce AI is worth. Catch what's at risk. Your employees create real value with AI every day — ChatGPT, Copilot, Gemini, Claude, and 850+ more. Olakai Assistive shows where that value lands, what the licenses return, and where you're exposed. - **Prove the productivity.** Adoption and time saved across every tool, translated into dollars your licenses actually return. - **Catch the shadow AI.** Surface unapproved tools and sensitive data leaving your walls before it's an incident. [Explore Olakai Assistive](https://olakai.ai/assistive-iq/) ![Assistive IQ overview dashboard — AI value created, time saved, total cost, and data risks](/wp-content/uploads/2026/06/assistive-iq-overview-platform.webp) ![Assistive IQ license utilization across assistive AI tools — per-seat usage and unused license spend](/wp-content/uploads/2026/06/assistive-iq-licences-platform.webp) ![Assistive IQ shadow AI detection view](/wp-content/uploads/2026/06/assistive-iq-shadow-ai-platform.webp) ### Integrations ## Plug Olakai into your entire AI stack. From GitHub and Bitbucket to Claude, Cursor, Copilot, Gemini, and Bedrock — plus Microsoft 365 Copilot and 600+ assistive tools. Olakai connects out of the box and normalizes every source into one view of usage, cost, and ROI. No agent to install. [See All Integrations](https://olakai.ai/integrations/) ### Why Olakai ## The only platform built for the AI program — not the AI tool. Every AI vendor sells you a tool. Every tool gives you a dashboard. Nobody else gives you the whole program. Olakai is built differently. One platform for every copilot, every AI coding tool, and every autonomous agent — across every vendor. One place to measure ROI, govern risk, control cost, and prove what your AI is actually worth. One conversational interface — **Kai** — that anyone on your team can use to get answers. So you can stop reporting on AI one tool at a time, and finally run it like the business unit it is. ![Olakai illustration — one platform unifying every kind of AI](/wp-content/uploads/2026/04/olakai-hero-2b-transparent.png?v=2) ## Explore Olakai on your own terms. Drop your work email below and we'll send you a private link to a live Olakai environment — pre-loaded with realistic data so you can click around at your own pace, run a few Kai queries, and see exactly what your AI program would look like inside the platform. No account to create. No demo call to book. No commitment. If you want a guided walkthrough after, we're one click away. ### Get your Magic Link Enter your work email. We'll send the link in seconds. First name Last name Work email Company Your role By submitting, you agree to receive a one-time email with your Magic Link. [Privacy](/privacy-policy/). --- ## Agent Iq Source: /agent-iq [Key Features](/platform/) \| [Measure ROI](/ai-roi/) · [Govern Risk](/ai-governance/) · Agent IQ · [Custom KPIs](/analytics-kpis/) · [Monitor AI](/complete-ai-monitoring/) · [Shadow AI](/shadow-ai/) · [Integrations](/integrations/) · [Kai](/kai/) # Measure every autonomous agent’s business impact. Your enterprise is investing real money in autonomous agents — Salesforce Agentforce, Microsoft Copilot Studio, ServiceNow Now Assist, Google Agentspace, LangChain, CrewAI, AutoGen, custom-built workflows. Most leaders can’t tell which ones are actually delivering business value. Agent IQ ties every agent’s activity to dollars, hours, and outcomes. ![Agent IQ home dashboard showing autonomous agent overview](/wp-content/uploads/2026/06/agent-iq-home-platform.webp) ## When an agent fails, nobody knows. When it succeeds, nobody can prove it. Every agent platform shows you its own agents. Agentforce shows you Agentforce. Copilot Studio shows you Copilot Studio. None of them tell you whether the agent your team built last quarter is actually moving the needle on the KPI it was supposed to move. **Agent IQ sits above every platform and framework, watches every agent execution, and ties it back to the business outcome you actually care about.** ## What Agent IQ measures - Business impact, in dollars and hours — Every agent execution gets tied to real outcomes — dollars saved, hours returned, KPIs moved, processes accelerated. The way your CFO and Head of AI actually measure value, not the way an SRE measures uptime. - Governance before bad actions — Audit trails for every agent execution. Failure alerts before they cascade. Policy enforcement before an agent takes a costly action. The controls your CISO needs — without slowing down the teams shipping autonomous workflows. - Decisions about what to scale — Side-by-side ROI comparison across every agent in your enterprise. The metric that finally tells you which agents to scale next quarter, which to fix, and which to retire — backed by actual usage and outcome data. ### Agent overview ## Every agent in your organization. One dashboard. Agent IQ pulls execution data from every agent platform and framework you run — including the agents your team built in-house. The home dashboard shows you which agents are active, how much they’re costing, and what they’re delivering, in one place. No more switching between vendor consoles to understand your own AI program. - Real-time activity across every agent and workflow - Total execution volume, success rates, and cost roll-ups - Cross-platform view: Agentforce, Copilot Studio, Now Assist, LangChain, CrewAI, custom-built — all in one place ![Agent IQ home dashboard with cross-platform agent overview](/wp-content/uploads/2026/06/agent-iq-home-platform.webp) ![Agent IQ per-agent view with execution metrics and business outcomes](/wp-content/uploads/2026/06/agent-iq-agent-view-platform.webp) ### Per-agent metrics ## Click into any agent. See exactly what it’s doing — and what it’s worth. For every agent in your org, Agent IQ gives you the full picture: how many executions, what they cost, which ones succeeded, which ones failed, and — most importantly — what business outcomes they produced. Hours returned to a team. Tickets deflected. Revenue protected. Customers retained. The view your CFO actually wants when they ask “what are we getting from this agent?” ### Execution drill-down ## Trace any execution. Audit any decision. Govern any action. Drill into individual agent executions: which inputs the agent received, which tools it called, which decisions it made, and what the outcome was. The detailed view your CISO needs to govern autonomous workflows — and your engineers need to debug them. Same data, two audiences. ![Agent IQ detailed execution drill-down with audit trail and business impact](/wp-content/uploads/2026/06/agent-iq-detailed-platform.webp) ## Agent IQ doesn’t live in a silo. It runs on the same platform as [Assistive IQ](https://olakai.ai/assistive-iq/) and [Coding IQ](https://olakai.ai/coding-iq/), so the agent story rolls up into the same enterprise AI ROI dashboard your CFO and CAIO are already looking at. And because everything flows through [Kai](https://olakai.ai/kai/), you can ask your AI program a question in plain English and get a reasoned answer in seconds: *“Which of our autonomous agents delivered the most value last quarter, and which ones should we retire?”* ### Ready to measure what matters? Stop guessing. Start optimizing. [Talk to an Expert](/schedule-a-demo/) --- ## Ai Governance Source: /ai-governance [Key Features](/platform/) \| [Measure ROI](/ai-roi/) · Govern Risk · [Agent IQ](/agent-iq/) · [Custom KPIs](/analytics-kpis/) · [Monitor AI](/complete-ai-monitoring/) · [Shadow AI](/shadow-ai/) · [Integrations](/integrations/) · [Kai](/kai/) # Govern AI Risk Before It Governs You Policy Enforcement. Audit Trails. Compliance Frameworks. ## The AI Governance Problem Your teams are deploying AI faster than your policies can keep up. Every new model, agent, and copilot introduces risk—but most organizations don’t discover governance gaps until something goes wrong. **72% of enterprises have no formal AI governance framework in place.** The result: compliance exposure, audit failures, and AI initiatives that get shut down by legal before they deliver value. The EU AI Act, SOC 2 requirements, and internal risk policies demand real accountability. Spreadsheet-based compliance tracking and manual policy reviews can’t keep pace with the speed of AI adoption. **Policy blind spots** AI tools operate outside existing governance frameworks, with no visibility into what’s being used or how. * * * **Audit failures** Regulators expect complete audit trails for AI decisions—most organizations can’t produce them. **Fragmented tools** Governance is spread across vendor dashboards, spreadsheets, and tribal knowledge with no single source of truth. * * * **Manual compliance** Policy enforcement depends on people remembering rules—not systems enforcing them automatically. ### The Challenge ### Without governance guardrails, every AI deployment is a compliance risk waiting to surface. ![Olakai Assistive IQ shadow AI detection showing unapproved AI tools and sensitive data leaving the organization](/wp-content/uploads/2026/06/assistive-iq-shadow-ai-platform.webp) The Solution ## Governance That Enables, Not Blocks Define governance policies once and enforce them automatically across every AI tool, model, and agent in your stack—turning compliance from a bottleneck into an accelerator. ![Olakai governance risk overview showing PII detection, policy risks, and built-in risks across employee AI prompts with totals and trends](/wp-content/uploads/2026/06/ai-risk-governance.webp) **Policy Enforcement** Codify governance rules that map to your compliance requirements—EU AI Act, SOC 2, HIPAA, or internal policies. Olakai enforces them automatically across every AI interaction. * * * **Audit Trails** Every AI decision, interaction, and policy check is logged with full context. Generate audit-ready reports that satisfy regulators and internal review boards. * * * **Risk Scoring** Automatically assess and score AI risk by agent, tool, and department. Surface high-risk deployments before they become incidents, with real-time dashboards that security teams actually use. * * * **Compliance Reporting** Map AI usage to regulatory frameworks and generate compliance reports on demand. Track governance posture across the organization with metrics that legal and compliance teams trust. ### Ready to govern your AI? Stop blocking. Start enabling. [Talk to an Expert](/schedule-a-demo/) --- ## Ai Roi Source: /ai-roi [Key Features](/platform/) \| Measure ROI · [Govern Risk](/ai-governance/) · [Agent IQ](/agent-iq/) · [Custom KPIs](/analytics-kpis/) · [Monitor AI](/complete-ai-monitoring/) · [Shadow AI](/shadow-ai/) · [Integrations](/integrations/) · [Kai](/kai/) # Prove the Value of Every AI Investment ROI you can take to the board. ## The AI ROI Problem You’re past piloting. AI is everywhere in your organization now, and the spend is real — and climbing every month as tools shift to usage-based pricing. But when finance asks “what’s the return?”, the honest answer is still a shrug and a few anecdotes. **[NVIDIA surveyed 3,200 enterprise leaders; 30% still can’t measure their AI ROI.](/blog/nvidia-ai-report-roi-measurement/)** The gap isn’t adoption anymore — it’s proof. The teams that can put a defensible dollar figure on AI are the ones getting budget to scale it. The rest are guessing, and finance can tell. **Spend keeps climbing** Usage-based pricing means the AI bill grows every month — with no matching proof of what it’s returning. * * * **Anecdotes, not numbers** “It feels faster” doesn’t survive a budget review. Finance wants dollars, not vibes. **Siloed dashboards** Your OpenAI, Copilot, and Salesforce dashboards don’t talk to each other — or to business outcomes. * * * **No baseline** Without before-and-after data, you can’t say what AI actually changed — only that you’re paying for it. ### The Challenge ### You’re spending more on AI than ever — and still can’t prove it’s worth it. The Solution ## Olakai: AI ROI You Can Prove Olakai measures the work itself — before and after AI — and converts the time saved into dollars using your own wage data. The result is a defensible ROI number, not a vibe. ![Olakai AI ROI dashboard showing 196x ROI, AI Equivalent Engineers rising from 36 to 163, 658K dollars of value created against a 3,360 dollar AI tool cost, and value-vs-cost trends over time](/wp-content/uploads/2026/06/ai-roi-dashboard.webp) **Before-and-after ROI** Measure output before vs. after AI, by team and by tool. The delta is the value AI actually added — not a vendor’s estimate. * * * **Hours to dollars** Convert the time AI saves into dollar value using your own wage config. The number finance trusts, down to the team. * * * **AI Equivalent Engineers, and beyond** Translate productivity gains into headcount-equivalent capacity. Engineering gets AI Equivalent Engineers; the same before-and-after model measures assistive copilots across your knowledge workers and the business impact of your autonomous agents. * * * **Board-ready reporting** Value created, cost, and net ROI in one view — by team, tool, and project. Cost attribution included, ready for the budget meeting. ### Ready to prove your AI ROI? Stop guessing. Start measuring. [Talk to an Expert](https://olakai.ai/schedule-a-demo/) --- ## Analytics Kpis Source: /analytics-kpis [Key Features](/platform/) \| [Measure ROI](/ai-roi/) · [Govern Risk](/ai-governance/) · [Agent IQ](/agent-iq/) · Custom KPIs · [Monitor AI](/complete-ai-monitoring/) · [Shadow AI](/shadow-ai/) · [Integrations](/integrations/) · [Kai](/kai/) # Measure What Matters Most Custom Dashboards. Business Metrics. Real-Time KPIs. ## The AI Measurement Problem Your AI dashboards are full of data that nobody trusts. Token counts, API calls, and model latency might impress engineers, but **they mean nothing to the CFO asking “What’s the ROI?”** The metrics your AI vendors provide measure activity, not outcomes. And the business metrics that actually matter—cost per resolution, time saved, revenue influenced—require manual assembly from a dozen different sources. Without KPIs that speak your stakeholders’ language, AI investments look like cost centers instead of value drivers. **Vanity metrics** Token counts and API calls tell you AI is running—not whether it’s delivering business value. * * * **Manual reporting** Building AI performance reports means pulling data from multiple vendors and stitching it together in spreadsheets. **No baselines** You can’t measure improvement without knowing where you started—and most organizations have no AI performance baselines. * * * **Engineering-only dashboards** AI analytics are built for developers, not the business leaders who approve budgets and set strategy. ### The Challenge ### AI dashboards full of data that nobody trusts—and nobody acts on. ![Olakai Agent IQ workflow performance dashboard showing execution cost, time saved, ROI, and governance compliance aggregated across agents](/wp-content/uploads/2026/06/agent-iq-workflow.webp) The Solution ## KPIs That Speak Your Language Define the metrics that matter to your business—cost per resolution, time saved, revenue influenced—and track them in real time across every AI deployment, team, and vendor. ![Olakai custom KPIs dashboard showing task success, data extraction accuracy, processing time, and efficiency and cost trends over time](/wp-content/uploads/2026/06/agentic-custom-kpis.webp) **Custom KPIs** Build metrics that map to your business goals—not your vendor’s. Define KPIs at the team, department, or enterprise level with thresholds that trigger alerts when things change. * * * **Workflow Efficiency** Drill into efficiency gains by workflow, task, and subtask. See exactly where AI delivers value—from data processing to strategic planning—with dollar amounts attached to every improvement. * * * **Agent Comparison** Compare AI agent and model performance side by side. Benchmark success rates, costs, and business impact across vendors to optimize your AI portfolio based on actual results. * * * **ROI Reporting** Generate board-ready reports that translate AI activity into business outcomes. Export to your existing BI tools or share dashboards directly with stakeholders who need to see the numbers. ### Ready to measure what matters? Stop guessing. Start optimizing. [Talk to an Expert](/schedule-a-demo/) --- ## Answers Source: /answers # Answers Questions about Olakai, each answered from its own pages. ## AI Spend and Financials - [How does Olakai detect unused and idle AI SaaS seat licenses?](/answers/detecting-unused-ai-licenses) - [How does Olakai help CFOs track token costs and forecast AI spend before the invoice arrives?](/answers/how-olakai-helps-cfos-track-token-costs) - [How does Olakai help companies manage AI model routing to lower token expenses?](/answers/how-model-routing-reduces-ai-costs) ## AI System of Record - [What custom KPI framework does Olakai use to measure enterprise AI success?](/answers/four-layer-ai-kpi-framework) - [What is an AI system of record and what makes it different from traditional enterprise systems?](/answers/what-is-an-ai-system-of-record) ## Developer Productivity and Coding - [How does Olakai calculate pull request velocity and ROI for engineering teams?](/answers/measuring-pr-velocity-ai-coding) - [How does Olakai measure AI coding tool productivity and ROI?](/answers/measuring-ai-coding-tool-roi) ## Executive Assistant Kai - [What is Kai and how does it provide AI program insights to executives?](/answers/what-is-kai-executive-ai-assistant) ## Governance and Regulatory Compliance - [How can CISOs use an AI risk heatmap to balance governance and innovation?](/answers/ai-risk-heatmap-framework) ## Integrations and Ecosystem - [How does Olakai integrate with developer tools and Microsoft 365 Copilot?](/answers/how-does-olakai-integrate-with-developer-tools-and-microsoft-365-copilot) ## Pricing and Plans - [What features are included across Olakai pricing tiers?](/answers/pricing-tiers-and-features) ## Product Offerings - [How does Olakai track and audit autonomous AI agent executions?](/answers/auditing-autonomous-agent-executions) - [What is the difference between Olakai Agentic and Olakai Assistive?](/answers/difference-between-olakai-agentic-and-assistive) ## Security and Data Privacy - [How does Olakai secure customer data and maintain multi-tenant isolation?](/answers/olakai-security-and-data-isolation) - [What hosting and deployment options does Olakai support for enterprise environments?](/answers/olakai-deployment-options) ## Shadow AI Management - [How does Olakai discover and control shadow AI usage across an enterprise?](/answers/how-olakai-detects-shadow-ai) --- ## Ai Risk Heatmap Framework Source: /answers/ai-risk-heatmap-framework # How can CISOs use an AI risk heatmap to balance governance and innovation? Not all AI use cases carry equal risk. An AI risk heatmap prioritizes governance on two axes, business value and risk sensitivity, so controls land where exposure is real instead of blanketing everything. Governance is a spectrum, not a binary: most organizations will run AI use cases at several governance tiers at once, and that is the correct state. ## Plotting use cases by value and risk Prioritize governance based on both business value and risk sensitivity. High value, high risk use cases get governed tightly. That quadrant includes customer support agents with PII access, financial data analysis agents, contract review and drafting, and HR policy chatbots. Those need role-based access control, PII protection, comprehensive logging, human-in-the-loop review, and regular audits. High value, medium risk use cases, such as code assistants and copilots and sales research assistants, are governed moderately. Source: [AI Governance Checklist for CISOs](https://olakai.ai/blog/ciso-governance-checklist) ## Why proportional governance unlocks value Gartner's 2025 research found that organizations conducting regular AI system assessments are three times more likely to report high business value from their generative AI investments. Governance is not only risk avoidance, it unlocks value. The key insight from the same research is that governance must be proportional. Over-engineering controls for a low-risk internal tool carries its own cost. Source: [AI Risk Heatmap: Matching Governance to Business Value](https://olakai.ai/blog/ai-risk-heatmap) ## Running multiple governance tiers at once Treat governance as a spectrum. The NIST AI Risk Management Framework supplies a useful structure, with implementation tiers running from basic documentation at Tier 1 to comprehensive automated monitoring and response at Tier 4. Most organizations will have AI use cases sitting at several tiers simultaneously, and that is exactly right. Minimal governance, meaning basic logging, user feedback, and periodic review, fits internal tools and low-risk experiments. Standard governance adds comprehensive logging and access control. Source: [AI Risk Heatmap: Matching Governance to Business Value](https://olakai.ai/blog/ai-risk-heatmap) ## Also asked as - How does Olakai categorize AI tool risk across enterprise quadrants? - What is the four-quadrant AI risk heatmap framework? [← All answers](/answers) --- ## Auditing Autonomous Agent Executions Source: /answers/auditing-autonomous-agent-executions # How does Olakai track and audit autonomous AI agent executions? Olakai Agentic logs every input received, every tool called, every decision made and every outcome, per agent run. Policy is enforced at the workflow level, so violations are caught before the action is taken. The audit trail is generated continuously, not reconstructed from fragmented logs the week before a review. ## A full execution log for every agent run For every autonomous agent running inside a financial institution, including KYC workflows, fraud triage and credit decisioning, Olakai logs every input received, every tool called, every decision made, and every outcome. When a model risk team or regulator needs documentation, it exists and it is current. The capability set covers a full execution log of inputs, tools called, decisions and outcomes per agent run, policy enforcement at the workflow level that catches violations before the action is taken, and SR 11-7 and EU AI Act compliance reports generated automatically. Source: [Olakai for Financial Services](https://olakai.ai/industries/financial-services) ## The report exists before anyone asks for it When an examiner asks for an AI audit trail, the team should not spend two weeks assembling it from logs, emails and screenshots. Olakai logs every agent decision in real time, so the report exists before anyone asks for it. Source: [Olakai for Financial Services](https://olakai.ai/industries/financial-services) ## Clinical and administrative agent audit trails For every autonomous agent running in a clinical or administrative environment, covering prior authorization, clinical documentation and revenue cycle, Olakai logs every input, every tool called, every decision made and every outcome. Documentation exists when a compliance team or accreditor needs it, generated continuously inside your own infrastructure rather than assembled after the fact from fragmented logs. Policy enforcement operates at the workflow level, blocking violations before they affect patient care or billing. Source: [Olakai for Healthcare & Life Sciences](https://olakai.ai/industries/healthcare-life-sciences) ## Also asked as - How does Agent IQ trace and govern agent actions? - Can Olakai provide an execution audit trail for autonomous AI agents? ## Related questions - [What is the difference between Olakai Agentic and Olakai Assistive?](/answers/difference-between-olakai-agentic-and-assistive) [← All answers](/answers) --- ## Detecting Unused Ai Licenses Source: /answers/detecting-unused-ai-licenses # How does Olakai detect unused and idle AI SaaS seat licenses? Olakai Assistive tracks every AI tool employees touch, approved copilots and Shadow AI alike, and maps actual usage to the licenses you pay for. It shows active versus idle seats by team and by individual, exposes teams double-paying for overlapping tools, and points to where you can consolidate before the next renewal cycle. ## Mapping actual usage to every license you pay for Olakai Assistive maps real usage against every AI license on the invoice, then breaks it out by team and by individual. It reports active versus idle seats across every AI copilot license, surfaces Shadow AI cost exposure by separating tools employees expense out-of-pocket from company-approved licenses, and produces renewal recommendations on which tools to cut, consolidate, or expand based on what people actually use. Source: [Olakai for CFOs](https://olakai.ai/use-cases/cfo) ## Seat utilization by practitioner and practice group For professional services firms, Olakai Assistive maps actual usage to every AI license by practitioner, by practice group, and by tool, so idle seats are visible before the next renewal cycle. The same view identifies which practitioners are getting ROI and which need enablement, reports time saved per billable hour from AI-assisted research, drafting and analysis, and surfaces every Shadow AI tool used on client matters, risk-classified alongside the approved ones. Source: [Olakai for Professional Services](https://olakai.ai/industries/professional-services) ## Utilization and outcome in one platform Paying for 500 Copilot seats when 200 are active is a finance problem. Not knowing which 200 are delivering ROI is a strategy problem. Olakai solves both, holding utilization and outcome in one platform. Source: [Olakai for Professional Services](https://olakai.ai/industries/professional-services) ## Also asked as - Can Olakai identify inactive AI tool subscriptions to cut software waste? - How does Assistive IQ manage enterprise AI license utilization? ## Related questions - [How does Olakai help companies manage AI model routing to lower token expenses?](/answers/how-model-routing-reduces-ai-costs) - [How does Olakai help CFOs track token costs and forecast AI spend before the invoice arrives?](/answers/how-olakai-helps-cfos-track-token-costs) [← All answers](/answers) --- ## Difference Between Olakai Agentic And Assistive Source: /answers/difference-between-olakai-agentic-and-assistive # What is the difference between Olakai Agentic and Olakai Assistive? Olakai Assistive governs the AI your employees use: ChatGPT, Claude, Microsoft Copilot, and 800+ other tools, priced per employee. Olakai Agentic governs the AI your engineers and systems run: coding tools like Claude Code, Cursor, Copilot, and Codex, plus autonomous agents, priced per developer. They are two lenses on one platform, sharing one data model, one set of controls, and Kai. What you see in one lens is immediately visible in the other. ## Olakai Assistive — the workforce lens Assistive AI is the AI your people open in a browser. The questions it raises are governance and adoption questions: who is using what, what data is going into it, which tools are unsanctioned, and whether any of it is producing value worth the seats. Olakai Assistive covers: - Every employee AI interaction across 800+ tools - Shadow AI detection - Data risk, PII, and policy enforcement (DLP) - Adoption and efficiency by team, role, and region - Business impact and time saved, in dollars - Idle license optimization and tool consolidation - Custom KPIs and the OLA Index ## Olakai Agentic — the engineering and agent lens Agentic AI is metered, not seated. Cost scales with consumption rather than headcount, which makes budget the dominant risk and forecasting the dominant need. Olakai Agentic covers: - Budgets and forecasts that alert before an overrun - Cost per merged PR, the receipt for every token spent - Model routing that cuts the bill without cutting the output - AI ROI in dollars, plus Agent IQ for autonomous agents - Coding IQ analytics and governance across every coding tool - AI spend governance: budgets, forecasting, and alerts ## How to choose, and when you need both The split follows where the spend sits. A workforce rollout of chat assistants is an Assistive problem. An engineering organization burning tokens through coding agents is an Agentic problem. Assistive and Agentic are separate products and do not stack. Olakai Assistive shows no Coding IQ, Agent IQ, or spend governance; Olakai Agentic shows no Shadow AI, DLP, or Custom KPIs. Organizations that need both take **Olakai Enterprise**, which is everything in Assistive and Agentic together, with private cloud or on-premises deployment, custom SLAs, and audit-ready compliance reporting. To try either one first, **Olakai Starter** is free forever for up to 4 seats, with full platform access to one edition, Assistive or Agentic, chosen at signup. ## Also asked as - How do Olakai Agentic and Olakai Assistive differ? - What are the differences between Olakai's Agentic and Assistive products? ## Related questions - [How does Olakai track and audit autonomous AI agent executions?](/answers/auditing-autonomous-agent-executions) [← All answers](/answers) --- ## Four Layer Ai Kpi Framework Source: /answers/four-layer-ai-kpi-framework # What custom KPI framework does Olakai use to measure enterprise AI success? Olakai's custom KPI system is built in four layers of decreasing rigidity. Composites sit at the top, computed automatically from the slots that feed them. The flagship composite is ROI: Value Created divided by Execution Cost, expressed as a multiplier. ## The four layers and the ROI composite The framework runs four layers, each less rigid than the one above it. Composites sit above the slots. They are computed automatically and are not directly editable at all, because their values come entirely from the slots feeding them. The flagship composite is ROI, calculated as Value Created divided by Execution Cost and expressed as a multiplier. Below 1x means the agent costs more than it saves. Between 1x and 5x is good and worth continued investment. Above 5x is excellent and worth expanding to new use cases. You cannot tune ROI directly. The only way to move it is to refine the Execution Cost and Value Created slots feeding it, by adjusting the underlying cost formula or the hourly rate assumption. Source: [Custom KPIs: The Four-Layer System Behind Olakai's Metrics](https://olakai.ai/blog/custom-kpis-ai-measurement) ## What the metrics report on Olakai tracks efficiency gains, time saved, and cost savings per Agentic AI workflow, bottom-up and verifiable. It benchmarks performance by persona, department, and enterprise, with trend visibility, and delivers board-grade reporting and ROI insights for CIOs and CFOs. Source: [Use Cases](https://olakai.ai/use-cases) ## Measured per pillar, inside your own infrastructure Every pillar gives a different lens on AI performance. The metrics Olakai measures run across every vendor and every practitioner, inside your own infrastructure. Source: [Professional Services](https://olakai.ai/industries/professional-services) ## Also asked as - How does Olakai's four-layer KPI system work for measuring AI ROI? - What are the four layers of Olakai's custom AI metrics? ## Related questions - [What is an AI system of record and what makes it different from traditional enterprise systems?](/answers/what-is-an-ai-system-of-record) [← All answers](/answers) --- ## How Does Olakai Integrate With Developer Tools And Microsoft 365 Copilot Source: /answers/how-does-olakai-integrate-with-developer-tools-and-microsoft-365-copilot # How does Olakai integrate with developer tools and Microsoft 365 Copilot? Olakai connects at the provider and repository level, not on the developer's machine. You drop in your provider admin API keys and point Olakai at your GitHub or Bitbucket organization, and data flows within minutes. There is no heavy SDK rollout, no per-tool instrumentation, and no agent for anyone to install. Microsoft 365 Copilot is different again: Olakai is a Microsoft partner with native access to that data, so there is nothing to instrument at all. ## Connect your whole stack in an afternoon, not a quarter-long rollout The usual objection to measuring AI tooling is the rollout. Every team has to adopt something, every repository has to be wired, and the measurement lands a quarter after the budget question was asked. Olakai inverts that. Drop in your provider admin API keys and point Olakai at your GitHub or Bitbucket org, and data flows within minutes. No heavy SDK rollout, no per-tool instrumentation, no waiting on every team to adopt a new agent. Start with what you have connected today and add sources as you go, so partial coverage still answers real questions instead of stalling until coverage is total. Source: [Integrations](https://olakai.ai/integrations) ## Microsoft 365 Copilot, across Word, Excel, PowerPoint, Outlook, and Teams Olakai is a Microsoft partner with native access to your Microsoft 365 Copilot data, so there is nothing to instrument. Connect once and you immediately see how Copilot is used across Word, Excel, PowerPoint, Outlook, and Teams, with adoption, seat-level ROI, spend, and governance updating in near real time. That is the visibility Microsoft's own dashboard will not give you, and it sits alongside the rest of your AI stack rather than in a separate console. Source: [Integrations](https://olakai.ai/integrations) ## Also asked as - How does Olakai connect to GitHub, Anthropic, and Microsoft Copilot? - Does Olakai require installing an agent to measure coding AI tools? - What setup is required to connect enterprise AI tools to Olakai? [← All answers](/answers) --- ## How Model Routing Reduces Ai Costs Source: /answers/how-model-routing-reduces-ai-costs # How does Olakai help companies manage AI model routing to lower token expenses? Olakai is the vendor-neutral measurement layer that shows cost per outcome across every model and every vendor, so a routing decision can be proven rather than taken on a router's word. Most AI calls never needed the top model. Across Olakai deployments, 60 to 80% of the work gets handled just as well by a model costing five or ten times less. ## Match the model to the task The idea is simple. Most AI calls never needed the top model in the first place. Across the deployments Olakai sees, somewhere between 60 and 80% of the work, the summarizing, the extracting, the routine code, gets handled just as well by a model that costs five or ten times less. Peer-reviewed routing research (RouteLLM, presented at ICLR 2025, from researchers at UC Berkeley, Anyscale, and Canva) showed roughly 85% cost savings while holding 95% of frontier-model quality. Source: [How to Be a Smarter Token Manager: Model Routing, Explained](https://olakai.ai/blog/model-routing-explained) ## Prove the saving with a number a CFO can defend Leading an AI transformation in 2026 is not chasing the biggest model. It is matching the model to the task, measuring that the swap held quality, and proving the savings with a number a CFO can defend in a board meeting. That is the vendor-neutral measurement layer Olakai exists to provide: one place to see cost per outcome across every model and every vendor, not a router's word for it. That is how a team ships more, spends like it is its own money, and walks into the next budget review with the receipt instead of an excuse. Source: [How to Be a Smarter Token Manager: Model Routing, Explained](https://olakai.ai/blog/model-routing-explained) ## Also asked as - How can enterprise token costs be reduced using model routing in Olakai? - What is Olakai's approach to intelligent AI model routing? ## Related questions - [How does Olakai detect unused and idle AI SaaS seat licenses?](/answers/detecting-unused-ai-licenses) - [How does Olakai help CFOs track token costs and forecast AI spend before the invoice arrives?](/answers/how-olakai-helps-cfos-track-token-costs) [← All answers](/answers) --- ## How Olakai Detects Shadow Ai Source: /answers/how-olakai-detects-shadow-ai # How does Olakai discover and control shadow AI usage across an enterprise? Olakai handles Shadow AI in three steps: discover, govern, report. Discovery is automatic and continuous, scanning browsers, APIs and integrations to find AI tools as employees adopt them. Every detected tool is risk-scored and placed under an acceptable-use policy as approved, monitored or blocked. The order matters: Olakai starts with visibility, not with blocking. ## Automatic discovery and risk scoring Olakai automatically discovers every AI tool in use across your organization, assesses risk in real time, and enforces governance policies without blocking the innovation your teams need. Auto-detection continuously scans for unauthorized AI tools across browsers, APIs and integrations, discovering new AI services as employees adopt them, with no manual inventory required. Every detected AI tool is risk-scored, and acceptable-use policies classify tools as approved, monitored or blocked. Source: [Shadow AI](https://olakai.ai/shadow-ai) ## Discover, govern, report Olakai's Shadow AI capability is built around three steps: discover, govern and report. Discovery starts with a lightweight browser extension that identifies over 600 AI tools the moment an employee starts using one. It does not require SSO integration and it does not require expense report reconciliation, which is why it catches the tools that would never show up in either. Within days you have a complete inventory of every AI tool your organization is actually using, mapped to departments, users and usage patterns. Source: [The Shadow AI Opportunity](https://olakai.ai/blog/shadow-ai-opportunity) ## Visibility before blocking Olakai takes a visibility-first approach to Shadow AI detection and control. Rather than starting with blocking, it starts with discovery: what AI tools are actually being used, who is using them, what data is flowing through them, and what outcomes they are producing. That visibility layer creates the foundation for informed governance. Once you understand the full picture of AI usage, you can make intelligent decisions about what to allow, what to restrict, and what to redirect to approved alternatives. Source: [Shadow AI: The Enterprise Risk Hiding in Plain Sight](https://olakai.ai/blog/shadow-ai-enterprise-risk) ## Also asked as - How can Olakai detect unauthorized shadow AI tools in our organization? - What is Olakai's approach to identifying and managing shadow AI risk? [← All answers](/answers) --- ## How Olakai Helps Cfos Track Token Costs Source: /answers/how-olakai-helps-cfos-track-token-costs # How does Olakai help CFOs track token costs and forecast AI spend before the invoice arrives? Olakai tracks token spend by provider, team and developer, normalized across Anthropic, Cursor, OpenAI and Copilot, then projects month-end spend from a 7-day trailing average. When the trajectory crosses a threshold you configure, the alert fires before the limit is hit rather than after. Month-to-date spend is a rearview mirror; run-rate forecasting is the forward-looking signal almost no finance team has today. ## Budget forecasting for finance Olakai tracks token spend by provider, team and developer, and projects month-end spend from a 7-day trailing average. When the trajectory crosses your configured threshold, you get an alert before the limit is hit. Not after. Teams that burned their annual AI coding budget in four months were watching monthly actuals; run-rate forecasting watches the trajectory daily. What finance gets: - Token spend by provider, team and developer, normalized across Anthropic, Cursor, OpenAI and Copilot - Run-rate month-end forecast with confidence level and trajectory trend - Threshold alerts at 50%, 80% and 100% of budget Source: [Olakai for CFOs](https://olakai.ai/use-cases/cfo) ## Month-to-date is the fire report. Run-rate is the smoke alarm. Month-to-date spend is a rearview mirror. By the time April's actuals landed in Uber's financial system, the year was already gone. What every CFO needs, and almost none have, is a forward-looking signal: at the current trajectory, when do we exhaust this budget? This matters urgently because of the 18.6x nine-month consumption growth rate. Token spend does not grow linearly. It grows exponentially as more engineers adopt AI tools and as those engineers use them more heavily. Source: [Token Cost Metrics for CFOs](https://olakai.ai/blog/token-cost-metrics-cfo) ## The AI P&L The AI P&L is becoming a real thing inside enterprise finance. Token spend, cost-per-outcome, run-rate forecasting and value leak rate are the line items. The CFOs who define those metrics now, build the instrumentation to track them, and establish the governance to act on them will be in a fundamentally different position than those who wait for the token dashboards to catch up. The gap between tracking spend and understanding value is the gap between a cost center and a competitive advantage. Source: [Token Cost Metrics for CFOs](https://olakai.ai/blog/token-cost-metrics-cfo) ## Also asked as - How can finance teams forecast AI token usage and avoid budget overruns using Olakai? - What token cost metrics does Olakai provide for CFOs and finance teams? ## Related questions - [How does Olakai detect unused and idle AI SaaS seat licenses?](/answers/detecting-unused-ai-licenses) - [How does Olakai help companies manage AI model routing to lower token expenses?](/answers/how-model-routing-reduces-ai-costs) [← All answers](/answers) --- ## Measuring Ai Coding Tool Roi Source: /answers/measuring-ai-coding-tool-roi # How does Olakai measure AI coding tool productivity and ROI? Olakai measures outcome, not activity. It computes cycle time delta for AI-assisted versus non-AI pull requests, broken out by every provider you run and split into coding time, review time and total cycle, backed by your real GitHub data. Alongside that it segments every developer into Power, Casual, New or Idle cohorts and tracks token spend by provider, team and developer. Two answers come from one platform: whether AI makes your engineers faster, and whether the cost is under control. ## Every coding tool sells you adoption. None of them sell you outcome. Cursor shows you Cursor adoption. Anthropic shows you Claude Code spend. GitHub shows you Copilot acceptance rates. None of them connect that activity to shipping velocity, or warn you when a team's token burn is trending 3x over the monthly budget. Olakai connects them all: cost and velocity, end to end, from every PR. You need two answers, whether AI makes your engineers faster and whether the cost is under control, and most tools give you only one. Source: [Olakai for VPs of Engineering](https://olakai.ai/use-cases/vp-engineering) ## Cycle time, cohorts and budget control Cycle time impact is reported by tool: the cycle time delta for AI-assisted versus non-AI PRs, broken out by every provider you run, covering coding time, review time and total cycle. It is the exact metric procurement asks for, backed by your real GitHub data. Adoption coaching segments every developer in your org into Power, Casual, New or Idle cohorts, with the data you need to coach the casual users, reclaim the idle licenses, and standardize on what your power users have already chosen. Budget control tracks token spend by provider and team before the overrun. Source: [Olakai for VPs of Engineering](https://olakai.ai/use-cases/vp-engineering) ## Why the question is being forced now Engineering organizations are entering a moment where AI coding tool budgets are large enough to require accountability. The days of "it feels productive" as sufficient justification are ending. CFOs are starting to ask for the data, and boards are asking whether AI investments across the organization are generating returns. The organizations that will be able to answer are the ones that started measuring before the question was forced on them, because value without measurement is invisible. Source: [AI Coding Tool ROI](https://olakai.ai/blog/ai-coding-tool-roi) ## Also asked as - How does Olakai track developer productivity and ROI from AI coding assistants like Cursor and Copilot? - What metrics does Olakai use to prove the ROI of AI coding tools? ## Related questions - [How does Olakai calculate pull request velocity and ROI for engineering teams?](/answers/measuring-pr-velocity-ai-coding) [← All answers](/answers) --- ## Measuring Pr Velocity Ai Coding Source: /answers/measuring-pr-velocity-ai-coding # How does Olakai calculate pull request velocity and ROI for engineering teams? Olakai Agentic reads pull request data directly from your GitHub org and plots cycle time for AI-assisted versus non-AI PRs side by side, across every repo, team, and provider. AI-assisted PRs are typically 25 to 40 percent faster, and Olakai Agentic tells you whether yours are. Nothing gets installed on a developer's machine. ## What PR Analysis measures Olakai Agentic breaks coding time, review time, and total cycle time out for AI-assisted and non-AI PRs side by side. It reports PR volume and the AI code ratio, meaning the percentage of merged lines that came from AI-assisted PRs. It also gives a provider breakdown showing which tool produces faster PRs on your actual repos. Source: [For VPs of Engineering](https://olakai.ai/use-cases/vp-engineering) ## How the data gets there Coding IQ connects directly to your GitHub organization and to your AI coding tool admin APIs, covering Anthropic, GitHub Copilot, Cursor, Windsurf, and OpenAI, and pulls the data together automatically. Engineering teams do not have to build custom data pipelines or work from fragmented vendor consoles. The result is a unified view: cycle time comparison between AI-assisted and non-AI PRs, provider cost breakdown, developer adoption cohorts of Power, Casual, New, and Idle, and team-level benchmarks. Source: [Is Your $500K AI Coding Tool Investment Paying Off?](https://olakai.ai/blog/ai-coding-tool-roi) ## Baselines and cohorts behind the number The Impact Dashboard's Developers tab breaks adoption into cohorts. Its Productivity tab uses a before and after methodology that compares each developer against their own historical baseline rather than against peers. None of this requires installing anything new on a developer's machine. It runs on pull request data Olakai already has access to. Source: [Inside the AI Impact Dashboard](https://olakai.ai/blog/ai-impact-dashboard-explained) ## Also asked as - How does Olakai measure PR speedup and output from AI coding assistants? - Can Olakai track code delivery acceleration across engineering cohorts? ## Related questions - [How does Olakai measure AI coding tool productivity and ROI?](/answers/measuring-ai-coding-tool-roi) [← All answers](/answers) --- ## Olakai Deployment Options Source: /answers/olakai-deployment-options # What hosting and deployment options does Olakai support for enterprise environments? Olakai deploys as SaaS, inside your own private cloud tenancy, or fully on-premises. The capabilities are the same in every case. The architecture fits your security policy, data residency and contractual requirements rather than the other way around. ## SaaS, private cloud, or your own tenancy Most technology companies deploy Olakai as SaaS and are running in hours, with no infrastructure to manage. Where a security policy, enterprise customer contract or data residency requirement calls for something different, Olakai also runs inside your own AWS, Azure, or GCP tenancy, or fully on-premises. Same platform, same capabilities. SaaS gives the fastest time to value with no infrastructure overhead; private cloud or on-premises is available when SOC 2, data residency, or enterprise customer security demands it. Source: [Olakai for Technology & Software](https://olakai.ai/industries/technology-software) ## On-premises where operational data cannot leave the facility Many manufacturing organizations run Olakai as SaaS for their corporate AI programs, covering knowledge workers, engineering teams and supply chain functions on standard IT infrastructure. For environments where operational data cannot leave the facility, including plant-floor Agentic AI, OT-connected systems and production workflows under IEC 62443 or NIST CSF, Olakai deploys fully on-premises or inside your own private cloud. One platform, deployed where each part of the operation actually runs. Source: [Olakai for Manufacturing](https://olakai.ai/industries/manufacturing) ## What the Enterprise tier includes Olakai Enterprise combines Olakai Assistive and Olakai Agentic running on your infrastructure, with the SLAs and audit-ready reporting a risk team needs. It covers private cloud, on-prem, or managed SaaS, with zero Olakai access to your data, custom SLAs and dedicated support, audit-ready compliance and risk reporting, and volume and multi-year pricing. It is priced custom, for unlimited users, and aimed at regulated and large organizations that need the full picture and full control. Source: [Pricing](https://olakai.ai/pricing) ## Also asked as - Can Olakai be deployed on-premises or in a private cloud? - What are the deployment options for Olakai? ## Related questions - [How does Olakai secure customer data and maintain multi-tenant isolation?](/answers/olakai-security-and-data-isolation) [← All answers](/answers) --- ## Olakai Security And Data Isolation Source: /answers/olakai-security-and-data-isolation # How does Olakai secure customer data and maintain multi-tenant isolation? Olakai enforces tenant isolation in the application layer: every database query is scoped by account ID, across all repositories, use cases, and server actions, backed by automated tests and query guards. Data is encrypted in transit with TLS 1.2+ and at rest with AES-256, and the practices behind both are backed by an independent SOC 2 Type II examination. ## Compliance and certifications Olakai maintains rigorous security practices across all infrastructure and operations, backed by an independent SOC 2 Type II examination. Source: [Trust & Security](https://olakai.ai/trust) ## Multi-tenant isolation and access control Every database query is scoped by account ID. No customer can access another customer's data. This is enforced at the application layer across all repositories, use cases, and server actions, and supplemented by automated tests and query guards. The design decision worth naming is that isolation does not rest on the discipline of whoever writes the next query. Query guards fail the request, and the automated tests fail the build. Source: [Trust & Security](https://olakai.ai/trust) ## Encryption standards All data is encrypted in transit and at rest using industry-standard algorithms. - **Data in transit** — TLS 1.2+ on all external connections. HTTPS enforced on all endpoints, with HTTP 80 redirecting to 443. - **Data at rest** — AES-256 via AWS RDS for the database and AWS S3 for file storage. AES-256-GCM for sensitive application fields. - **Sessions and credentials** — HMAC-SHA256 signed JWTs in HTTP-only, Secure, SameSite cookies. Passwords stored using bcrypt one-way hashing. Source: [Trust & Security](https://olakai.ai/trust) ## Also asked as - What are Olakai's security, encryption, and data isolation practices? - Is Olakai SOC 2 Type II compliant? - Where is Olakai hosted and how is tenant data separated? ## Related questions - [What hosting and deployment options does Olakai support for enterprise environments?](/answers/olakai-deployment-options) [← All answers](/answers) --- ## Pricing Tiers And Features Source: /answers/pricing-tiers-and-features # What features are included across Olakai pricing tiers? Olakai has four tiers: Starter, Assistive, Agentic, and Enterprise. Starter is free forever for up to 4 seats on one edition. Assistive covers Assistive AI usage tracking, Shadow AI detection and DLP, and Custom KPIs. Agentic covers Coding IQ, Agent IQ, and AI spend governance. Enterprise combines both and adds on-prem deployment, custom SLAs, and audit-ready compliance reporting. Ask Kai is included in every tier. ## What each tier includes The feature comparison splits the platform along two lines. Assistive AI usage tracking, Shadow AI detection and DLP, and Custom KPIs with the OLA Index are in Olakai Assistive and Olakai Enterprise, not Olakai Agentic. Coding IQ (AI coding tool analytics), Agent IQ (autonomous agent analytics), and AI spend governance covering budgets, forecasting, and alerts are in Olakai Agentic and Olakai Enterprise, not Olakai Assistive. Olakai Starter carries the first five of those capabilities limited to one edition. Ask Kai, the conversational insights layer, is in all four tiers. Seat caps are 4 on Starter and unlimited on Assistive, Agentic, and Enterprise. On-prem deployment and custom SLAs with dedicated support are Enterprise only. Source: [Pricing](https://olakai.ai/pricing) ## Olakai Starter Starter is free forever, capped at 4 seats. You choose one edition at signup, either Olakai Assistive or Olakai Agentic, and get full platform access for those seats. It delivers real usage, adoption, and cost data from day one, includes the Ask Kai AI assistant for answers in plain language, deploys in your cloud or ours, and upgrades whenever the team grows. It is built for teams that want usage and ROI evidence before rolling AI out further. Source: [Pricing](https://olakai.ai/pricing) ## Olakai Enterprise Enterprise is everything in Olakai Assistive and Olakai Agentic together, running on your infrastructure. Pricing is custom and users are unlimited. It offers private cloud, on-prem, or managed SaaS deployment, with zero Olakai access to your data. It adds custom SLAs and dedicated support, audit-ready compliance and risk reporting, and volume and multi-year pricing. The tier is aimed at regulated and large organizations that need the full picture, full control, and audit-ready proof. Source: [Pricing](https://olakai.ai/pricing) ## Also asked as - What is included in Olakai Starter versus Olakai Enterprise? - How is Olakai priced across Assistive and Agentic tiers? [← All answers](/answers) --- ## What Is An Ai System Of Record Source: /answers/what-is-an-ai-system-of-record # What is an AI system of record and what makes it different from traditional enterprise systems? An AI System of Record is the authoritative, vendor-neutral record of everything an organization's AI does. It captures every AI interaction and outcome, from coding agents to assistants to autonomous agents, down to the token and its cost, and structures it into one data model by user, team, department, project, agent, model, and vendor, kept over time. What separates it from a CRM, an ERP, or an HRIS is that its data is captured rather than entered, it spans vendors by design, and its basic unit is the individual interaction. ## What the term actually means An AI System of Record is the authoritative, vendor-neutral record of everything an organization's AI does. It captures every AI interaction and outcome, from coding agents to assistants to autonomous agents, down to the token and its cost, and structures it into one data model by user, team, department, project, agent, model, and vendor, kept over time. Enterprises already accept this idea everywhere else. Finance has an authoritative record, HR has one, revenue has one. AI is the exception, which is why the answer to "what is our AI program worth" gets assembled by hand from vendor consoles and expense reports, quarter after quarter. Source: [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record) ## How AI breaks the assumptions traditional systems were built on Three assumptions fail at once. Its data is captured automatically rather than entered by people, so completeness depends on coverage rather than on discipline. A CRM is only as good as the reps who update it. An AI System of Record is only as good as what it is connected to, which is a different problem with a different fix. It spans vendors by design, because no company runs all of its AI through one provider. Every vendor will show you your usage of that vendor. None of them will tell you what you are getting per dollar across all of them. Its basic unit is the individual interaction and the tokens behind it, which means the volume is measured in millions of events rather than thousands of transactions. That changes the storage model, the query model, and the cost model underneath it. Source: [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record) ## Also asked as - What does an AI system of record do? - How is an AI system of record different from HRIS, CRM, or ERP? - Why do enterprises need an AI system of record? ## Related questions - [What custom KPI framework does Olakai use to measure enterprise AI success?](/answers/four-layer-ai-kpi-framework) [← All answers](/answers) --- ## What Is Kai Executive Ai Assistant Source: /answers/what-is-kai-executive-ai-assistant # What is Kai and how does it provide AI program insights to executives? Kai is the conversational layer on top of Olakai, spanning both Olakai Agentic and Olakai Assistive. Instead of building a dashboard or filing a request with the analytics team, an executive asks the question directly and gets a reasoned, data-backed answer in seconds, with the full reasoning shown behind every number. ## Ask your AI program anything Kai is the conversational layer on top of Olakai. Instead of building a dashboard or filing a request with the analytics team, executives just ask: ROI by department, Shadow AI in legal, which agents are underperforming, which engineering teams are getting the most out of Cursor. Kai pulls the data, runs the analysis, and shows you the reasoning behind every answer. For board reporting it delivers a recommendation covering governance health, measurable value created and prioritized next actions. Source: [Kai](https://olakai.ai/kai) ## One layer across both products Kai is the conversational layer across both Olakai Agentic and Olakai Assistive. Ask it anything about your AI program and get cross-cutting answers in seconds, with full reasoning shown: in plain English, in real time, with audit-ready math behind every number. Typical questions look like this: - "What's our total AI spend this month, and which team is driving it?" - "Which AI tools are producing the most value per dollar spent?" - "Are we forecast to overrun any coding budget before month-end?" Source: [Olakai for Heads of AI](https://olakai.ai/use-cases/head-of-ai) ## Answer the board, the CFO and the CISO from one place Ask Kai any question about your AI program, whether it concerns ROI, risk, adoption, cost or cycle time, and get a reasoned, data-backed answer in seconds. No reports to build. No analyst in the middle. Source: [Olakai for Heads of AI](https://olakai.ai/use-cases/head-of-ai) ## Also asked as - How does Olakai's Kai natural language assistant work? - What types of board and executive questions can Kai answer about enterprise AI? [← All answers](/answers) --- ## Assistive Iq Source: /assistive-iq [The Platform](/platform/) \| The Products » [Olakai Agentic](/coding-iq/) · Olakai Assistive # Track every copilot. Catch every shadow AI. Your employees use a lot more AI than IT thinks. ChatGPT, Microsoft Copilot, Google Gemini, Claude, Perplexity, Harvey, Descript, Jasper, Writer, Notion AI — and 620+ more show up in the enterprise stack every month. Assistive IQ sees all of it: approved, unapproved, and quietly buried inside your SaaS apps. [Try it live](#magic-link) ![Assistive IQ overview dashboard with AI value created, time saved, total cost, and data risks](/wp-content/uploads/2026/06/assistive-iq-overview-platform.webp) ## Most enterprises track 5 AI tools. The reality is closer to 50. Every team has signed up for an AI tool you’ve never heard of. Marketing has Jasper. Sales has Apollo. Legal has Harvey. Design has Gamma. Engineering has Perplexity. The tools your CIO approved are the tip of an iceberg — the rest is being expensed quietly, used daily, and feeding sensitive data into systems no one’s reviewed. **Assistive IQ deploys via a browser extension and surfaces every AI tool your team is actually using — including the ones nobody approved.** ## What Assistive IQ measures - ROI for every AI tool — Every assistive AI interaction gets measured against productivity outcomes: time saved, cost per use, OLA Index quality scores, adoption depth. Across 630+ tools your team is actually using — from ChatGPT and Microsoft Copilot to Harvey, Descript, and Perplexity. - Shadow AI control — Detect every unauthorized AI tool the moment an employee starts using it. Enforce content policies on every prompt. Govern what sensitive data your teams can send to which tools. The shadow AI surface, finally visible — and finally controllable. - Adoption that’s actually real — Benchmark adoption by department, region, role, and team. See which groups are getting real value from a tool versus which are just clicking through it. The data your CAIO needs to decide what to scale, what to consolidate, and what to retire. ### Overview ## AI value, time saved, and risk — in one dashboard. The Assistive IQ overview gives leaders the four numbers that matter most: dollars of AI value created, hours saved, total tool cost, and data risk exposure. Track ROI in real time, segmented by team and tool. The board-ready view of your assistive AI program. - AI value created and total time saved across every assistive tool - Total licensing cost vs utilized licenses, with reclaim recommendations - Live data risk indicators (PII, PHI, code, security) per tool and per team ![Assistive IQ overview dashboard](/wp-content/uploads/2026/06/assistive-iq-overview-platform.webp) ![Assistive IQ app-level view showing per-app analytics, governance status, and data risk indicators](/wp-content/uploads/2026/06/assistive-iq-app-detail-platform.webp) ### App-level detail ## Analytics and governance down to the individual app. Click into any AI tool — Perplexity, ChatGPT, Copilot, or any of the 630+ apps Assistive IQ tracks — and see its value created, time saved, cost, prompt quality score, and data risk in one view. IT governance status, licensing, and risk level sit right next to the analytics, so measurement and control live in the same place. - Per-app ROI: value created, time saved, and cost trend over time - Prompt quality scoring and data risk indicators (PII, PHI, security) - IT governance status: licensed vs shadow AI, risk level, monitoring config ### License management ## Find the seats you’re paying for. And nobody’s using. Every AI tool charges per seat. Every enterprise over-provisions. Assistive IQ shows you exactly which licenses are sitting idle — by tool, by team, by user — and gives you a precise reclaim list. The procurement view that pays for the platform in the first quarter. Then layer in the data your CAIO needs: which tools have overlapping use cases, which can be consolidated, and which are quietly delivering 10x more value than the headline product. ![Assistive IQ license tracking and reclaim view](/wp-content/uploads/2026/06/assistive-iq-licences-platform.webp) ![Assistive IQ shadow AI detection view with risk surfaces](/wp-content/uploads/2026/06/assistive-iq-shadow-ai-platform.webp) ### Shadow AI detection ## Catch shadow AI before it becomes a compliance incident. The browser extension surfaces every unauthorized AI tool the moment an employee starts using it — even if it never showed up in your SSO logs or expense reports. Then Olakai ranks each by risk surface: which tools are processing PII, which departments are most exposed, which prompts contain sensitive data. Govern. Approve. Block. With confidence — backed by real usage data, not guesswork. ## Assistive IQ doesn’t live in a silo. It runs on the same platform as [Coding IQ](https://olakai.ai/coding-iq/) and [Agent IQ](https://olakai.ai/agent-iq/), so the assistive AI story rolls up into the same enterprise AI ROI dashboard your CFO and CAIO are already looking at. And because everything flows through [Kai](https://olakai.ai/kai/), you can ask your AI program a question in plain English and get a reasoned answer in seconds: *“Has Harvey usage actually grown since we rolled it out to legal — and is it worth standardizing on?”* ## Explore Assistive IQ on your own terms. Drop your work email below and we’ll send you a private link to a live Assistive IQ environment — pre-loaded with realistic data so you can click through AI value created, license utilization, shadow AI detected, risk surfaces, and adoption cohorts at your own pace. No account to create. No demo call to book. No commitment. If you want a guided walkthrough after, we’re one click away. ### Get your Magic Link Enter your work email. We'll send the link in seconds. First name Last name Work email Company Your role By submitting, you agree to receive a one-time email with your Magic Link. [Privacy](/privacy-policy/). --- ## Blog Source: /blog BLOG # Enterprise AI Blog: Strategy, Governance & ROI Insights Practical insights for scaling AI with confidence — from ROI to governance [![Executive between legacy ledger books and a modern glass office, representing the AI system of record](https://olakai.ai/wp-content/uploads/2026/09/what-is-an-ai-system-of-record-featured-1024x585.webp)](https://olakai.ai/blog/what-is-an-ai-system-of-record/) AI Strategy ## [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) An AI system of record is the authoritative record of what your AI does, costs, and produces. Learn the definition, its five properties, and how to build one. September 10, 2026 [Read Article →](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) — Industry Analysis · From the AI ROI Series, recorded 9 September 2026. Two stories broke this week that most people filed under separate headlines: OpenAI’s Astra, and Anthropic \[…\] · September 9, 2026 - [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) — Industry Analysis · From Enterprise AI Weekly, recorded 28 August 2026. Three things happened in enterprise AI that week, and read together they settle an argument the industry \[…\] · August 31, 2026 - [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) — AI Strategy · From the AI ROI Series, recorded 25 August 2026. Anthropic is going public, and it is shaping up to be the largest listing in history, \[…\] · August 25, 2026 - [The Cache Tax: Where DeepSeek’s Price Increase Is Concentrated](https://olakai.ai/blog/the-cache-tax/) — Industry Analysis · From Enterprise AI Weekly, recorded 14 August 2026. On Tuesday I said compute gets more expensive from here and that the falling price per token \[…\] · August 14, 2026 - [Four Agents Carried 70% of the Return](https://olakai.ai/blog/four-agents-70-percent-of-the-return/) — AI Strategy · From the AI ROI Series, recorded 11 August 2026. Two headlines that week are worth translating into tokenomics. Intel asked Wall Street for $15 billion \[…\] · August 11, 2026 - [Uber Blew Through a Year of AI Budget in Four Months. The Guardrail It Built Next Already Existed.](https://olakai.ai/blog/uber-ai-budget-blowout/) — Industry Analysis · Before Uber capped anything, it did the opposite. The company encouraged employees to use AI coding tools “as much as possible” and put usage on \[…\] · July 31, 2026 - [Build or Buy: The Arithmetic on Running Your Own Model](https://olakai.ai/blog/build-or-buy-self-hosting-ai-cost/) — AI Strategy · From the AI ROI Series, recorded 28 July 2026. Two things happened that week, and they are more connected than they look. Visa cut 2,600 \[…\] · July 28, 2026 - [Custom KPIs: The Four-Layer System Behind Olakai’s Metrics](https://olakai.ai/blog/custom-kpis-ai-measurement/) — AI Strategy · “Custom KPIs” sounds like a settings screen — pick a formula, name a metric, done. What’s actually interesting about Olakai’s KPI system for AI agents \[…\] · July 22, 2026 - [Your AI Got Cheaper. Your Bill Didn’t.](https://olakai.ai/blog/your-ai-got-cheaper-your-bill-didnt/) — AI Strategy · From the AI ROI Series, recorded 21 July 2026. Apple raised prices on Macs and iPads and pointed straight at AI, saying the data centre \[…\] · July 21, 2026 - [Even Google Can’t Ship Its Best AI](https://olakai.ai/blog/google-cant-ship-best-ai/) — Industry Analysis · TSMC bet another $100 billion this week. Google can’t ship its best model. And the evaluations that actually decide enterprise AI purchases just went private. \[…\] · July 17, 2026 - [The EU AI Act Is Now Enforceable. Is Your AI Governance Ready?](https://olakai.ai/blog/eu-ai-act-enforcement-august-2026/) — AI Governance · Two EU AI Act deadlines are converging this summer — a July 22 Code of Practice signing window and an August 2 enforcement date for chatbot disclosure and GPAI fines. Governance built after the fact won’t hold up. · July 16, 2026 - [Shadow AI, Caught in the Act: Inside Olakai’s App Catalog and Policy Alerts](https://olakai.ai/blog/shadow-ai-app-catalog-policy-alerts/) — AI Governance · “We have a Shadow AI problem” is a sentence a CISO says a lot and rarely means precisely. It could mean dozens of unclassified tools \[…\] · July 15, 2026 - 1 - [2](https://olakai.ai/blog/page/2/) - [3](https://olakai.ai/blog/page/3/) - [4](https://olakai.ai/blog/page/4/) - [5](https://olakai.ai/blog/page/5/) - [6](https://olakai.ai/blog/page/6/) - [Next](https://olakai.ai/blog/page/2/) --- ## 30 Day Ai Pilot Source: /blog/30-day-ai-pilot [← Back to Olakai's Blog](/blog/) # The 30-Day AI Pilot That Actually Proves Value ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. February 26, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Seventeen active AI pilots. $2.3 million in annual spend. Zero measurable business outcomes. That was the state of AI at a mid-market professional services firm when their CFO finally asked the question everyone had been avoiding: “Which of these should we actually scale?” Nobody could answer. Not because the pilots weren’t working — several were. But none had been designed to produce the data needed to make a scaling decision. They were experiments without exit criteria, running indefinitely on the premise that “we’ll figure out ROI later.” Later never came. This is pilot purgatory — and MIT’s 2025 State of AI research found that [95% of enterprise AI pilots deliver zero measurable financial return](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/). Not low returns. Zero. That’s roughly $30-40 billion in destroyed shareholder value from AI pilots running worldwide without the measurement infrastructure to prove they’re worth continuing. ## The Pilot Purgatory Problem The data on AI pilot failure is stark. S&P Global Market Intelligence found that the average enterprise scrapped 46% of AI pilots before they ever reached production in 2025. Bain’s executive survey reported that only 27% of companies successfully moved generative AI from testing to real-world implementation. And McKinsey’s State of AI report found that nearly two-thirds of organizations remain stuck in pilot phase, unable to scale projects across the enterprise despite significant adoption. The financial toll is substantial. Industry analysis estimates that pilot purgatory costs the average enterprise $15-25 million annually in wasted development resources, infrastructure spending, and opportunity costs. Individual pilot failures run $500,000 to $2 million each. And the cost grows every month a pilot runs without producing decision-quality data, because the organization continues investing without the information needed to decide whether that investment is justified. The root cause isn’t technical. Most AI pilots work from a technical standpoint — the models perform, the integrations function, the users adopt the tools. The root cause is that pilots are designed to test technology, not prove business value. They answer “can this AI tool do the thing?” when the question the organization needs answered is “should we invest more in this AI tool?” ## Why 30 Days Is the Right Timeframe Enterprise best practice points to a 30-to-45-day window as the optimal pilot duration. Short enough to maintain executive attention and organizational momentum. Long enough to generate statistically meaningful data on business outcomes. Shorter pilots (under three weeks) don’t capture enough data to distinguish signal from noise, especially for use cases where business outcomes lag behind AI activity — like lead qualification, where the revenue impact shows up when leads close, not when they’re scored. Longer pilots (three to four months) generate more data but introduce a different risk: losing stakeholder attention. By month three, the executive sponsor has moved on, the team working on the pilot has been pulled to other priorities, and the pilot drifts into that twilight zone where it’s too expensive to kill and too poorly measured to champion. The 30-day pilot isn’t about speed for its own sake. It’s about creating a forcing function — a defined moment where the organization must decide: scale, fix, or kill. That decision point is what separates pilots that generate value from pilots that generate costs. ## Pre-Pilot: Setting Up for a Decision The 30-day clock doesn’t start when the AI tool gets deployed. It starts when the measurement infrastructure is in place. Before the pilot begins, four things must be defined: **The business outcome KPI.** Not “accuracy” or “adoption” — the business outcome that this AI initiative should change. Revenue influenced, costs reduced, time recovered, errors prevented. This is the metric that will appear in the scaling decision. If you can’t name it before the pilot starts, you’re not ready for the pilot. Our [AI ROI framework](/blog/ai-roi-framework/) provides a methodology for identifying the right success KPI by use case. **The baseline.** What is the current performance on that KPI without AI? If the AI agent is supposed to reduce customer support resolution time, what’s the current average? If it’s supposed to improve lead conversion, what’s the current conversion rate? Without a baseline, there is no counterfactual, and without a counterfactual, there’s no way to attribute improvement to AI versus other factors. **The success threshold.** How much improvement constitutes a “scale” decision? What range triggers a “fix” decision? What level triggers a “kill” decision? These thresholds must be agreed upon before the data comes in. Post-hoc threshold setting is subject to confirmation bias — teams will unconsciously set the bar wherever the data lands. **The decision authority.** Who makes the scale/fix/kill call on day 30? If this isn’t defined upfront, the pilot’s data will be debated indefinitely by stakeholders with competing interests. The decision authority needs to be a single individual (typically the executive sponsor) with the organizational power to allocate or reallocate budget based on the results. ## During the Pilot: What to Measure Once the pilot is running, measurement operates on two tracks. **The outcome track** measures the business KPI you defined pre-pilot. This is the number that matters for the scaling decision. Track it weekly so you can see trend direction, but don’t make decisions based on week-one data. Enterprise AI use cases need at least two to three weeks for patterns to stabilize, especially in workflows with downstream dependencies like sales pipeline or compliance review. **The diagnostic track** measures operational and technical metrics that help you understand why the outcome KPI is moving (or not). If resolution time is dropping, the diagnostic track tells you whether that’s because the AI is providing better answers, because agents are spending less time searching for information, or because the easiest tickets are being routed to AI first. If the outcome KPI isn’t improving, the diagnostic track tells you where to look: data quality issues, workflow integration problems, user adoption gaps, or a fundamental mismatch between the AI capability and the business need. McKinsey’s research is clear on the value of this approach: [organizations that define and track AI-specific KPIs see nearly two-thirds meet or exceed their targets](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai). The measurement itself doesn’t cause success — the discipline of defining what matters and instrumenting it creates organizational clarity that makes success more likely. ## Day 30: The Decision Point This is where most enterprises fail — not because they lack data, but because they lack a framework for using it. The day-30 decision uses four inputs: **Outcome KPI performance vs. threshold.** Did the AI initiative hit the success threshold you defined pre-pilot? If yes, the data supports scaling. If it’s in the “fix” range, the diagnostic data tells you what to change. If it’s below the “kill” threshold, the data supports sunsetting the initiative and reallocating resources. The threshold was set before the data arrived, so this isn’t a subjective judgment. It’s a data-driven decision. **Cost-to-value ratio.** What was the total cost of the pilot (tooling, infrastructure, team time, opportunity cost) versus the total value generated? Even at pilot scale, this ratio signals whether scaling will be financially viable. If the cost-to-value ratio is favorable at pilot scale, it typically improves at production scale due to economies. **Governance and risk profile.** Can the AI initiative operate within your organization’s risk tolerance at production scale? Data security concerns, compliance requirements, and [governance gaps](/blog/ciso-governance-checklist/) that are manageable at pilot scale can become critical at production scale. If the governance profile isn’t ready for scaling, the decision might be “fix governance first, then scale.” **Operational readiness.** Does the organization have the operational capacity to absorb the change at scale? User training, workflow integration, support infrastructure, and change management all need to be assessed. A pilot that works with 50 engaged early adopters may perform differently when deployed to 5,000 users with varying levels of enthusiasm and technical proficiency. ## What Successful Enterprises Do Differently The enterprises that escape pilot purgatory share three characteristics. First, they secure executive sponsorship with decision authority, not just endorsement. Organizations with top-level executive mandate scale AI three times faster and achieve significantly higher revenue impact compared to those stuck at pilot stage. Second, they instrument measurement from day one, not after the pilot shows promising results. This means defining KPIs, establishing baselines, and deploying tracking before the AI tool goes live — not retrofitting measurement after the fact. Retrofitting measurement costs three to four times more than building it in from the start and produces lower-quality data because the baseline period is missing. Third, they redesign workflows rather than just deploying tools. McKinsey found that [AI high performers are 2.8 times more likely to redesign workflows](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) (55% versus 20%) compared to other organizations. Dropping an AI tool into an existing workflow and measuring whether the workflow speeds up is the lowest-value form of AI measurement. Redesigning the workflow around AI capabilities and measuring the redesigned outcome is where the step-change improvements come from. ## Breaking Free Pilot purgatory isn’t a technology problem. It’s a measurement problem. The AI works. The organization just can’t prove it — because it never built the measurement infrastructure to generate decision-quality data in a defined timeframe. The 30-day structured pilot is the DECIDE step in the [SEE, MEASURE, DECIDE, ACT playbook](/blog/enterprise-ai-roi-playbook/). (This is the third of four companion deep-dives — see also [SEE](/blog/ai-visibility-audit/), [MEASURE](/blog/ai-metrics-that-matter/), and [ACT](/blog/ai-roi-act-framework/).) It takes the visibility data from SEE and the business metrics from MEASURE and converts them into a concrete decision: scale, fix, or kill. No more indefinite experiments. No more “let’s give it another quarter.” No more pilot purgatory. The enterprises moving from [AI experimentation to business impact](/blog/ai-experimentation-impact/) are the ones that commit to structured measurement before the pilot starts and structured decisions when the data comes in. The framework isn’t complicated. The discipline is what’s hard. And the cost of avoiding it — $15-25 million per year in wasted pilot investment — far exceeds the cost of getting it right. **Ready to run an AI pilot that actually produces a decision?** [Talk to an expert](/schedule-a-demo/) and we’ll show you how Olakai instruments AI measurement from day one — so your 30-day pilot generates the data your board needs to say yes. [The Enterprise Leader’s Toolkit for Navigating Agentic AI](https://olakai.ai/blog/future-of-agentic-enterprise-toolkit/) [What Is AI Analytics? The Definitive Enterprise Guide](https://olakai.ai/blog/what-is-ai-analytics/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Agent Roi Lessons Source: /blog/ai-agent-roi-lessons [← Back to Olakai's Blog](/blog/) # What 100+ AI Agent Deployments Taught Us About Proving ROI ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. February 5, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) A voice AI agent in a retail call center was handling thousands of calls per month. Costs were down. Resolution rates were up. The operations team was thrilled. Then the CFO asked a question no one could answer: *“How much revenue did this thing actually generate?”* The basic metrics — calls handled, cost per call, resolution rate — told an efficiency story. But efficiency doesn’t get budget renewed. Revenue does. When the team finally tracked qualified leads that converted within 30 days, the agent proved thousands of dollars in quarterly value. Not cost savings. Revenue. That’s the gap hiding in plain sight across enterprise AI today. And after measuring more than 100 AI agent deployments across retail, financial services, healthcare, and professional services, we’ve seen the same pattern repeat with remarkable consistency. ## The $2.5 Trillion Question Nobody Can Answer Global AI spending is projected to reach [$2.5 trillion in 2026](https://www.gartner.com/en/newsroom/press-releases/2026-1-15-gartner-says-worldwide-ai-spending-will-total-2-point-5-trillion-dollars-in-2026), according to Gartner. AI now represents more than 40% of total IT spending. Yet MIT’s Project NANDA found that 95% of companies see zero measurable bottom-line impact from their AI investments within six months. Read that again. Trillions in spend. Ninety-five percent with nothing to show the CFO. The problem isn’t that AI doesn’t work. The agents we’ve measured do work — they resolve tickets, qualify leads, process documents, flag anomalies. The problem is that most enterprises never connect that activity to business outcomes. They measure what’s easy (calls handled, tokens processed, tasks completed) instead of what matters (revenue influenced, costs avoided, risk reduced, time recovered). This is why 61% of senior business leaders now report *more* pressure to prove AI ROI than they felt a year ago, according to [Fortune’s 2025 CFO confidence survey](https://fortune.com/2025/12/17/cfo-confidence-rebounds-delivering-ai-value-next-test-2026/). The era of “trust us, AI is helping” is over. ![The AI Measurement Gap — $2.5T global AI spend vs. only 5% can prove ROI impact, with findings from MIT, Fortune, and Olakai data](https://olakai.ai/wp-content/uploads/2026/02/ai-measurement-gap-inline.webp) ## What 100+ Deployments Actually Taught Us Across more than 100 measured agent deployments, we’ve identified four patterns that separate the 5% who prove ROI from the 95% who can’t. ### 1\. They Define the Success KPI Before Deployment The retail voice AI example above illustrates this perfectly. The operations team measured what they controlled: call volume, handle time, resolution rate. All green. But the finance team needed to see qualified leads that converted — a metric that crossed departmental boundaries and required connecting the agent’s activity to CRM data 30 days downstream. The enterprises that prove ROI identify this “success KPI” before the agent goes live. Not after. Not when the CFO asks. Before. It’s the single metric that answers the question: *If this agent works perfectly, what business outcome changes?* ### 2\. They Measure the Counterfactual, Not Just the Output One financial services firm deployed an AI agent to flag compliance anomalies. The agent flagged 340 issues in its first quarter. Impressive? The team thought so — until someone asked how many of those would have been caught by the existing manual process. The answer was 312. The agent’s real value wasn’t 340 flags. It was 28 catches that would have been missed, each representing potential regulatory exposure worth six figures. Measuring output without a baseline is vanity metrics dressed up as ROI. The question isn’t “what did the agent do?” It’s “what would have happened without it?” ### 3\. They Track Cost-to-Value, Not Just Cost-to-Run Enterprise AI cost conversations almost always focus on infrastructure: compute costs, API calls, token usage. These matter, but they’re only half the equation. A customer success agent we measured cost $4,200 per month to run — and prevented an average of $47,000 in monthly churn by identifying at-risk accounts three weeks earlier than the human team. The cost-to-run looked expensive in isolation. The cost-to-value ratio was 11:1. The enterprises that scale AI investment successfully present both numbers to finance. They don’t defend the cost. They contextualize it against the value. ### 4\. They Build Governance Into Measurement, Not Around It Here’s the pattern that surprised us most. The deployments with the strongest ROI data weren’t the ones with the most sophisticated AI models. They were the ones with the most rigorous governance frameworks. Why? Because governance forces you to define what the agent is allowed to do, which forces you to define what success looks like, which forces you to instrument the metrics that prove value. Governance and measurement aren’t separate workstreams. They’re the same workstream. Organizations that treat them as separate end up with compliant agents they can’t prove are valuable, or valuable agents they can’t prove are compliant. ## The SEE → MEASURE → DECIDE → ACT Framework These four patterns map to a framework we’ve refined across every deployment: **SEE:** Get [unified visibility into what AI agents are actually doing](/complete-ai-monitoring/) across your organization. Not just which agents exist, but what they’re touching — which data, which workflows, which customer interactions. You can’t measure what you can’t see, and most enterprises have agents running in places they don’t even know about. **MEASURE:** Connect agent activity to the success KPIs that matter to the business. This means going beyond operational metrics (tokens, latency, uptime) to outcome metrics (revenue influenced, costs avoided, risk mitigated). It also means establishing baselines so you can measure the counterfactual. **DECIDE:** Use measurement data to make scaling decisions. Which agents get more budget? Which get sunset? Which workflows should be automated next? Without measurement, these decisions are political. With measurement, they’re strategic. **ACT:** Scale what’s working, fix what’s not, and govern the entire portfolio continuously. This is where most enterprises stall — not because they lack the will, but because they lack the data to act with confidence. The framework isn’t complicated. But it requires designing measurement and governance from day one, not bolting them on after deployment. Enterprises that bolt on measurement retroactively spend 3-4x more time and money instrumenting metrics than those who build it in from the start. ## Why This Matters Now Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by the end of 2026 — up from less than 5% in 2025. That’s an 8x increase in one year. Meanwhile, 58% of organizations still cite unclear ownership as their primary barrier to [measuring AI performance](/ai-roi/), and 62% lack a comprehensive inventory of the AI applications they’re running. The math is straightforward. Agent proliferation is accelerating. Measurement capability is not keeping pace. The gap between AI activity and AI accountability is widening every quarter. And the organizations that close that gap first will be the ones who scale AI investment while their competitors are still stuck in [pilot purgatory](/blog/ai-experimentation-impact/), unable to answer the CFO’s question. In 2026, AI is being judged less on promise and more on proof. The playbook for providing that proof exists. It starts with seeing what you have, measuring what matters, deciding with data, and acting with confidence. If your enterprise is deploying AI agents and struggling to prove their value, you’re not alone — but the organizations pulling ahead aren’t waiting for better AI. They’re building better measurement. [Our AI ROI framework](/blog/ai-roi-framework/) breaks down the methodology, and [Future of Agentic’s success KPI library](https://futureofagentic.com/success-kpis) offers specific metrics by use case. **Ready to see what your AI agents are actually worth?** [Talk to an expert](/schedule-a-demo/) and we’ll show you how enterprises are turning AI activity into measurable business outcomes. [What ServiceNow’s $8B AI Acquisition Spree Tells Us About the Future of Enterprise AI](https://olakai.ai/blog/servicenow-ai-acquisitions-governance/) [JP Morgan Spent $2B on AI. Here’s What They Measured.](https://olakai.ai/blog/jpmorgan-ai-measurement/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Bill Eating Everything Else Source: /blog/ai-bill-eating-everything-else [← Back to Olakai's Blog](/blog/) # The AI Bill Is Eating Everything Else ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. July 14, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) From the AI ROI Series, recorded 14 July 2026. [IBM](https://www.ibm.com) lost about $55 billion in market value in a single session, the stock fell more than 20%, and it was the company’s worst day since 1987. Preliminary second-quarter revenue came in at $17.2 billion against a consensus near $17.86 billion. The reason came straight from CEO Arvind Krishna. In the final weeks of June, clients redirected their capital expenditure toward servers, storage, and memory, racing to lock in supply-constrained infrastructure before prices climbed. In his words, IBM “did not anticipate the magnitude of the capex reprioritization.” And then, rather more bluntly, “this quarter we faltered.” Almost everyone covered this as a company having a bad quarter, which it plainly was. Infrastructure fell 7%, large software deals that were supposed to close did not, and consulting was roughly flat. I look at it from my seat, which is spent inside enterprise AI budgets, and from there the interesting part is where the money went rather than which line it came out of. ## The tell is what happened to everyone else Accenture, Cognizant, ServiceNow, Adobe, and Workday all sold off on somebody else’s earnings, which is the market pricing a pattern rather than a company. Enterprise AI spending is still climbing, and the thing worth understanding is that it is now climbing at the expense of the budget lines next to it. Every dollar going into compute, memory, and tokens is a dollar that did not go into software licenses, consulting engagements, and the rest of what an enterprise buys. Which means every line item in your budget now has to justify itself against the AI line item, and that includes the AI line item itself. When AI is the thing crowding out everything else, AI had better be able to show what it returned. So let us look at where that money actually goes, because I promised those numbers and, honestly, they surprised me. ## Where the token money actually goes What Share Why it matters Input tokens, reading your codebase \~90% of token usage You pay mostly to read, not to write Input tokens as a share of cost \~70% The cost lives on the input side Output tokens, once caching is counted 0.6% of usage Writing the code is a rounding error Cost without caching \~10x higher Caching is the biggest hidden lever in the bill *Source: Cursor aggregated usage data, via [The Pragmatic Engineer](https://newsletter.pragmaticengineer.com). Figures as at July 2026.* That table upends how most people picture their coding bill. The money goes on having the model read your codebase and your documentation, over and over, on every turn. Output is very nearly a rounding error at 0.6% of usage once cache reads are counted, and Cursor’s own data says that without smart caching the cost would be roughly ten times higher. Caching sits somewhere between a tuning detail and the single largest variable in the invoice, and most organisations I talk to have never looked at it. ## The correction I owe you Here is a number that complicates something I have been saying for months, so let me be straight about it rather than quietly move on. Model Cost per agent request Cost per accepted line Opus 4.7 \~$1.57 roughly equal to GPT-5.5 GPT-5.5 \~$0.81 roughly equal to Opus Composer 2.5 \~$0.18 cheapest per request *Source: Cursor aggregated usage data, via The Pragmatic Engineer. Figures as at July 2026.* Opus costs about twice as much per agent request as GPT-5.5, and on that number alone it looks like an easy cut. Measure cost per line of code that actually survives review, though, and the two land in roughly the same place, because more of the expensive model’s output gets accepted. So the simple version of the [routing argument](/blog/model-routing-explained/), which is to send everything to the cheaper model, turns out to be too simple. Routing is still right, and it is still the highest-leverage lever available, but the unit you route on has to be cost per accepted output. Optimise the wrong unit and you will cut the bill while quietly destroying the value underneath it, which is the same trap as [measuring acceptance rate on its own](/blog/ai-coding-tool-roi-metrics/). ## The governance number in the same dataset One more figure, and it is the one that should make a CTO put down their coffee. In the span of a single month, the share of developers letting AI agents commit code with no manual review went from about 10% to around 40%. Four in ten developers are no longer personally checking the output. So you are paying for tokens, on input you are not measuring, producing code that increasingly nobody reads, inside a stack where you cannot see what any of it returned. The cost problem and the [governance problem](/ai-governance/) are arriving in the same quarter, which is inconvenient, because most organisations have separate teams and separate timelines for the two. ## What the AI natives are doing about it [Perplexity](https://www.perplexity.ai) is quietly building its own internal coding tool, codenamed Teammate, to run software projects end to end. Two things about that are worth your attention. The first is the economics: if you are an AI company paying a model vendor for tokens, you are funding a competitor, so you build. The second is that Teammate is deliberately model-agnostic. They are designing routing in from day one, because they understand that locking yourself to a single model is a cost trap and a capability trap at once. The companies closest to the tokens are the ones being most disciplined about them, and that is worth sitting up for. Their CTO reportedly told engineers they should be able to “stop looking at code” by the end of the year. Put that next to the 40% who already are not, and the direction is not especially subtle. ## The uncomfortable question for consulting Now the part that will be uncomfortable for a lot of people reading this. If AI agents do work that used to be billable hours, what is an hour worth? IBM’s consulting line was flat while clients poured money into compute, and Accenture and Cognizant sold off on IBM’s numbers, so the advisory world is standing directly in the crossfire of the capex shift. I do not think consulting is finished, and the change contains a genuine opportunity, because consultancies are exactly who enterprises turn to and ask to prove the AI is working. That is the highest-value question in the market right now, and it is not one you can answer by the hour: a quarterly slide deck is a photograph, and the meter runs every second. Proving AI value has to be instrumented, continuous, and measured at the token level, tied to what actually shipped, which is a product problem rather than an engagement problem. The firms that productise that measurement will win an enormous amount of work, and the ones attempting it manually will be compressed by the technology they are advising on. ## What to check this quarter This is directional, as always, and you should check my math against your own invoices rather than take mine. But three things follow, and they are checks rather than recommendations. Can you see what share of your coding spend is input rather than output, and do you know whether caching is switched on across every tool you pay for? Do you route on cost per accepted output, or on cost per request, which is the number your vendor console happens to show you? And can you say what proportion of AI-written code in your repositories was reviewed by a person, which is a question about [your own engineering data](/coding-iq/) rather than about any vendor’s dashboard. Most organisations cannot answer the first, guess at the second, and have never asked the third. That gap is the reason a [measured view of AI ROI](/ai-roi/) stopped being a reporting exercise this year, and it is why the record of what your AI did, kept across every tool and every token, is the thing I would build before the next budget cycle rather than after it. The [capex reckoning](/blog/ai-capex-reckoning/) is already deciding which line items survive. One question worth taking into your next budget review, and you can answer it from what you already have: if AI ate into your budget this year, what did it give back, and can you show it? I’m Paul, co-founder of Olakai. Measuring what AI actually costs and what it actually returns, across every tool and every token, is the work I spend my days on. [Your AI is an investment, so let’s measure it like one](/schedule-a-demo/). [Gartner: Only 28% of AI Projects Deliver ROI. Here’s Why the Rest Don’t.](https://olakai.ai/blog/gartner-ai-roi-28-percent/) [Shadow AI, Caught in the Act: Inside Olakai’s App Catalog and Policy Alerts](https://olakai.ai/blog/shadow-ai-app-catalog-policy-alerts/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Capex Reckoning Source: /blog/ai-capex-reckoning [← Back to Olakai's Blog](/blog/) # AI’s $725B Capex Reckoning: Prove ROI or Get Cut ![Abstract visualization of enterprise AI capital spending measured against return metrics](https://olakai.ai/wp-content/uploads/2026/06/ai-capex-reckoning-featured.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. May 30, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) One company spent $500 million on AI in a single month. Not across a year, not spread over a sprawling transformation program, but in one month, because nobody had set a usage limit on employee licenses. That detail, reported by [Axios](https://www.axios.com/2026/05/28/ai-spending-roi-enterprise-costs) at the end of May, is the kind of figure that used to be a rounding error in a hyperscaler’s budget and is now the thing that ends careers. Axios called the broader phenomenon “AI sticker shock,” and it is moving through corporate America quickly. Microsoft reportedly pared back AI coding licenses partly over cost. Uber’s operating chief said AI expenses were becoming harder to justify. The build-out that boards celebrated as visionary eighteen months ago is now generating invoices that finance teams cannot tie to outcomes, and the market has finally started asking the only question that matters: what did all of this actually produce? ## The scale of the bet The numbers behind the spending are staggering, even by big-tech standards. The four largest hyperscalers are on track for roughly $725 billion in combined capital expenditure in 2026, up about 77% from the prior year, the largest concentrated infrastructure build in the history of the industry. [Meta alone raised its 2026 capex guidance to between $125 billion and $145 billion](https://fortune.com/2026/04/29/meta-zuckerberg-145-billion-ai-spending-roi/) on its first-quarter earnings call, adding tens of billions in new commitments in a single revision. Someone has to pay for that, and increasingly it is the workforce. Tech-sector layoffs passed 142,000 in the first five months of 2026, up roughly a third year over year, with companies openly framing payroll cuts as a way to fund AI infrastructure. The story enterprises told themselves was simple and seductive: spend now on AI, cut headcount, and watch the returns roll in. The first half of that story is unfolding on schedule. The second half is where the reckoning begins. ## Gartner’s verdict: layoffs don’t equal returns In May, [Gartner published a finding](https://www.gartner.com/en/newsroom/press-releases/2026-05-05-gartner-says-autonomous-business-and-artificial-intelligence-layoffs-may-create-budget-room-but-do-not-deliver-returns) that should have stopped the spreadsheet logic cold. Surveying 350 executives at billion-dollar companies, Gartner found that 80% of organizations deploying AI had reduced headcount, yet there was no correlation between those cuts and higher returns. The companies slashing the most jobs were posting nearly identical financial results to the companies cutting the least. As Gartner’s Helen Poitevin put it, workforce reductions may create budget room, but they do not create return. The organizations actually pulling ahead, Gartner found, were the ones using AI to amplify their people rather than replace them. That distinction matters more than it first appears, because amplification is something you have to be able to see and measure. You cannot prove that AI made a team more productive unless you know what that team was doing before, what it is doing now, and what the difference is worth. The losers in this cycle are not the companies that spent too much. They are the companies that spent without instrumenting anything, and now cannot tell whether the spending worked. This is exactly the signal a [vendor-neutral measurement layer](/platform/) was built to capture: the link between AI activity and business outcome, across every tool, in numbers a board will accept. ## The accountability gap nobody instrumented for The measurement gap is not a fringe problem affecting a handful of laggards. It is the median state of the enterprise. A recent RGP survey of 200 finance chiefs found that only 14% have seen clear, measurable impact from their AI investments to date. NVIDIA’s own survey of more than 3,200 leaders found that [30% still cannot quantify AI ROI at all](/blog/nvidia-ai-report-roi-measurement/), even as the vast majority report rising budgets. [McKinsey’s 2026 State of AI work](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) landed in the same place, with more than 80% of respondents saying gen AI has produced no tangible effect on enterprise-level earnings. Read those three findings together and an uncomfortable picture emerges. The problem is not necessarily that AI fails to work. The problem is that almost nobody can say with rigor whether it is working, which means almost nobody can defend a budget when the question finally comes. And the question is coming. This is the same dynamic we mapped in [the enterprise AI revenue gap](/blog/enterprise-ai-roi-gap-2026/): a widening distance between the organizations that built measurement into their AI programs and the ones that treated proof as something to figure out later. “Later” has arrived, and it is sitting in the CFO’s chair holding an invoice. ## What the winners actually measure The companies that will survive the capex reckoning are not the ones with the biggest GPU clusters. They are the ones that can walk into a budget review with evidence. That evidence has a consistent shape: visibility into what AI is genuinely being used for across the organization, business metrics tied to each use case rather than vanity counts of prompts and tokens, and a clear line from spend to outcome that finance can audit. These are precisely [the metrics that matter to financial leadership](/blog/ai-metrics-that-matter/), and they are the difference between a renewal and a cut. This is the work Olakai exists to do. As a vendor-neutral system of record for the entire AI stack, it gives a CFO or a [head of finance the board-ready answer](/use-cases/cfo/) that “we think it’s helping” can never provide: which tools are delivering value, which licenses are sitting idle, where spend is running ahead of return, and what the next dollar of AI budget is actually buying. The same visibility that proves value also [controls cost](/coding-iq/), because the $500 million surprise in the Axios story was not really a pricing problem. It was a visibility problem. Nobody was watching the meter. ## Before your next budget review The capex wave is not slowing down. With three quarters of a trillion dollars flowing into AI infrastructure this year and agentic systems multiplying the number of decisions made without a human in the loop, the volume of spending that needs justification is only growing. The market has shifted from rewarding ambition to demanding proof, and that shift is permanent. Organizations still stuck moving from [pilot to production](/blog/ai-pilot-to-production/) without a measurement foundation are the ones whose budgets get cut first when the board goes looking for savings. The fix is not complicated, but it is urgent. Instrument before you scale. Establish baselines before the next deployment. Treat measurement as a foundational layer of your AI architecture the way you treat security, not as a report you assemble in a panic the week before budget season. The companies that do this will go to their boards with numbers. The ones that do not will go with narratives, and narratives are the first thing cut when the money gets tight. **Will you have an answer when the board asks what your AI is worth?** [Talk to an expert](/schedule-a-demo/) to see how Olakai gives you unified visibility, business-aligned KPIs, and audit-ready ROI evidence across every AI tool in your enterprise. [Shadow AI Statistics 2026: The Governance Crisis Is Already Here](https://olakai.ai/blog/shadow-ai-statistics-2026/) [Inside AI Spend Governance: Budgets and the Alerts That Fire Before You Blow Through Them](https://olakai.ai/blog/inside-ai-spend-governance/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Coding Tool Roi Source: /blog/ai-coding-tool-roi [← Back to Olakai's Blog](/blog/) # Is Your $500K AI Coding Tool Investment Paying Off? What the Data Shows ![Is Your $500K AI Coding Tool Investment Paying Off? What the Data Shows](https://olakai.ai/wp-content/uploads/2026/03/ai-coding-tools-featured.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. March 26, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Most engineering leaders made the same bet in 2024. They licensed GitHub Copilot for the team, added Cursor for the power users, maybe rolled out Claude Code for a few senior engineers. The invoices added up fast. A mid-sized engineering organization with 100 developers can easily spend $400,000 to $600,000 per year across these tools before accounting for the API costs that accumulate quietly in the background. The bet seemed obvious. The tools were impressive in demos. Every vendor had benchmarks showing dramatic productivity gains. And the competitive pressure to “enable developers with AI” made saying no feel reckless. So the tools went in, the credit cards got charged, and the organization moved on to the next priority. Twelve months later, most of those organizations still cannot answer the most basic question their CFO will eventually ask: is this working? ## The Benchmark Problem The AI coding tool vendors are not shy about publishing productivity statistics. GitHub claims Copilot users are 55% faster at coding tasks. Cursor publishes testimonials from engineers who describe 10x output improvements. Anthropic’s data on Claude Code shows meaningful reductions in time-to-completion for well-defined tasks. These numbers are real, in the sense that they come from controlled evaluations of specific tasks. But controlled evaluations are not engineering organizations. The gap between “this tool helped a developer complete an isolated coding challenge faster” and “this tool made our entire engineering organization more effective” is where most ROI analysis breaks down. The industry research is more sobering. [Jellyfish](https://jellyfish.co/resources/engineering-benchmark-report/), which analyzes data from over 500 engineering organizations, puts the average cycle time improvement from AI coding tools at around 25%, with PR throughput gains of roughly 12%. Those are meaningful numbers for a well-run rollout. But Jellyfish also tracks adoption rates, and the data shows that AI-assisted PRs account for roughly half of all merged pull requests across their customer base, up from 14% just two years ago — which means roughly half of your developers’ output still has no AI involvement at all, despite the licenses sitting idle in the admin console. McKinsey’s [research on AI-enabled software engineering](https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/supercharging-software-development-with-gen-ai) found that productivity gains are highly uneven across teams and functions, and that organizations with structured measurement programs capture three to four times more value from AI tools than those without. The tools don’t create value uniformly. Whether your organization captures that value depends almost entirely on whether anyone is paying attention to the data. ## What “Paying Off” Actually Means There is a version of this analysis that stops at cycle time. If your AI-assisted pull requests close 25% faster than non-AI PRs, and you can assign a dollar value to engineering time, you can construct a spreadsheet that shows a positive return. Many organizations do exactly this and call it done. That math is not wrong, but it is incomplete in ways that matter. Three dimensions of ROI tend to get ignored. **Adoption is not uniform.** Aggregate adoption rates hide the distribution underneath. In most engineering organizations, AI coding tool adoption follows a familiar pattern: a small cohort of power users who have integrated AI deeply into their workflow, a larger group of casual users who pull the tool out occasionally, a segment who have never meaningfully engaged, and new adopters still learning. These cohorts have entirely different productivity profiles. A 50% adoption rate that is all casual usage delivers a fraction of the value compared to a 50% rate built on genuine depth. The aggregate metric obscures everything interesting. **Tool spending is not consolidated.** The average engineering organization is paying for multiple AI coding tools simultaneously. The same developers who have GitHub Copilot licensed are also using Cursor, and some have Claude Code running in their IDE. The vendor consoles report usage for their own tool only. No single view shows you cost per PR across all providers, which tool is delivering the best return per dollar, or where licenses are sitting unused. Without that cross-vendor view, optimization is impossible. **Not all PRs are equal.** AI coding tools deliver more value on some work than others. Boilerplate generation, documentation, test writing, and well-scoped feature additions tend to see strong AI contribution. Architecture decisions, complex debugging, and novel problem-solving tend to see less. If your metric is simply “AI code ratio” — the percentage of merged lines that originated from an AI tool — you may be measuring the wrong thing, or at least measuring it in a way that tells you nothing about whether the AI contribution was on the work that matters most. ## What Measurement Actually Requires Getting a real answer to the ROI question requires connecting three data sources that almost no organization has unified. The first is GitHub data: PR volume, cycle time, AI commit detection, code contribution patterns by developer and team. This is where the before-and-after comparison lives. AI-assisted PRs versus non-AI PRs, by team, by developer cohort, by time period. Without this, you are estimating. The second is provider cost data: per-user spend, token consumption, acceptance rates, and usage patterns by tool. This requires pulling from the admin APIs of each vendor — Anthropic, GitHub, Cursor, Windsurf, OpenAI — and normalizing the data into a single cost view. The math is not complicated, but the data integration work is non-trivial, and almost no engineering organization has done it. The third is the developer adoption dimension: who is in which cohort, which teams are getting deep value versus surface-level usage, and where the gaps are. This is where the improvement roadmap lives. If your power user cohort is 8% of your developers and your casual cohort is 42%, you have a very different problem than if those numbers are reversed. When these three data sources are unified, the analysis becomes tractable. Cost per PR by provider. Cycle time delta for AI-assisted versus non-AI work. Developer cohort distribution by team. Which providers are getting the most usage per dollar. Where idle licenses should be reassigned. These are the questions the CFO is eventually going to ask. The organizations that can answer them will have a very different conversation than those who cannot. ## How Coding IQ Approaches This This is the problem [Olakai’s Coding IQ](https://olakai.ai/platform/) was built to solve. Rather than requiring engineering teams to build custom data pipelines or rely on fragmented vendor consoles, Coding IQ connects directly to your GitHub organization and your AI coding tool admin APIs — Anthropic, GitHub Copilot, Cursor, Windsurf, OpenAI — and pulls the data together automatically. The result is a unified view: cycle time comparison between AI-assisted and non-AI PRs, provider cost breakdown, developer adoption cohorts (Power, Casual, New, Idle), team-level benchmarks, and a cost-per-PR metric by provider. Questions that previously required a data engineering project — “which coding tool gives us the best ROI?”, “which teams have the lowest AI adoption?”, “what is our AI code ratio trending toward?” — become answerable in seconds. Coding IQ also surfaces what the vendor dashboards cannot. Shadow AI in engineering is real: developers using personal API keys, unauthorized tools, or AI assistants outside sanctioned tools. A developer who builds on Claude’s API with a personal account doesn’t show up in your GitHub Copilot analytics. Coding IQ detects AI contribution patterns from the code itself — not just from vendor data — so the picture is complete rather than bounded by what each vendor chooses to report. For organizations already using a dedicated engineering intelligence platform, the question worth asking is whether that platform can show you governance, shadow AI exposure, and the full cross-vendor cost picture alongside your engineering metrics. For most, the answer is no. Coding IQ was built to provide that layer. ## The Question Worth Asking Now Engineering organizations are entering a moment where AI coding tool budgets are large enough to require accountability. The days of “it feels productive” as sufficient justification are ending. CFOs are starting to ask for the data. Boards are asking whether AI investments across the organization are generating returns. The organizations that will be able to answer those questions are the ones that started measuring before the question was forced on them. Not because the tools are failing — many of them are genuinely delivering value — but because value without measurement is invisible. And invisible value does not survive budget season. If you are spending $400,000 per year on AI coding tools and cannot answer what your cost per PR is, which teams are in which adoption cohort, or whether your investment would be better concentrated in one tool over another, the issue is not the tools. The issue is measurement. You have the data. You are probably just not looking at it yet. **[Talk to an expert](https://olakai.ai/schedule-a-demo/)** to see how Coding IQ gives engineering leaders the full picture on AI coding tool ROI. [The 76% Problem: Shadow AI Is Getting Worse, Not Better](https://olakai.ai/blog/shadow-ai-76-percent-problem/) [5 Tools Enterprises Actually Use to Measure AI ROI — And What None of Them Get Right](https://olakai.ai/blog/ai-roi-measurement-tools/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Coding Tool Sprawl Source: /blog/ai-coding-tool-sprawl [← Back to Olakai's Blog](/blog/) # Your Engineering Team Uses 3+ AI Coding Tools. What You’re Missing. ![Abstract visualization of fragmented AI coding tool networks with no unified connection between them](https://olakai.ai/wp-content/uploads/2026/07/ai-coding-tool-sprawl.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. July 8, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) In May 2026, Microsoft’s Experiences + Devices division quietly pulled Claude Code licenses from its engineers. The reason wasn’t performance. Token billing had reportedly climbed to roughly $2,000 per engineer per month, and nobody inside the division had seen it coming until the invoice did. Weeks earlier, Uber had burned through its entire 2026 AI coding budget in four months flat, after adoption across its 5,000-engineer org surged from 32% to 84% almost overnight, with its heaviest users individually costing the company $2,000 a month. Both stories made headlines for the same reason: the bill was a surprise. Neither company lacked data from its AI coding vendors. Each had a perfectly good dashboard — for one tool. That’s the part that should worry every VP of Engineering reading this. Uber and Microsoft aren’t outliers because they use AI coding tools aggressively. They’re outliers because their overruns became public. Jellyfish’s 2026 AI Engineering Trends report, which analyzed more than 20 million pull requests across 700+ companies and 200,000+ engineers, found that [Claude Code, Gemini Code Assist, and GitHub Copilot now cluster within nine points of each other at the top of enterprise adoption, with twelve more tools trailing close behind](https://jellyfish.co/ai-engineering-trends/). A year earlier, Copilot alone held a commanding 42% share. That era is over. The modern engineering org doesn’t pick a coding assistant. It accumulates several, one team at a time, until nobody in leadership can name all the tools running against the company’s codebase — let alone say what each one costs, who’s actually using it, or whether it’s paying for itself. ## Sprawl is the default, not the exception It’s tempting to treat “which AI coding tool should we standardize on” as the strategic question. It isn’t, anymore. Claude Code lands with one team because a senior engineer swears by its planning mode. Cursor spreads through another because it’s the fastest way to onboard a new hire onto an unfamiliar repo. GitHub Copilot ships by default because it’s bundled into the existing GitHub Enterprise contract. Codex creeps in through a few engineers experimenting on side projects. Gemini Code Assist arrives bundled with a Google Workspace renewal nobody scrutinized closely. None of these adoptions individually looks like a decision worth escalating. Collectively, they add up to an organization running four or five AI coding vendors with zero shared measurement layer between them — which is precisely the fragmentation problem we’ve written about at the platform level in [what agentic AI actually means for the enterprise](/blog/what-is-agentic-ai/), and precisely the gap Olakai was built to close. We’ve written before about the harder problem of proving that any single AI coding tool is generating value rather than just generating code, and about why acceptance rate is the wrong metric to chase when you’re evaluating one vendor in isolation — see [Your AI Coding Tools Are Generating Code. Are They Generating Value?](/blog/ai-coding-tool-roi/) and [AI Coding Tool ROI: Why Acceptance Rate Is the Wrong Metric](/blog/ai-coding-tool-roi-metrics/). Those posts assumed a single tool as the unit of analysis. This one doesn’t. The question enterprises are actually facing in mid-2026 isn’t “is Copilot worth it” — it’s “we run five of these, and I have five different answers to that question, none of which use the same metric, currency, or time window.” That’s a portfolio problem, and native vendor dashboards were never built to solve it. Copilot’s dashboard sees Copilot. Cursor’s admin panel sees Cursor. Neither will ever tell you which tool your best engineers are quietly switching away from, or which team is paying triple the per-seat cost of another team doing comparable work. ## Nobody is actually measuring this — even one tool at a time Before an organization can worry about comparing five AI coding tools, it has to be tracking metrics on any of them, and most aren’t. Jellyfish’s same report found that only 46% of organizations are actively tracking AI-specific metrics at all — adoption, acceptance rate, model usage, anything. That’s not 46% tracking consistently across every vendor in use. That’s 46% tracking anything, from any vendor, in any form. The other 54% are running a multi-vendor AI coding program on instinct: a sense that “the team seems to like Cursor” or “we haven’t heard complaints about Copilot,” with no underlying data to confirm or contradict it. Layer cost onto that visibility gap and the picture gets worse. Research from DX covering more than 400 organizations found blended per-developer spend across tiers and tools now running $200 to $600 a month, with agentic token consumption alone sometimes reaching [$200 to $2,000-plus per engineer per month](https://getdx.com/blog/ai-coding-assistant-pricing/) depending on usage intensity — the same range that blindsided Microsoft’s E+D division. And forecasting that spend is failing broadly, not just at the companies that make the news: a Mavvrik/Benchmarkit survey of 372 enterprises found only 15% forecast AI costs within 10% of actual, while nearly one in four miss by more than 50%. Multiply that forecasting failure across four or five tools running in parallel, each billed differently, each reported through a different console, and “surprise” stops being a risk and starts being the expected outcome. ## What a unified view actually requires Solving this isn’t a matter of asking engineering managers to check five dashboards instead of one and mentally reconcile the numbers. It requires a measurement layer that sits above every vendor and normalizes what each one reports into a single, comparable view. That’s the specific gap [Olakai Agentic](/coding-iq/) is built to close: a vendor-neutral analytics and governance layer across Claude Code, Cursor, GitHub Copilot, Codex, Gemini Code Assist, and whatever the team adopts next, without requiring the organization to standardize on one vendor first. Concretely, that means three things a single-vendor dashboard structurally cannot give you. First, cost-per-PR comparison across tools on common ground — not Cursor’s definition of a productive session next to Copilot’s definition of an accepted suggestion, but one measurement standard applied consistently, so a VP of Engineering can see that Team A’s tool costs three times what Team B’s does for comparable throughput and ask why. Second, adoption cohorts that span vendors, showing who’s actually using what — the power users worth studying, the licenses sitting idle regardless of which tool issued them, and the teams quietly switching tools without anyone approving the shift. Third, budget forecasting that aggregates spend across every provider into one number the CFO can trust, with alerts before a team’s token usage on any single tool turns into the kind of invoice that ends a pilot. This is the same measurement-layer thinking behind our [Analytics & Custom KPIs](/analytics-kpis/) capability, applied specifically to the vendor sprawl that now defines every engineering org’s AI coding stack. For a VP of Engineering, the practical shift is to stop evaluating AI coding tools one procurement cycle at a time and start treating the portfolio itself as the thing to manage. That means asking which teams are using which tools before the next contract renewal, not after a token bill forces the conversation; it means comparing cost-per-outcome across vendors on the same axis instead of trusting each tool’s self-reported acceptance rate; and it means building budget alerts before adoption surges the way Uber’s did, not after. Olakai Agentic is purpose-built for exactly that workflow, and it’s the specific reason we built a dedicated page for engineering leadership — see [Olakai for VPs of Engineering](/use-cases/vp-engineering/) for how the cross-tool view maps to the decisions this role actually has to make. None of this requires an organization to consolidate down to one AI coding tool, and for most engineering teams that wouldn’t even be the right call — different tools genuinely suit different workflows, and forcing a single vendor sacrifices real productivity gains for the sake of simpler reporting. The fix isn’t fewer tools. It’s a unified measurement layer across every coding tool the organization already runs, so the next $2,000-a-month surprise shows up on a dashboard weeks before it shows up on an invoice. If your engineering org is running three, four, or five AI coding tools right now with no shared view across them, that’s not a future governance project — it’s the state of your AI spend today, and it’s already accumulating risk you can’t see. [Talk to an Expert](/schedule-a-demo/) to see how Olakai Agentic brings every AI coding vendor into one measurement and governance layer. [Companies Are Cutting Jobs to Pay for AI. Can They Prove It’s Working?](https://olakai.ai/blog/companies-cutting-jobs-to-pay-for-ai/) [The CFO Just Walked Into the Coding Room](https://olakai.ai/blog/cursor-cfo-council/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Coding Tools Generating Value Source: /blog/ai-coding-tools-generating-value [← Back to Olakai's Blog](/blog/) # Your AI Coding Tools Are Generating Code. Are They Generating Value? ![Your AI Coding Tools Are Generating Code. Are They Generating Value?](https://olakai.ai/wp-content/uploads/2026/03/featured-post-1711.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. March 19, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Your engineering team just shipped 10,000 lines of code this sprint. Nearly half of it was written by AI. Do you know which half — and whether it was any good? This isn’t a theoretical question anymore. According to [the 2025 DORA Report](https://dora.dev/research/2025/dora-report/), almost half of companies now have at least 50% AI-generated code, up from just 20% at the start of 2025. Ninety percent of engineering teams now use AI coding tools in their workflows. Cursor crossed $2 billion in annualized revenue by February 2026. Claude Code hit $2.5 billion. GitHub Copilot remains embedded in enterprises worldwide. The adoption question is settled. The measurement question is not. ## The Measurement Gap Nobody Talks About Here’s what most engineering leaders are tracking: lines of code generated, completion acceptance rates, developer satisfaction surveys, and seat utilization. These are vanity metrics. They tell you that developers are using the tools. They don’t tell you whether the tools are making your organization better. BCG found that [60% of companies have no defined financial KPIs for their AI initiatives](https://blog.exceeds.ai/measure-ai-developer-productivity-roi/) — they’re counting pilots, celebrating deployments, and measuring model accuracy instead of actual business value. Bain’s 2025 Technology Report went further, finding that AI coding tools deliver only 10 to 15 percent productivity gains despite adoption by two-thirds of software firms. That’s a fraction of the 10x improvement vendors promised. The gap between what companies measure and what actually matters is where millions disappear. Your board isn’t asking how many code completions your team accepted last quarter. They’re asking whether your $1.2 million in AI coding tool licenses is making your engineering organization faster, safer, and more competitive. If you can’t answer that question with data, you have a measurement problem — not a productivity problem. ## What You Should Be Measuring Instead The metrics that matter for AI coding tools aren’t about the tools themselves. They’re about what happens after the code ships. **Cycle time delta.** How much faster do AI-assisted pull requests move from first commit to production compared to non-AI pull requests? This is the clearest signal of real productivity gain. Early data suggests AI-assisted PRs are 25 to 40 percent faster through the pipeline, but this varies wildly by team, codebase complexity, and tool. If you aren’t measuring the delta, you’re guessing. **Incident rate on AI-authored code.** A [Stanford study cited by CIO.com](https://www.cio.com/article/4122916/how-enterprise-cios-can-scale-ai-coding-without-losing-control.html) found that participants using coding assistants wrote less secure code in 80% of tasks — yet were 3.5 times more likely to believe their code was secure. That confidence gap is dangerous. If your AI-generated code is creating more production incidents, more security vulnerabilities, or more hotfixes, the productivity gains are illusory. You need to track post-deployment quality by code origin. **Cost per pull request by provider.** Your team is probably using three or four AI coding tools simultaneously — Copilot on some repos, Cursor on others, Claude Code for complex refactors. Each has different pricing, different token consumption patterns, and different value profiles. Without a unified cost-per-PR metric across providers, you can’t make rational decisions about which tools to standardize and which licenses are going unused. **Deployment frequency.** The DORA framework remains the gold standard for engineering performance, but AI introduces a wrinkle. Deployment frequency may rise slightly while lead times increase as review cycles grow longer to accommodate AI-generated code. Measuring deployment frequency in isolation misses this dynamic. You need to track it alongside review time and change failure rate to see the full picture. ## The Shadow Coding Problem There’s another dimension most CTOs haven’t confronted: developers using personal accounts for AI coding tools that your organization doesn’t manage, monitor, or govern. A developer signs up for Cursor with a personal email. Another uses Claude Code through a personal API key. A third is running a locally hosted model for code generation. None of these show up in your IT asset inventory. None are covered by your data handling policies. And all of them are processing your proprietary source code through systems you don’t control. This is shadow AI in the codebase — and it’s arguably more dangerous than [shadow AI in other parts of the organization](/blog/shadow-ai-enterprise-risk/) because the outputs become permanent parts of your software. Code generated through ungoverned tools gets committed, reviewed, merged, and deployed. It becomes your product. If that code was generated using a model that trained on GPL-licensed code, or if proprietary algorithms were sent to a third-party API without appropriate data handling agreements, the liability sits with your organization — not the developer. According to HiddenLayer’s 2026 AI Threat Landscape Report, 76% of organizations now cite shadow AI as a definite or probable problem, a 15-point jump from the prior year. For engineering organizations, the stakes are uniquely high because the shadow doesn’t just create risk — it becomes part of the product. ## The Adoption Cohort Blindspot Aggregate metrics hide critical patterns. When engineering leaders report that “our team has 70% AI adoption,” they’re averaging over a distribution that looks nothing like a uniform curve. In practice, adoption breaks into cohorts. Power users — developers with more than 70% of their pull requests AI-assisted — are producing dramatically different work than casual users at 20 to 40 percent. New adopters who started using AI tools within the past two weeks have different needs than idle users who tried a tool once and stopped. Each cohort requires different support, different training, and different expectations. Without cohort-level visibility, you can’t identify which developers are getting genuine value, which ones need enablement, and which expensive licenses are sitting unused. You also can’t detect the productivity paradox that multiple studies have now documented: developers predict a 24% speedup from AI tools but some studies have measured a 19% slowdown, while those same developers still report a 20% perceived improvement afterward. The gap between perception and measurement is real, and only cohort-level data can surface it. ## What the Competitors Miss Engineering analytics platforms like Jellyfish have built impressive capabilities for measuring developer productivity. They can track DORA metrics, analyze PR throughput, and benchmark teams against each other. But they were built before AI coding became the default mode of software development, and their architecture reflects that. Most engineering analytics tools work from metadata — commit timestamps, PR merge events, Jira ticket transitions. They can tell you that a developer merged 12 PRs this week. They can’t tell you which of those PRs were AI-assisted, what tool was used, how much it cost, or whether the AI-generated portions introduced quality issues. Without code-level detection that identifies AI co-author trailers, bot PR authors, and tool-specific markers, the attribution problem remains unsolvable. Then there’s the governance dimension. Your CISO needs to know which AI tools are processing your source code and whether they comply with your data handling policies. Your CFO needs to know the total cost across all AI coding providers, not just the ones IT provisioned. Your compliance team needs an audit trail showing what code was AI-generated and by which model. Productivity analytics tools don’t cover any of this. The measurement gap isn’t just about better dashboards. It’s about connecting [AI ROI measurement](/blog/ai-roi-framework/) with governance, cost control, and security in a single view — the same way organizations learned to manage cloud infrastructure by combining performance monitoring with cost optimization and compliance controls. ## Building the Framework If you’re spending six or seven figures on AI coding tools and can’t answer basic questions about their impact, here’s where to start. First, establish a baseline. Before you can measure improvement, you need to know where you stand. What percentage of your pull requests are AI-assisted? What’s your current cycle time for AI-assisted versus non-AI code? What are you spending per developer, per provider, per month? Most engineering organizations can’t answer these questions today. Second, segment by cohort. Stop reporting a single adoption number. Break your engineering organization into power users, casual users, new adopters, and idle license holders. Each cohort tells a different story, and each requires a different response. Third, connect quality to origin. Track incident rates, security findings, and change failure rates by whether the code was AI-assisted or not. This is the data your board actually needs — not how many lines the AI generated, but whether those lines made your product better or worse. Fourth, unify cost visibility. Aggregate spending across Copilot, Cursor, Claude Code, and every other tool your developers are using — including the ones they’re paying for themselves. The [enterprise AI revenue gap](/blog/enterprise-ai-roi-gap-2026/) starts with cost sprawl that nobody can see. The organizations that will win the AI coding race aren’t the ones that adopt the most tools. They’re the ones that measure the right things, govern the risks, and make data-driven decisions about where to invest. Your AI coding tools are generating code. The question is whether they’re generating value. *Want to see how your engineering AI investment is actually performing? [Talk to an expert](/schedule-a-demo/) to see Coding IQ in action — vendor-neutral analytics across every AI coding tool your team uses.* [NVIDIA Surveyed 3,200 Leaders. 30% Still Can’t Measure AI ROI.](https://olakai.ai/blog/nvidia-ai-report-roi-measurement/) [The 76% Problem: Shadow AI Is Getting Worse, Not Better](https://olakai.ai/blog/shadow-ai-76-percent-problem/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Cybersecurity Agents Source: /blog/ai-cybersecurity-agents [← Back to Olakai's Blog](/blog/) # How AI Agents Are Revolutionizing Cybersecurity ![AI-powered cybersecurity defense network protecting enterprise systems](https://olakai.ai/wp-content/uploads/2025/12/ai-cybersecurity-agents-featured-1.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. December 23, 2025 · [AI Governance](https://olakai.ai/blog/category/ai-governance/) In December 2023, a mid-sized financial services firm detected unusual network activity at 2:47 AM. Their traditional SIEM flagged it as a medium-priority alert—one of 847 alerts generated that night. By the time a human analyst reviewed it eight hours later, the attackers had already exfiltrated customer records and established persistent backdoors across a dozen servers. This scenario plays out daily across enterprises worldwide. Security operations centers are drowning in alerts, understaffed and overwhelmed, while adversaries move faster than humans can respond. According to the [2025 SANS Detection and Response Survey](https://www.stamus-networks.com/blog/what-the-2025-sans-detection-response-survey-reveals-false-positives-alert-fatigue-are-worsening), alert fatigue has escalated to crisis levels, with 64% of respondents citing high false positive rates as their primary challenge. Industry data shows that 25-30% of security alerts go completely uninvestigated due to overwhelming volume. AI agents offer a different path: intelligent systems that can triage alerts, investigate threats, and respond to incidents at machine speed—transforming security operations from reactive firefighting to proactive defense. ## The Cybersecurity Challenge Security operations centers face a perfect storm of challenges that traditional approaches cannot solve. The scale of the problem is staggering: an average enterprise SOC processes over 11,000 alerts daily, with organizations over 20,000 employees seeing more than 3,000 critical alerts that demand attention. Studies indicate that false positive rates in enterprise SOCs frequently exceed 50%, with some organizations reporting rates as high as 80%. A [Trend Micro survey](https://www.trendmicro.com/vinfo/us/security/research-and-analysis/predictions/the-ai-fication-of-cyberthreats-trend-micro-security-predictions-for-2026) found that 51% of SOC teams feel overwhelmed by this alert volume, with analysts spending over a quarter of their time handling false positives. The talent situation makes matters worse. The 2025 SANS survey reveals that 70% of SOC analysts with five years or less experience leave within three years—burned out by the relentless pace and thankless work of triaging endless alerts. Meanwhile, organizations operating 24/7 experience peak alert fatigue during shift transitions, when context is lost between teams and attackers know defenders are at their weakest. Modern organizations deploy an average of 28 security monitoring tools, each generating its own alert stream. This tool proliferation, while intended to improve security coverage, creates an overwhelming flood of notifications that no human team can effectively process. The Osterman Research Report reveals that almost 90% of SOCs are overwhelmed by backlogs and false positives, while 80% of analysts report feeling consistently behind in their work. The result: analysts burn out, real threats get missed, and mean time to respond stretches dangerously long. According to [IBM’s 2025 Cost of a Data Breach Report](https://www.ibm.com/reports/data-breach), the average cost of a breach now exceeds $4.88 million globally—and a major factor in that figure is the length of time it takes to contain an incident. Attackers aren’t hacking in anymore; they’re logging in, exploiting valid credentials and trusted systems to move undetected across networks. This is compounded by the rise of [shadow AI](/blog/shadow-ai-risk/), where unsanctioned AI tools create additional attack vectors that security teams can’t monitor. ## Where AI Agents Fit AI agents are particularly well-suited to cybersecurity because they address the fundamental mismatch between threat velocity and human response capacity. For a broader understanding of how autonomous AI systems work, see our guide to [what makes AI truly agentic](/blog/what-is-agentic-ai/). ### Triage at Scale An AI agent can review thousands of alerts in seconds, correlating related events across multiple data sources and prioritizing the small percentage that warrant human attention. This transforms the analyst role from “review everything” to “investigate the high-priority cases.” The industry is already seeing agent-style co-workers inside security operations platforms that can assemble context, draft response actions, and even simulate likely attacker next moves. Organizations report that this approach reduces the number of alerts requiring human review by 60-80%. ### Autonomous Investigation When an alert fires, an agent can automatically gather context: user behavior history, related network traffic, file reputation, and threat intelligence feeds. It presents analysts with a complete picture rather than a single data point. IBM found that companies heavily using security AI and automation identified and contained breaches 108 days faster than those without such tools. For high-severity incidents, that’s the difference between a contained incident and a catastrophic breach. ### Rapid Response For well-understood threats, agents can execute response playbooks autonomously: isolate a compromised endpoint, block a malicious IP, disable a compromised account. The agent acts in seconds while a human would take minutes or hours. Organizations with comprehensive playbook coverage show a 32% reduction in mean time to remediation. Financial services teams often aim for under two hours on high-severity incidents, and AI-driven automation makes that target achievable. ### Continuous Learning As analysts confirm or dismiss alerts, agents learn which patterns matter. False positive rates drop over time. Novel threats that slip through can be incorporated into detection logic. This creates a virtuous cycle where the system gets more accurate the more it’s used, unlike traditional rule-based systems that require constant manual tuning. ## Key Use Cases ### Incident Response Automation When a security alert fires, an AI agent can gather relevant logs and context, correlate with threat intelligence, assess severity and potential impact, execute initial containment steps, and escalate to human analysts with full context—all within seconds of detection. Organizations report 40-60% reduction in mean time to respond and significant improvement in analyst productivity. Government agencies will increasingly adopt agentic AI for threat detection and response, moving beyond traditional SIEM and SOAR platforms. ### Threat Hunting AI agents can proactively search for signs of compromise rather than waiting for alerts to fire. They analyze logs for suspicious patterns, identify anomalous user or system behavior, correlate indicators across multiple data sources, and surface potential threats before traditional detection systems catch them. This proactive approach catches sophisticated attackers who specifically design their techniques to avoid triggering standard alerts. ### Vulnerability Management With enterprises struggling to manage machine identities that now outnumber human employees by an astounding 82 to 1, agents can help prioritize vulnerability remediation by assessing severity in business context, identifying which vulnerabilities are actively exploited in the wild, recommending patching priorities based on actual risk, and tracking remediation progress across the organization. By embedding AI into IT asset management, enterprises can detect and isolate rogue or untracked devices before they become attack vectors. ## Governance Considerations Security AI requires especially careful governance—the stakes are simply higher than in other domains. For CISOs developing governance programs, our [AI Governance Checklist](/blog/ciso-governance-checklist/) provides a comprehensive framework. ### Higher Stakes An AI agent with security privileges can do significant damage if compromised or misconfigured. Kill switches, granular access controls, and [comprehensive logging](/complete-ai-monitoring/) are essential. Every automated action should be auditable, and high-impact actions should require explicit authorization. The ability to rapidly revoke agent permissions and roll back automated changes must be built in from day one. ### Adversarial Attacks Attackers will specifically target AI systems through adversarial inputs, prompt injection, or model poisoning. The 2026 defining challenge for cybersecurity will be learning to defend against intelligent, adaptive, and autonomous threats. From agentic AI to shape-shifting malware, the same technologies that accelerate defense will further expand the cybercriminal’s toolkit. Security testing must include AI-specific attack vectors, and security teams need to understand how their AI systems could be manipulated. ### Explainability Matters When an agent takes action—blocking an IP, isolating an endpoint, disabling an account—analysts need to understand why. Black-box decisions erode trust and complicate incident review. The best security AI systems provide clear reasoning chains that auditors and analysts can follow, even under pressure during an active incident. ### Human Oversight For high-impact actions—blocking executive access, shutting down production systems, initiating incident response procedures—human approval should remain in the loop. Agents can recommend and prepare, but humans should authorize. This isn’t a limitation; it’s a feature that prevents automated systems from causing more damage than the threats they’re trying to stop. ## The Human + AI Partnership The goal isn’t to replace security analysts—it’s to make them dramatically more effective. Analysts focus on complex investigations, strategic threat hunting, and security architecture decisions. Agents handle triage, routine investigation, and initial response. Together they respond faster and catch more threats than either could alone. The best security teams are already working this way: humans set strategy and handle judgment calls; AI handles scale and speed. Enterprises deploying a massive wave of AI agents in 2026 will finally have the force multiplier security teams have desperately needed. For SOCs, this means triaging alerts to end alert fatigue and autonomously blocking threats in seconds rather than hours. ## Getting Started If you’re considering AI for security operations, start with triage. Alert prioritization is low-risk and high-impact—let AI help analysts focus on what matters rather than drowning in false positives. Build containment playbooks next. Identify routine responses that can be automated and start with low-impact actions like logging and alerting before moving to high-impact ones like blocking and isolating. The IBM Security Incident Response Index showed that most organizations lack predefined workflows for high-impact incidents, delaying containment and increasing operational downtime. Invest in explainability from the beginning. Ensure analysts can understand AI decisions and trace the reasoning behind automated actions. This builds trust and supports incident review when things go wrong—and eventually they will. Finally, test adversarially. Include AI-specific attacks in your security testing. Assume attackers will try to manipulate your AI and design your defenses accordingly. The threats aren’t slowing down—ransomware attacks on critical industries grew by 34% year-over-year in 2025. AI agents give security teams the scale and speed to keep up. ## The Future of Security Operations Security operations is evolving from human-driven with AI assistance to AI-driven with human oversight. The fastest improvements will appear in extended detection and response suites, security operations automation, email and collaboration security, and identity threat detection. The [Future of Agentic use case library](https://futureofagentic.com/use-cases/) includes several detailed security automation scenarios with architecture diagrams and implementation guidance. The organizations that master this transition will operate more securely, respond faster, and make better use of scarce security talent. At least 55% of companies now use some form of AI-driven cybersecurity solution, and that number will only grow as the threat landscape accelerates. *Ready to explore AI for security operations? [Talk to an expert](/schedule-a-demo/) to see how Olakai helps you measure and govern AI across your security stack.* [AI in Finance: 5 Use Cases Every CFO Should Know](https://olakai.ai/blog/cfo-ai-use-cases/) [AI Predictions for 2026: What Enterprise Leaders Need to Know](https://olakai.ai/blog/ai-predictions-2026/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Experimentation Impact Source: /blog/ai-experimentation-impact [← Back to Olakai's Blog](/blog/) # From AI Experimentation to Business Impact ![Team collaborating on AI initiatives in modern office](https://olakai.ai/wp-content/uploads/2025/11/ai-experimentation-impact-photo.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. November 21, 2025 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) In 2024, a global manufacturing company ran 23 AI pilots across its business units. The pilots worked. Chatbots answered questions. Document processors extracted data. Forecasting models outperformed spreadsheets. Leadership declared success and… nothing changed. A year later, exactly zero of those pilots had reached production. The company had proven AI could work; they hadn’t proven it could deliver value at scale. This story repeats across enterprises worldwide. According to [research from MIT](https://argano.com/insights/articles/overcoming-the-ai-pilot-trap.html), 95% of AI pilots fail to deliver measurable business value—most never scale beyond the experimental phase. In 2025, the average enterprise scrapped 46% of AI pilots before they ever reached production. Global investment in generative AI solutions more than tripled to roughly $37 billion in 2025, yet 74% of companies still struggle to scale their AI initiatives into real business impact. Why do some organizations break through while others remain trapped in what we call “pilot purgatory”? The answer isn’t technology—it’s how organizations approach the transition from experiment to production. ## The Pilot Trap Most enterprises approach AI the same way. They identify an interesting use case, assemble a team, run a pilot, declare success, and then stall. The pilot proved the technology works, but scaling requires investment, change management, and governance that organizations aren’t prepared to provide. The result is a graveyard of successful experiments that never delivered business value. The symptoms are unmistakable. Organizations have multiple proof-of-concepts but zero production deployments. Data science teams are enthusiastic while business stakeholders remain skeptical. There’s a “we did AI” checkbox without measurable outcomes to show for it. Security and compliance concerns block production deployment. No one owns the responsibility for scaling successful pilots into real operations. The [ISG State of Enterprise AI Adoption Report 2025](https://isg-one.com/state-of-enterprise-ai-adoption-report-2025) quantifies this problem: only about one in four AI initiatives actually deliver their expected ROI, and fewer than 20% have been fully scaled across the enterprise. In a survey of 120,000+ enterprise respondents, only 8.6% of companies report having AI agents deployed in production, while 63.7% report no formalized AI initiative at all. The gap between AI adoption and AI value remains stubbornly wide. ## What Successful Organizations Do Differently ### 1\. Start with Business Problems, Not Technology Failed AI initiatives typically start with “We should use AI for something.” Successful ones start with “This business problem costs us $X million annually—can AI help?” The difference matters enormously. Business problems come with budgets and executive sponsors who have a stake in the outcome. Clear problems have measurable success criteria that everyone can agree on. Stakeholders are invested in solutions rather than experiments. When a pilot solves a quantified problem, the case for scaling writes itself. Before launching any AI initiative, quantify the business problem. If you can’t put a dollar figure on it, you probably don’t have the executive sponsorship needed to scale. The successful implementations follow what researchers call a counterintuitive split: 10% on algorithms, 20% on infrastructure, 70% on people and process. That last 70% requires business ownership, not just technical enthusiasm. ### 2\. Build Governance from Day One Pilots often skip governance because “we’ll figure it out later.” But when “later” arrives, the lack of logging, security controls, and compliance documentation blocks production deployment. Security teams rightfully refuse to approve systems they can’t audit. Compliance finds gaps that require redesign. What should have been a straightforward scale becomes a rebuild. Organizations that scale AI treat governance as a feature, not an afterthought. Security and compliance stakeholders are involved from the start. [Logging and monitoring](/platform/) are built into the MVP, not bolted on later. Data handling practices are documented before production. Risk assessments happen during design, not after deployment. For a comprehensive framework on what governance should include, our [CISO AI Governance Checklist](/blog/ciso-governance-checklist/) provides the full requirements. The key insight: governance built early accelerates production; governance added late delays or blocks it entirely. ### 3\. Measure Outcomes, Not Activity “The chatbot handled 10,000 conversations” sounds impressive—but did it reduce support costs? Improve customer satisfaction? Drive revenue? Activity metrics are easy to collect but often misleading. Outcome metrics are harder to define but actually prove value. Activity metrics track what the AI does: chatbot conversations, AI completions, agent tasks, documents processed. Outcome metrics track what the business gains: cost savings, time saved, revenue impact, error reduction, customer satisfaction changes. The difference between “we processed 50,000 invoices” and “we reduced invoice processing costs by 60%” is the difference between a pilot that stalls and one that scales. Define outcome metrics before the pilot begins. Establish baselines so you can prove improvement. Our [AI ROI measurement framework](/blog/ai-roi-framework/) provides a structured approach to connecting AI activity to business outcomes. ### 4\. Plan for Change Management AI that changes workflows requires people to change behavior. Without change management, even great technology fails. Employees resist tools they don’t understand. Workarounds emerge that bypass the AI entirely. Training gaps lead to misuse and disappointment. The technology works but the adoption doesn’t. Successful organizations plan for adoption from the beginning. They involve end users in design and testing, building tools that fit how people actually work. They create training and documentation before launch, not after complaints pile up. They measure adoption rates and address resistance directly rather than hoping it resolves itself. They iterate based on user feedback, treating the human side of deployment as seriously as the technical side. Include change management in your pilot plan. Budget time and resources for training and adoption. A pilot that users love has a path to production; a pilot that users ignore doesn’t. ### 5\. Create a Path to Production Many pilots succeed in isolation but have no path to production. They’re built on different infrastructure than production systems. They lack integrations with enterprise tools. They don’t meet security and compliance requirements that production demands. No one owns ongoing maintenance once the pilot team moves on. Organizations that scale design pilots with production in mind from day one. They use production-like infrastructure from the start so there’s no migration surprise. They build integrations that will scale rather than proof-of-concept workarounds. They document operational requirements—monitoring, alerting, failover, maintenance. They assign ownership for post-pilot operation before the pilot begins. Before starting a pilot, define what production deployment looks like. Build the pilot to minimize the gap between demo and deployment. ## The Scaling Playbook When you’re ready to scale a successful pilot, the process typically unfolds in four phases. During the first two weeks, validate value rigorously. Review pilot metrics against the success criteria you defined at the start. Calculate ROI and payback period with real numbers, not projections. Document lessons learned and risks discovered during the pilot. Secure executive sponsorship for scaling—if you can’t get it now, your pilot hasn’t proven enough value. Weeks three through six are about preparing for production. Address security and compliance gaps identified during the pilot. Build production-grade infrastructure that can handle real load. Create monitoring and alerting that will catch problems before users do. Develop training materials that help users succeed with the new tools. Weeks seven through ten involve limited rollout. Deploy to a subset of users and monitor closely for issues. Gather feedback and iterate quickly. Validate that production metrics match pilot expectations. This phase catches problems at manageable scale before they become enterprise-wide crises. From week eleven onward, execute full deployment. Expand to all users with confidence built from the limited rollout. Complete training and communication across the organization. Establish ongoing monitoring that will support the system long-term. Report outcomes to stakeholders to demonstrate value and build support for future initiatives. ## Signs You’re Ready to Scale You’re ready to move from pilot to production when several conditions align. Metrics prove value with clear ROI and documented baselines—not projections, but measured results. Governance is in place with security and compliance sign-off on the production deployment. Infrastructure is ready with production-grade systems that can support scale. Ownership is clear with a team accountable for operation and improvement. Users are engaged, ideally asking for broader access rather than avoiding the pilot. Executive sponsorship is confirmed with leadership committed to the investment required. ## Signs You’re Not Ready Don’t scale if you can’t quantify the business value delivered—enthusiasm isn’t evidence. Don’t scale if security or compliance have outstanding concerns that haven’t been addressed. Don’t scale if users aren’t adopting the pilot solution—production won’t fix adoption problems. Don’t scale if no one owns ongoing operation—orphaned systems become liabilities. And don’t scale if you’re scaling to “prove AI works” rather than solve a business problem—that’s the path to expensive experimentation with no business impact. ## The Path Forward Moving from AI experimentation to business impact requires more than technology. It requires clear business problems with quantified value that justify investment. It requires governance that enables rather than blocks production deployment. It requires metrics that prove outcomes, not just activity. It requires change management that drives adoption. And it requires infrastructure that supports production scale. The enterprises that master this transition will compound their AI investments, building capability on capability. Those that don’t will keep running pilots—and keep wondering why AI isn’t delivering the transformation they were promised. The [Future of Agentic use case library](https://futureofagentic.com/use-cases/) provides detailed examples of enterprise AI deployments that have successfully made this transition, with architecture patterns and governance frameworks you can adapt. *Ready to scale AI with confidence? [Talk to an expert](/schedule-a-demo/) to see how Olakai helps enterprises measure ROI, govern risk, and move from pilot to production.* [AI Governance Checklist for CISOs](https://olakai.ai/blog/ciso-governance-checklist/) [7 AI Use Cases for Customer Success Teams](https://olakai.ai/blog/customer-success-ai/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Impact Dashboard Explained Source: /blog/ai-impact-dashboard-explained [← Back to Olakai's Blog](/blog/) # Inside the AI Impact Dashboard: How Olakai Turns PR Data Into Proof of AI Value ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. April 15, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Ask most [VPs of Engineering](/use-cases/vp-engineering/) how AI coding tools are doing on their team, and you’ll get an adoption number: “80% of developers used Claude Code or Cursor last month.” That number answers a real question, but not the one the CFO is actually asking. Adoption tells you who opened the tool. It says nothing about whether the team is shipping more, shipping faster, or shipping the same amount of code with an extra subscription line item attached. That gap is wider than most engineering leaders assume. A [Black Duck survey of over 800 enterprise software engineers and DevOps professionals](https://news.blackduck.com/2026-06-09-AI-Coding-Hits-97-Enterprise-Adoption-New-Black-Duck-Study-Shows-Governance-Is-the-ROI-Multiplier), published in June 2026, found AI coding assistant adoption had hit 97% — functionally universal — while the same research pointed to governance and measurement, not adoption, as the actual multiplier on ROI. Everyone has the tool. Not everyone can prove what it’s doing. That’s the exact problem the [Olakai Agentic](/coding-iq/) AI Impact Dashboard was built to close, and it’s worth walking through how it actually does that, tab by tab, rather than taking the “proof of AI value” claim on faith. ## Overview: what you’re actually spending, and what came back The Overview tab starts with money, and it’s careful about which money is real. The Spend Summary section adds two genuinely different billing streams together: Admin API costs, which are token-billed usage pulled straight from Anthropic, Cursor, and OpenAI’s Admin APIs — the same figure that lands on the actual invoice for usage-priced plans — and licensing costs, seat subscriptions like Cursor Business or GitHub Copilot seats, prorated to the selected window. Most companies pay both at once: token bills for power users on usage plans, seat licenses for everyone else on subscription plans. Adding them together is the number finance actually writes the check for. What the dashboard refuses to call the resulting monthly figure is instructive. The “Projected 30 day” number is explicitly labeled a straight-line extrapolation, not a forecast — actual spend times 30 divided by 7, answering “if the next 23 days look like the last 7, what does a full month cost?” It doesn’t model growth, seasonality, or seat changes, and Olakai says so in the product rather than letting a rough projection masquerade as a confident prediction. That distinction matters more than it sounds like it should: a lot of AI analytics tools show a single “forecasted spend” number with no indication of how much confidence to put in it. The Codebase Outcomes section is where spend turns into a claim about output — AI Code Ratio (the percentage of lines from AI-assisted pull requests, daily) and PR Volume (AI versus non-AI PRs per day), framed explicitly as “the shipped result of the AI coding activity above.” It’s a deliberate causal chain: spend, then activity, then shipped outcome — not three unrelated charts sitting next to each other. ## PR Analysis: a three-way split, not a binary one The PR Analysis tab is the place most “is AI making us ship more?” conversations should start. Instead of a simple AI-versus-human split, every pull request in the window lands in one of three buckets: Fully Agentic (an AI agent drove it end to end — Claude Code, Cursor Agent, and similar), Human + AI Assisted (AI helped, but a person drove the work), and Non-AI. Each bucket shows both a count and its share of all PRs, and the distinction between fully agentic and assisted work is the kind of nuance that a single “AI adoption %” figure erases entirely — a team where AI opens and merges PRs unsupervised is a fundamentally different governance conversation than one where AI is a fast autocomplete for human-driven work. Underneath the headline split sits a searchable, sortable table with per-PR granularity: repository, author, percentage of AI-attributed code, which specific AI tools touched the PR, lines added and removed, cycle time, and merge date. That level of detail is what turns “our AI adoption looks healthy” into something a VP of Engineering can actually defend in a planning meeting — a specific repo, a specific tool, a specific number, not an aggregate percentage nobody can trace back to real work, and it’s the same granularity that separates a real [AI coding tool ROI metric](/blog/ai-coding-tool-roi-metrics/) from acceptance-rate vanity numbers. ## Cycle Time: the tail matters as much as the average The Cycle Time tab compares AI-assisted and non-AI pull requests at three percentiles — p50, p75, and p90 — each with a “N% faster” or “N% slower” delta column. That’s a deliberate methodological choice, not an arbitrary one: p50 tells you what a typical PR looks like, while p90 tells you whether AI is helping — or actively hurting — the slow tail of your worst cases. An average alone can hide a tool that makes routine work faster while making the hard, unusual PRs meaningfully worse; percentiles don’t let that hide. The tab also tracks issue linkage — the average number of tracked issues linked per PR via `Fixes #N` or `Closes #N` references — alongside the overall first-pass approval rate, as a check on whether AI-assisted PRs are solving planned, tracked work or generating ad-hoc changes nobody asked for. And it’s explicit about its own limits: breakdowns are available per-repository and per-AI-tool, but there is no per-team view on this tab. If you need a team-level cut, that’s a different report, not a filter you’re missing here. ## The number that goes in the board deck All of this rolls up into two composite figures designed for a leadership audience rather than an engineering one. The AI Productivity Score is a 0-100 composite built from four weighted components — Adoption (25 points), Speed (30 points), Quality (25 points), and Efficiency (20 points) — giving a single number that moves as the underlying PR data moves, with an eight-week trend sparkline. Next to it sits AI Equivalent Engineers: the productivity gain expressed as “how many additional full-time engineers’ worth of output your AI tools are producing,” which converts into a quarterly dollar figure at a configurable fully-loaded engineer cost (the default is $200,000 a year). The honest part is what happens when the sample is small. Olakai attaches an explicit confidence tier to the Equivalent Engineers figure based on how many developers qualify for the underlying before/after comparison: High confidence needs 15 or more qualifying developers and is described as suitable for executive reporting; Medium is 5 to 14, useful for planning; Low is 3 to 4, a preliminary signal only; and below 3 qualifying developers, the guidance is blunt — do not use this for decisions. That’s an unusual thing for an analytics vendor to put in its own product: a built-in instruction not to trust its own headline metric below a stated threshold. For a platform built on the premise of vendor-neutral, board-ready proof rather than vanity dashboards, that kind of restraint is the point, not an afterthought. Two things the Impact Dashboard covers in more depth than this post has room for: the Developers tab, which breaks adoption down into cohorts covered in depth in [Power, Casual, New, Idle](/blog/adoption-cohorts-wasted-ai-licenses/), and the Productivity tab’s before/after methodology, which compares each developer against their own historical baseline rather than against peers. Both are real, separately documented mechanisms worth their own explanation. None of this requires installing anything new on a developer’s machine — it runs on pull request data Olakai already has access to through your connected GitHub, Bitbucket, or GitLab organization, which is also why it works the same way regardless of which AI coding tools your teams actually use. That vendor-neutral posture is what makes the dashboard useful for a mixed fleet — Claude Code here, Cursor there, Copilot somewhere else — instead of a single-vendor usage report dressed up as an ROI tool, and it’s the same reason [generating code isn’t the same as generating value](/blog/ai-coding-tools-generating-value/) across a mixed toolset. If your organization already has a mixed toolset and a growing AI coding bill, the harder question isn’t whether to measure impact — it’s whether the number you’re currently reporting up would survive this level of scrutiny. [Talk to an Expert](/schedule-a-demo/) to see the AI Impact Dashboard against your own repositories. *Sources: [Black Duck, “AI Coding Hits 97% Enterprise Adoption,” June 2026](https://news.blackduck.com/2026-06-09-AI-Coding-Hits-97-Enterprise-Adoption-New-Black-Duck-Study-Shows-Governance-Is-the-ROI-Multiplier).* [AI Can Do Math After All: Finance Is the \#2 AI ROI Function and Nobody’s Talking About It](https://olakai.ai/blog/ai-roi-finance-cfo/) [Tokenmaxxing Is the New Lines of Code: Why Token Leaderboards Won’t Prove AI Value](https://olakai.ai/blog/tokenmaxxing-claudeonomics/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Metrics That Matter Source: /blog/ai-metrics-that-matter [← Back to Olakai's Blog](/blog/) # AI Metrics That Matter: What CFOs Actually Want to See ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. February 25, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) A CFO recently told us she received an AI progress report from her technology team. It showed 92% employee adoption, 10,000 daily prompts, 4.3 out of 5 user satisfaction, and 99.7% uptime. She looked at it for thirty seconds and asked one question: “How much revenue did this generate?” The room went quiet. That silence is playing out in boardrooms everywhere. McKinsey’s State of AI research found that [fewer than 20% of enterprises track defined KPIs for their generative AI initiatives](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai). Not 20% track them well — 20% track them at all. Yet tracking those KPIs is the single strongest predictor of whether AI delivers bottom-line impact. This is the MEASURE problem — the second step in the [SEE, MEASURE, DECIDE, ACT framework](/blog/enterprise-ai-roi-playbook/). (This is the second of four companion deep-dives — see also [SEE](/blog/ai-visibility-audit/), [DECIDE](/blog/30-day-ai-pilot/), and [ACT](/blog/ai-roi-act-framework/).) Once you can see what AI is running across your organization, the next challenge is measuring what actually matters. And what matters to the CFO is almost never what technology teams measure first. ## The Metrics Theater Problem Eighty-seven percent of CFOs say AI will be extremely or very important to finance operations in 2026, according to [Deloitte’s CFO Signals survey](https://www.deloitte.com/us/en/insights/topics/business-strategy-growth/4q-2025-cfo-signals-survey.html). They’re allocating budget accordingly — tech spending on AI is expected to rise from 8% to 13% of total technology budgets over the next two years. Yet only 21% of active AI users report that AI has delivered clear, measurable value. The problem isn’t that AI fails to deliver value. It’s that organizations measure the wrong things. They track adoption rates, session counts, and user satisfaction — metrics that answer “are people using AI?” but not “is AI making us money?” IBM found that 79% of organizations see productivity gains from AI, but only 29% can measure ROI confidently. The productivity is real. The measurement isn’t. This creates what we call metrics theater: impressive dashboards full of activity data that tell a compelling adoption story but can’t answer a single P&L question. The CFO doesn’t care that 10,000 prompts were submitted yesterday. She cares that the customer success team’s AI-assisted response time dropped from 4 hours to 45 minutes, which reduced churn by 12%, which saved $2.3 million in annual recurring revenue. That’s the same data, measured differently — and only the second version survives a board meeting. ## Vanity Metrics vs. Value Metrics The distinction matters because it determines what gets funded. When you present vanity metrics, the board sees cost without context. When you present value metrics, the board sees investment with returns. **Vanity metrics** tell you AI is being used. They include adoption rate (percentage of employees who have logged in), volume metrics (prompts submitted, queries processed, tokens consumed), technical performance (latency, accuracy, uptime), and user sentiment (satisfaction surveys, NPS from internal users). These metrics matter to engineering teams managing infrastructure. They are meaningless to the people who control the budget. **Value metrics** tell you AI is producing outcomes. They include revenue impact (deals influenced, leads converted, upsell driven by AI recommendations), cost reduction (hours saved multiplied by fully loaded labor cost, infrastructure cost avoided, error remediation reduced), risk metrics (compliance incidents prevented, data exposure avoided, audit findings reduced), and time-to-outcome (cycle time compression, faster time to market, reduced mean time to resolution). McKinsey’s research is unambiguous on this point: organizations that tie AI to specific business KPIs are significantly more likely to report EBIT impact than those that track only usage. The metric itself isn’t what drives results — the discipline of connecting AI activity to business outcomes is what drives results. ## What CFOs Actually Want to See After working with finance leaders across industries, the requests cluster into four categories: **Hard ROI — dollars in, dollars out.** CFOs want to see the investment (AI tooling costs, infrastructure, implementation, training) alongside the return (labor cost reduction, operational efficiency gains, revenue influenced). Not estimates. Not projections based on “time saved.” Actual financial impact traced to specific AI initiatives. This is where most enterprises fall short, because connecting AI activity to downstream financial outcomes requires measurement infrastructure that most organizations haven’t built. **Portfolio view — which bets are paying off.** CFOs don’t manage single projects. They manage portfolios. They want to see all AI investments side by side: cost-to-value ratio by use case, department, and AI tool. Which of the fifteen AI initiatives running across the organization are generating returns? Which should be scaled? Which should be sunset? Without this portfolio view, every budget conversation becomes a case-by-case negotiation instead of a strategic allocation. **Risk-adjusted returns — the full picture.** Revenue and cost savings are only part of the equation. [CFOs also need to see the risk profile](/blog/ai-risk-heatmap/) of AI initiatives: compliance exposure, data security incidents, governance gaps. An AI agent that saves $500,000 annually but creates unquantified regulatory risk isn’t necessarily a good investment. The metric that matters is risk-adjusted return — and that requires integrating governance data with performance data. **Forward-looking indicators — where to invest next.** Historical ROI data is table stakes. CFOs want leading indicators: which AI capabilities are showing early traction? Where are adoption curves steepest? Which teams are seeing productivity gains that haven’t yet translated to financial outcomes but will? The [World Economic Forum found](https://www.weforum.org/stories/2025/10/cost-productivity-gains-cfo-ai-investment/) that AI ROI payback typically takes 2-4 years — far longer than the 7-12 months expected for typical technology investments. Leading indicators help CFOs maintain investment conviction during that gap. ## Why Technical Metrics Don’t Predict Business Outcomes There’s a persistent assumption in enterprise AI that better technical performance equals better business results. It rarely does. An AI model can have 99% accuracy and deliver zero business value — if it’s solving a problem nobody cares about. An AI agent can process 50,000 queries per day with sub-second latency and produce no measurable revenue impact — if those queries don’t connect to business workflows that generate outcomes. MIT’s research found that 95% of generative AI pilots technically succeed but yield no tangible P&L impact. The technical metrics are green. The business impact is zero. This disconnect exists because technical metrics measure the AI system’s performance, not its contribution. Accuracy, latency, throughput, and error rates tell you whether the model is working correctly. They don’t tell you whether it’s working on the right things, for the right people, in the right workflows, at the right time. The enterprises that prove AI ROI measure both — but they lead with business outcomes and use technical metrics as diagnostic tools. When revenue impact declines, they look at technical metrics to diagnose why. When accuracy drops, they assess whether it affects a high-value workflow or a low-impact one. The hierarchy matters: business outcomes first, technical metrics in service of understanding those outcomes. ## The MEASURE Step: Building Your AI Scorecard The MEASURE step in the [SEE, MEASURE, DECIDE, ACT playbook](/blog/enterprise-ai-roi-playbook/) translates these principles into a practical framework. It starts with three requirements: **Baselines before AI.** Without a baseline, you’re reporting output, not impact. What was the metric before AI? If a customer support agent reduces average handle time, what was the average handle time before the agent was deployed? If an AI tool accelerates document review, how long did review take manually? Baselines establish the counterfactual — the “what would have happened without AI” that separates real impact from activity. **Attribution models.** AI rarely operates in isolation. When revenue increases after deploying a sales AI tool, how much of that increase is attributable to AI versus seasonal trends, marketing campaigns, or pricing changes? Attribution isn’t perfect, but it’s necessary. Even a directional attribution model (comparing teams with AI to teams without, or measuring pre/post performance in the same team) is better than claiming all improvement for AI. **Time horizons that match the business cycle.** A lead generation AI doesn’t show revenue impact in week one. It shows impact when those leads close — which in enterprise B2B might be 90 to 180 days later. A compliance AI doesn’t show risk reduction until the next audit cycle. Measuring AI ROI on a monthly sprint cadence misses outcomes that operate on quarterly or annual timelines. [CFOs understand](/blog/cfo-ai-use-cases/) long payback periods. They don’t accept unmeasured ones. The result is a balanced AI scorecard: one to two business outcome metrics (the value metrics that appear in board presentations), one to two operational metrics (the efficiency indicators that show how AI is performing), and governance metrics (risk indicators that ensure AI operates within acceptable boundaries). This isn’t about tracking more metrics. It’s about tracking the right ones — and presenting them in the language your CFO speaks. ## Getting Started If you’re tracking AI adoption but not AI outcomes, start with three steps. First, identify the three to five business KPIs that your CFO or board reviews quarterly. Second, map each AI initiative to the KPI it should influence — if an AI initiative can’t be mapped to a business KPI, that’s a signal worth examining. Third, instrument measurement: establish baselines, deploy tracking, and commit to a review cadence that matches your business cycle. The 20% of enterprises that prove AI revenue impact aren’t using more sophisticated models. They’re using more sophisticated measurement. They defined what success looks like in financial terms before deploying AI, and they built the instrumentation to prove it. That discipline — not better technology — is what separates the organizations scaling AI from the organizations stuck explaining adoption dashboards to skeptical boards. [Olakai’s custom KPI tracking](/analytics-kpis/) lets you define the business metrics that matter and connect them to AI activity in real time. And [Future of Agentic’s KPI library](https://futureofagentic.com/success-kpis) provides ready-made metric templates by use case, so you don’t have to start from scratch. **Ready to move beyond adoption dashboards?** [Talk to an expert](/schedule-a-demo/) and we’ll show you how enterprises connect AI usage to the business metrics their CFOs actually want to see. [The Enterprise AI Revenue Gap: What 3,235 Leaders Reveal](https://olakai.ai/blog/enterprise-ai-roi-gap-2026/) [The Enterprise Leader’s Toolkit for Navigating Agentic AI](https://olakai.ai/blog/future-of-agentic-enterprise-toolkit/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Pilot To Production Source: /blog/ai-pilot-to-production [← Back to Olakai's Blog](/blog/) # AI Pilot to Production: Why Measurement Is the Decisive Factor ![AI pilot to production pipeline — measurement checkpoints separating failed experiments from successful scaling](https://olakai.ai/wp-content/uploads/2026/02/ai-pilot-to-production-featured.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. February 16, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) When JPMorgan Chase launched its LLM Suite platform in summer 2024, something unusual happened: within eight months, 200,000 employees were using it daily. No mandate. No compliance requirement. Just organic adoption at a scale that most enterprises can only dream about. Meanwhile, at most other organizations, a very different story was playing out. MIT’s 2025 “GenAI Divide” report, based on 150 executive interviews and 300 public AI deployments, found that [95% of generative AI pilots fail to deliver rapid revenue acceleration](https://fortune.com/2025/08/18/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo/). Not 50%. Not even 80%. Ninety-five percent. The gap between JPMorgan and everyone else isn’t about technology, talent, or even budget. It’s about something far more fundamental: whether you can prove your AI is working. ## The Measurement Gap Is the Real Pilot Killer Enterprise AI has an accountability problem. Organizations are spending aggressively — global generative AI investment tripled to roughly $37 billion in 2025 — but most cannot answer a simple question: *What’s the ROI on our AI?* The numbers tell a stark story. McKinsey’s State of AI 2025 report found that 88% of organizations now use AI regularly in at least one business function. Yet only 6% qualify as “AI high performers” who can attribute more than 5% of total EBIT to AI. The other 82% are running AI, but they cannot connect it to business results. Deloitte’s State of AI in the Enterprise 2026 survey — covering 3,000 director-to-C-suite leaders across 24 countries — revealed what might be the most telling statistic of all: 74% of organizations want AI to grow revenue, but only 20% have actually seen it happen. That’s not a technology gap. That’s a measurement gap. ## Why “Pilot Purgatory” Is Getting Worse, Not Better You might expect the pilot-to-production problem to improve as AI matures. It’s not. S&P Global data shows that 42% of companies abandoned most of their AI initiatives in 2025, more than double the 17% abandonment rate just one year earlier. The average enterprise scrapped 46% of AI pilots before they ever reached production — a pattern we first explored in [From AI Experimentation to Business Impact](/blog/ai-experimentation-impact/). For every 33 prototypes built, only 4 made it into production — an 88% failure rate at the scaling stage. The pattern is consistent: organizations launch pilots with enthusiasm, run them for three to six months, then struggle to justify continued investment. Without baseline metrics established before deployment, there’s no way to quantify what AI actually changed. Our [AI ROI measurement framework](/blog/ai-roi-framework/) provides the methodology for establishing those baselines and tracking outcomes. Without ongoing measurement, there’s no way to distinguish a successful pilot from an expensive experiment. And without clear ROI data, there’s no executive willing to sign off on scaling. Gartner reinforced this trajectory in June 2025, [predicting that over 40% of agentic AI projects will be canceled by the end of 2027](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027), citing three drivers: escalating costs, unclear business value, and inadequate risk controls. The emphasis on “unclear business value” is telling — it’s not that the AI doesn’t work, it’s that nobody built the infrastructure to prove that it does. ![AI Pilot Purgatory — 42% of companies abandoned AI in 2025 vs. 17% in 2024, with findings from MIT, S&P Global, and McKinsey](https://olakai.ai/wp-content/uploads/2026/02/ai-pilot-purgatory-inline.webp) ## What the 5% Do Differently The companies that successfully move AI from pilot to production share a pattern that has nothing to do with having better models or bigger datasets. They build measurement into the process from day one. JPMorgan didn’t just deploy AI — they tracked adoption rates, time savings, and productivity gains from the first week. Their AI benefits are growing 30-40% annually, and they know this because they measure it. Walmart didn’t just experiment with AI in their supply chain — they documented that route optimization eliminated 30 million unnecessary delivery miles and avoided 94 million pounds of CO2 emissions. Their customer service AI cut problem resolution times by 40%, a number they can report because they established baselines before deployment. This is the pattern MIT’s research confirmed across hundreds of deployments: the companies that scale AI successfully are the ones that treat measurement as infrastructure, not an afterthought. They know which processes AI is accelerating, by how much, and at what cost. They can calculate the total cost of ownership — including the API costs, engineering time, and maintenance burden that most organizations bury in IT budgets. And they can present executives with a clear picture: here’s what AI costs, here’s what it delivers, and here’s why scaling it makes financial sense. ## The Four Phases of Scaling (and Where Most Organizations Get Stuck) Successfully moving AI from pilot to production typically follows four phases, each gated by measurement milestones rather than arbitrary timelines. **Phase 1: Validate value (weeks 1-4).** Deploy the AI solution with a small group and establish clear baselines. What does the process look like without AI? How long does it take? What does it cost? What’s the error rate? Without these pre-AI measurements, you’ll never be able to quantify impact. Most organizations skip this step entirely and then wonder why they can’t prove ROI six months later. **Phase 2: Harden for production (weeks 5-10).** Once you have evidence that the AI delivers measurable value, build the governance and monitoring infrastructure needed for scale. This means [policy enforcement](/ai-governance/), access controls, audit trails, and cost tracking. It also means ensuring someone owns ongoing operations — not as a side project, but as a defined responsibility. **Phase 3: Controlled expansion (weeks 11-16).** Roll out to a broader group while continuing to measure. Are the gains from Phase 1 holding at scale? Are costs scaling linearly or exponentially? Are new user segments finding different use cases? This phase is where many organizations discover that their pilot’s curated dataset doesn’t translate to messy real-world data — Gartner found that data quality issues derail 85% of AI projects at this stage. **Phase 4: Full deployment and continuous optimization.** With validated ROI data from the first three phases, you have the evidence to justify enterprise-wide investment. But the measurement doesn’t stop — it shifts from proving value to optimizing it. Which teams are getting the most benefit? Where are costs disproportionate to returns? What new use cases are emerging? The organizations that stall are almost always stuck between Phase 1 and Phase 2. They ran a pilot, it “seemed to work,” but they never established the baselines or tracking needed to prove it. So the pilot sits in limbo — too promising to kill, too unproven to scale. ## Buy vs. Build: A Measurement Shortcut MIT’s research uncovered a surprising finding about the build-versus-buy decision. Purchasing AI tools from specialized vendors and building partnerships succeeds roughly 67% of the time, while internal builds succeed only about 22% of the time. Our analysis of [100+ AI agent deployments](/blog/ai-agent-roi-lessons/) confirms this pattern. The gap is striking, and measurement is a significant part of the explanation. Specialized vendors have already solved the measurement problem for their specific domain. They’ve established the benchmarks, built the tracking, and validated the ROI across hundreds of customers. When an enterprise buys rather than builds, they’re importing not just the technology but the measurement framework that proves it works. Internal builds, by contrast, require organizations to solve two problems simultaneously: making the AI work *and* building the infrastructure to prove it works. Most teams focus entirely on the first problem and neglect the second. ## From Science Experiment to Business Case Harvard Business Review captured the core challenge in November 2025: “Most AI initiatives fail not because the models are weak, but because organizations aren’t built to sustain them.” Their five-part framework for scaling AI emphasizes that the bottleneck is organizational, not technical — and at the center of every organizational bottleneck is the inability to prove value. The path from pilot to production isn’t about better technology. It’s about building the [measurement infrastructure](/ai-roi/) that turns an AI experiment into a business case. That means establishing baselines before deployment, tracking outcomes continuously, calculating total cost of ownership honestly, and presenting results in terms executives care about: revenue impact, cost reduction, risk mitigation, and time to value. Without that measurement layer, every AI pilot is a science experiment. And enterprises don’t scale science experiments — they scale proven investments. **Ready to move your AI from pilot to production?** [Talk to an expert](/schedule-a-demo/) to see how Olakai helps enterprises measure AI ROI, govern risk, and scale what works across every AI tool and team. [Voice AI in the Enterprise: From Call Centers to Revenue Impact](https://olakai.ai/blog/enterprise-voice-ai/) [The Enterprise AI ROI Playbook: See, Measure, Decide, Act](https://olakai.ai/blog/enterprise-ai-roi-playbook/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Predictions 2026 Source: /blog/ai-predictions-2026 [← Back to Olakai's Blog](/blog/) # AI Predictions for 2026: What Enterprise Leaders Need to Know ![Enterprise AI network visualization showing interconnected AI agents and data flows - AI predictions 2026](https://olakai.ai/wp-content/uploads/2025/12/ai-predictions-2026-featured.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. December 30, 2025 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) As 2025 draws to a close, enterprise AI has reached an inflection point. Chatbots and copilots proved the technology works — a progression we trace in [The Evolution of Enterprise AI](/blog/enterprise-ai-evolution/). [Agentic AI](/blog/what-is-agentic-ai/) is demonstrating the power of autonomous action. But the gap between AI experimentation and AI value remains stubbornly wide for most organizations. The stakes are higher than ever. According to [Gartner](https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025), 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. That’s an 8x increase in a single year. But the same Gartner research warns that over 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. The difference between the winners and the laggards won’t be who has the most AI—it’ll be who extracts the most value from it. Here are the trends we see shaping enterprise AI in 2026—and what they mean for business leaders. ## 1\. From Pilots to Production 2026 will be the year enterprises finally move beyond pilot purgatory. Organizations that have been experimenting for 2-3 years will face a “ship or kill” moment: either prove ROI and scale, or acknowledge the experiments failed. The era of open-ended experimentation is ending. This shift has real consequences. Expect pressure to quantify AI value in business terms, not just technology metrics. Governance and measurement become requirements, not nice-to-haves. Vendors will face harder questions about real-world results, not demo magic. According to McKinsey, high-performing organizations are three times more likely to scale agents than their peers—but success requires more than technical excellence. The key differentiator isn’t the sophistication of the AI models; it’s the willingness to redesign workflows rather than simply layering agents onto legacy processes. If you’ve been running pilots, define success criteria and set a deadline. Either demonstrate value or reallocate resources to use cases that can. For a structured approach to proving value, see our [AI ROI measurement framework](/blog/ai-roi-framework/). ## 2\. The Rise of Multi-Agent Systems Single-purpose agents will give way to coordinated multi-agent systems. Just as microservices transformed software architecture, agent ecosystems will transform how enterprises automate complex workflows. Gartner reported a 1,445% surge in multi-agent system inquiries from Q1 2024 to Q2 2025—a clear signal that enterprises are thinking beyond standalone agents. This shift enables complex processes like order-to-cash and hire-to-retire to become fully autonomous. Agents will hand off work to other agents, creating agent-to-agent workflows that mirror how human teams collaborate. But governance complexity increases as agent interactions multiply—you’ll need visibility not just into individual agents but into the handoffs and decisions across the entire system. [Forrester predicts](https://www.forrester.com/blogs/predictions-2026-ai-agents-changing-business-models-and-workplace-culture-impact-enterprise-software/) that 30% of enterprise app vendors will launch their own MCP (Model Context Protocol) servers in 2026, enabling external AI agents to collaborate with vendor platforms. Gartner outlines five stages in enterprise AI evolution: Assistants for Every Application (2025), Task-Specific Agents (2026), Collaborative Agents Within Apps (2027), Ecosystems Across Apps (2028), and “The New Normal” (2029) where at least half of knowledge workers will be expected to create, govern, and deploy agents on demand. Design your agent architecture with coordination in mind now. Establish standards for how agents communicate and hand off work before the complexity becomes unmanageable. ## 3\. Governance Becomes Competitive Advantage Organizations with mature AI governance will scale faster than those without. While governance has been seen as a brake on innovation, 2026 will reveal it’s actually an accelerator—enabling confident deployment of higher-risk, higher-value use cases that competitors can’t touch. Companies with governance in place can move to production faster because security and compliance aren’t blocking deployment at the last minute. Regulatory pressure will increase with the EU AI Act fully in effect, state laws emerging in the U.S., and industry standards solidifying. Customers and partners will ask about your AI governance posture. Forrester predicts 60% of Fortune 100 companies will appoint a head of AI governance in 2026—organizations ramping up agentic exploration will especially benefit from this increased focus. [Build governance foundations](/platform/) now. Start with visibility (what AI is running?), then add controls (who can do what?), then measurement (is it working?). Our [CISO governance checklist](/blog/ciso-governance-checklist/) provides a comprehensive framework. ## 4\. The ROI Reckoning CFOs will demand clear AI ROI numbers. The days of “we’re investing in AI for the future” are ending. 2026 will require concrete evidence that AI investments are paying off. McKinsey estimates generative AI could add between $2.6 and $4.4 trillion annually to global GDP, with AI productivity gains in areas like security potentially unlocking up to $2.9 trillion in economic value by 2030. But that’s the macro picture. At the individual enterprise level, AI leaders will need to connect AI metrics to business outcomes. Activity metrics like conversations and completions won’t be enough—you’ll need cost savings, revenue impact, and time-to-value calculations. Some AI projects will be cut when they can’t prove value. Establish baselines before deploying AI. Define what success looks like in business terms. Track outcomes, not just activity. ## 5\. Shadow AI Backlash A major data breach or compliance violation caused by [shadow AI](/blog/shadow-ai-risk/) will force enterprises to take unauthorized AI use seriously. What’s been tolerated as employee experimentation will become a recognized security risk. Enterprises will invest in shadow AI detection and governance. Policies will shift from “don’t use AI” (which doesn’t work) to “use approved AI” (which gives employees a sanctioned path). Security teams will add AI-specific controls to their toolkit. Gartner’s warning about “agent washing”—vendors rebranding existing products without substantial agentic capabilities—adds another dimension: you’ll need to distinguish real AI tools from marketing rebadging. Understand your shadow AI exposure now. Provide sanctioned alternatives that meet employee needs. Build detection capabilities before an incident forces your hand. ## 6\. Industry-Specific Agents Emerge Vertical AI solutions will outperform horizontal ones. Pre-built agents for specific industries—healthcare claims processing, financial underwriting, legal document review—will deliver faster time-to-value than general-purpose platforms that require extensive customization. Industry expertise becomes as important as AI capability. The build vs. buy calculus shifts toward buy for common workflows, with differentiation coming from proprietary data and processes rather than technology. Gartner estimates only about 130 of the thousands of agentic AI vendors are real—the rest are rebranding without substance. Evaluate industry-specific AI solutions for common workflows in your sector. Reserve custom development for truly differentiating use cases where your unique processes create competitive advantage. The [Future of Agentic use case library](https://futureofagentic.com/use-cases/) provides examples across industries. ## 7\. The Talent Shift AI will change the skills organizations need—but not in the ways people expect. Demand will grow for AI governance, integration, and change management expertise. Pure AI/ML research talent will remain concentrated at large labs; most enterprises won’t build models, they’ll integrate and govern them. Change management and training become critical for adoption—technology that people don’t use delivers zero value. New roles are emerging: AI Ethics Officer, AI Governance Lead, Agent Operations. Gartner predicts that through 2026, atrophy of critical-thinking skills due to GenAI use will push 50% of global organizations to require “AI-free” skills assessments. The top five HCM platforms will offer digital employee management capabilities, treating AI agents as part of the workforce requiring HR oversight. Invest in governance and integration capabilities. Build change management into every AI project. Upskill existing staff on AI governance rather than competing for scarce model-building talent. ## 8\. Cost Optimization Pressure AI costs will come under scrutiny. Early implementations often over-spend on model API calls, infrastructure, and maintenance. 2026 will bring focus to AI unit economics and cost optimization. Cost per transaction becomes a key metric alongside accuracy and time savings. Model selection will consider cost/performance tradeoffs—not every task needs the most powerful model. Right-sizing becomes standard practice: using simpler, faster, cheaper models where appropriate, reserving expensive frontier models for tasks that truly require them. Track AI costs at the use-case level so you understand where money is going. Experiment with smaller models for routine tasks. Optimize prompts and workflows for efficiency—often the cheapest improvement is making fewer API calls through better prompt engineering. ## The Path Forward 2026 will separate AI leaders from AI laggards. The difference won’t be technology—it will be execution. Leaders will prove ROI, scale successful pilots, and build governance that enables rather than blocks. Laggards will remain stuck in experimentation, unable to prove value or manage risk. Gartner’s best case scenario projects that agentic AI could drive approximately 30% of enterprise application software revenue by 2035, surpassing $450 billion—up from 2% in 2025. By 2028, Gartner predicts 90% of B2B buying will be AI agent intermediated, pushing over $15 trillion of B2B spend through AI agent exchanges. The enterprises that build the capabilities to participate in that future will thrive; those that don’t will struggle to compete. The enterprises that succeed will treat AI not as a technology project but as a business transformation. They’ll measure what matters, govern what’s risky, and scale what works. The future of enterprise AI is measurable, governable, and valuable. 2026 is the year to make it real. *Ready to move from experimentation to execution? [Talk to an expert](/schedule-a-demo/) to see how Olakai helps enterprises measure ROI, govern risk, and scale AI with confidence.* [How AI Agents Are Revolutionizing Cybersecurity](https://olakai.ai/blog/ai-cybersecurity-agents/) [Your Most Important 2026 Resolution: Measure Your AI](https://olakai.ai/blog/measure-ai-2026-resolution/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Risk Heatmap Source: /blog/ai-risk-heatmap [← Back to Olakai's Blog](/blog/) # AI Risk Heatmap: Matching Governance to Business Value ![Diverse executive team analyzing AI risk heatmap data on screens](https://olakai.ai/wp-content/uploads/2025/12/featured-post-1224.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. December 11, 2025 · [AI Governance](https://olakai.ai/blog/category/ai-governance/) In early 2024, Deloitte Australia made headlines for all the wrong reasons. An AI-generated government report contained fabricated information—statistics that looked credible but simply didn’t exist. The result: public criticism, a contract refund, and lasting reputational damage. It’s the kind of incident that keeps CISOs up at night, but here’s what makes it instructive: the same organization might have dozens of lower-risk AI tools running perfectly fine. The mistake wasn’t using AI—it was applying insufficient governance to a high-stakes use case. This is the fundamental challenge facing every enterprise today. Not all AI use cases carry equal risk. A customer service chatbot with access to PII is fundamentally different from an internal knowledge assistant. Yet many organizations apply the same governance to both—either over-governing low-risk use cases (killing innovation) or under-governing high-risk ones (creating liability). The numbers tell the story. According to [Gartner’s 2025 research](https://www.gartner.com/en/newsroom/press-releases/2025-11-04-gartner-survey-finds-regular-ai-system-assessments-triple-the-likelihood-of-high-genai-value), organizations that conduct regular AI system assessments are three times more likely to report high business value from their generative AI investments. The governance isn’t just about risk avoidance—it’s about unlocking value. But the key insight from that same research is that governance must be proportional. Over-engineer controls for a low-risk internal tool, and you’ll strangle the innovation that makes AI valuable in the first place. The solution is [risk-proportional governance](/ai-governance/): matching controls to the actual risk profile of each AI deployment. ## The AI Risk Heatmap Think of your AI portfolio like a financial investment portfolio. You wouldn’t apply the same due diligence to a Treasury bond as you would to a speculative startup investment. The same logic applies to AI governance. Plot your AI use cases on two dimensions: business value (how important is this use case to revenue, efficiency, or strategic goals?) and risk sensitivity (what’s the potential for harm—to customers, compliance, reputation, or operations?). This creates four quadrants, each demanding a different governance approach. Let’s walk through each one with specific guidance on what controls to apply—and equally important, what controls you can skip. ### Quadrant 1: High Value, High Risk (Govern Tightly) These use cases demand robust governance. The stakes are high on both sides, and this is where incidents like Deloitte’s tend to occur. According to a [Harvard Law School analysis](https://corpgov.law.harvard.edu/2025/10/15/ai-risk-disclosures-in-the-sp-500-reputation-cybersecurity-and-regulation/), 72% of S&P 500 companies now disclose at least one material AI risk—up from just 12% in 2023. The enterprises taking AI seriously are the ones getting governance right for high-stakes use cases. Think of customer support agents with PII access, financial data analysis agents, contract review and drafting systems, and HR policy chatbots. These are the applications where a single mistake can mean regulatory penalties, lawsuits, or front-page news. The risks are significant: customer-facing AI can leak sensitive data or violate privacy regulations like GDPR and CCPA. Prompt injection attacks can manipulate agent behavior. And if an AI agent gives incorrect legal or financial advice, the liability falls on your organization—not the AI vendor. For these high-stakes use cases, you need the full governance toolkit. Role-based access control ensures only authorized personnel can interact with sensitive functions. PII detection and masking prevents accidental data exposure. Comprehensive audit logging creates the paper trail regulators and auditors will demand. Human-in-the-loop review catches mistakes before they reach customers. Regular security testing identifies vulnerabilities before attackers do. And compliance reviews before deployment ensure you’re not creating regulatory exposure from day one. ### Quadrant 2: High Value, Medium Risk (Govern Moderately) Important use cases with manageable risk. Balance controls with usability—this is where most of your productive AI tools will live. Code assistants and copilots, sales research assistants, and AI meeting note takers fall into this category. The risks here are real but contained. Your code assistant might inadvertently train on proprietary code, leaking intellectual property to the model provider. Meeting transcription tools raise consent and privacy concerns. Sales assistants might expose competitive intelligence if prompts or outputs are stored insecurely. Third-party data processing adds vendor risk to your compliance surface. Moderate governance means being smart about where you invest control effort. Zero data retention agreements with vendors prevent your IP from becoming training data. Code review requirements ensure AI-generated code gets human scrutiny before deployment. Opt-in consent mechanisms address privacy concerns for recording tools. An approved vendor list streamlines procurement while ensuring security review. Data retention policies limit your exposure window. License scanning for AI-generated code catches potential open-source compliance issues. ### Quadrant 3: Medium Value, Low Risk (Govern Lightly) Helpful use cases with limited downside. Don’t over-engineer governance here—you’ll slow down innovation without meaningful risk reduction. Internal knowledge assistants, content drafting tools, and research summarization fit this profile. The primary concerns are accuracy-related: hallucinations and inaccurate information, stale information in knowledge bases, and gaps in source attribution. These can cause problems, but they’re unlikely to trigger regulatory action or make headlines. The appropriate response is light-touch governance: basic logging for troubleshooting, user feedback loops to catch quality issues, source citation requirements to enable verification, and regular accuracy spot-checks to ensure the system remains reliable. ### Quadrant 4: Low Value, High Risk (Reconsider) Why take significant risk for marginal value? This quadrant should give you pause. AI-generated customer communications without review, automated decision-making in regulated domains without oversight, and unsupervised agents with broad system access all fall here. The recommendation is clear: either add human oversight to move these use cases into Quadrant 2, or defer them until your governance capability matures. Some risks simply aren’t worth taking for limited business benefit. ## Building Your Risk Assessment Process Creating a risk heatmap isn’t a one-time exercise—it’s an ongoing practice. Here’s how to build a systematic approach that scales as your AI usage grows. Start by inventorying your AI use cases. Create a complete list of AI tools and agents in use—including [shadow AI](/blog/shadow-ai-risk/) that employees may be using without approval. Gartner research indicates that 81% of organizations are now on their GenAI adoption journey, but many lack visibility into the full scope of AI tools their employees actually use. Your inventory should capture not just sanctioned tools, but the unsanctioned ones that represent hidden risk. Next, assess business value for each use case. Consider revenue impact (direct or indirect), efficiency gains, strategic importance, and user adoption and satisfaction. Be honest about which tools are actually driving value versus which are just interesting experiments. Then assess risk sensitivity. Evaluate the data types involved (PII, financial, health, legal), regulatory exposure (GDPR, CCPA, HIPAA, SOX), potential for customer harm, reputational risk, and operational criticality. A tool that processes health data carries different risk than one that summarizes internal documents. Plot each use case on the heatmap and prioritize accordingly. Governance investment should flow to the high-value, high-risk quadrant first—that’s where incidents occur and where governance creates the most value. Finally, match controls to risk: heavy controls for high-risk use cases, light touch for low-risk ones. The goal isn’t maximum security; it’s appropriate security. ## Common Governance Controls Control Purpose When to Apply [Centralized logging](/platform/) Audit trail for all interactions All use cases Agent registry Inventory of deployed agents All use cases Role-based access Limit who can use what High-risk use cases PII detection/masking Protect personal data Any PII exposure Human-in-the-loop Review before action High-stakes decisions Kill switch Rapid shutdown capability Autonomous agents Prompt injection testing Security validation Customer-facing agents Policy enforcement Programmatic guardrails High-risk use cases ## The Governance Spectrum Think of governance as a spectrum, not a binary. The [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework) provides a useful structure here, with implementation tiers ranging from basic documentation (Tier 1) to comprehensive automated monitoring and response (Tier 4). Most organizations will have AI use cases at multiple tiers simultaneously—and that’s exactly right. Minimal governance—basic logging, user feedback, and periodic review—is appropriate for internal tools and low-risk experiments. Standard governance adds comprehensive logging, access controls, an approved vendor list, and regular audits; this fits production tools and medium-risk use cases. Maximum governance includes all standard controls plus human-in-the-loop review, real-time monitoring, immutable audit logs, regular security testing, and compliance certification. This level is appropriate for customer-facing, regulated, and high-stakes use cases. For CISOs developing governance programs, our [AI Governance Checklist](/blog/ciso-governance-checklist/) provides a comprehensive starting point for building these controls into your organization. ## Evolving Your Heatmap Your risk profile changes over time. A Gartner survey found that organizations with high AI maturity keep their AI initiatives live for at least three years at rates more than double those of lower-maturity peers—45% versus 20%. One key differentiator is governance that evolves with the technology. Plan to reassess when new use cases emerge that require fresh assessment. Maturing use cases may need upgraded controls as they scale from pilot to production. Changing regulations—like the EU AI Act—can shift risk levels overnight. And incident learnings, whether from your own experience or publicized failures at other organizations, should inform control updates. Review your heatmap quarterly. What was acceptable at pilot may not be acceptable at scale. ## The Bottom Line Risk-proportional governance is about making smart trade-offs. Over-govern and you kill innovation. Under-govern and you create liability. The heatmap helps you find the right balance for each use case. The enterprises winning with AI aren’t the ones with the most restrictive policies or the most permissive ones. They’re the ones who’ve figured out how to match governance to risk—protecting what matters while letting innovation flourish where it can. *Ready to build risk-proportional AI governance? [Talk to an expert](/schedule-a-demo/) to see how Olakai helps you assess risk, implement controls, and govern AI responsibly.* [The Evolution of Enterprise AI: From Prediction to Action](https://olakai.ai/blog/enterprise-ai-evolution/) [AI in Finance: 5 Use Cases Every CFO Should Know](https://olakai.ai/blog/cfo-ai-use-cases/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Roi Act Framework Source: /blog/ai-roi-act-framework [← Back to Olakai's Blog](/blog/) # AI ROI EP. 4: ACT — From Approved Pilot to Enterprise-Wide Impact ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. March 5, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) A VP of Operations at a $4 billion manufacturer had the data. Three AI pilots had cleared the DECIDE gate with strong cost-to-value ratios. The CFO had approved scaling budgets. The board was expecting results by Q3. Six months later, all three initiatives were still running at pilot scale. One team couldn’t get IT to provision enterprise licenses. Another was waiting for “the right moment” to roll out to the full department. The third had scaled technically but hadn’t changed a single workflow — so the AI was running at production capacity with pilot-level impact. Everyone was acting on AI. Nobody was acting systematically. And the gap between “approved for scaling” and “delivering enterprise-wide value” was growing wider every quarter. This is the ACT problem — the fourth and final step in the [SEE, MEASURE, DECIDE, ACT framework](/blog/enterprise-ai-roi-playbook/). You’ve mapped your AI ecosystem ([SEE](/blog/ai-visibility-audit/)). You’ve connected activity to business outcomes ([MEASURE](/blog/ai-metrics-that-matter/)). You’ve run structured pilots that produce scaling decisions ([DECIDE](/blog/30-day-ai-pilot/)). Now comes the hardest part: turning those decisions into enterprise-wide results that show up on the P&L. The data says most organizations fail here. PwC’s 2025 Global CEO Survey found that [nearly half of CEOs see no meaningful return from their generative AI investments](https://www.pwc.com/gx/en/issues/c-suite-insights/ceo-survey.html). Not low returns — none. Meanwhile, Gartner projects worldwide AI spending will reach $644 billion in 2025 and continue accelerating. The money is flowing. The returns aren’t. And the difference between the enterprises that scale AI successfully and those that don’t isn’t better technology — it’s better execution frameworks for going from “this pilot works” to “this is how we operate.” ## Why Scaling Is Harder Than Piloting The pilot-to-production gap is where most AI investments die. S&P Global found that enterprises scrapped 46% of AI pilots before reaching production in 2025, and Bain reported that only 27% of companies successfully moved generative AI from testing to real-world implementation. But even among those that do scale, a separate challenge emerges: scaling the technology without scaling the impact. This happens because organizations treat scaling as a deployment problem — more licenses, more compute, more users. But deployment without transformation just gives you a bigger pilot. The AI is running at scale. The workflows haven’t changed. The organizational structures haven’t adapted. And the business outcomes remain stubbornly similar to what you saw with 50 users, even though you now have 5,000. Deloitte’s 2026 State of AI survey captured this precisely: while 74% of organizations want AI to drive revenue growth, [only about one in five have redesigned workflows around AI capabilities](https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html). McKinsey’s data reinforces the point — AI high performers are 2.8 times more likely to redesign workflows than other organizations. Dropping an AI tool into an existing process and hoping for different outcomes isn’t a scaling strategy. It’s wishful thinking at enterprise cost. The ACT step addresses this with three frameworks that take organizations from “approved pilot” to “operating at scale”: the CFO Conversation, the Cloning Playbook, and the Operating Rhythm. ## Framework 1: The CFO Conversation Every scaling decision eventually becomes a budget conversation. And budget conversations require a language that most AI teams don’t speak fluently: operational economics. The CFO doesn’t want to hear that the AI agent “saves time.” She wants to know four things, in this order: **What’s the operational cost structure?** Total cost of ownership at scale: licensing, compute, integration, support, training, and the ongoing cost of maintaining the system. Not the pilot cost extrapolated — the actual production cost model, including volume discounts, infrastructure scaling curves, and the hidden costs that only appear at scale (data quality maintenance, model drift monitoring, edge case handling). **What’s the counterfactual?** What would the organization spend doing this work without AI? This isn’t a theoretical exercise. It’s a concrete comparison: headcount cost, error rates, cycle time, and customer impact in the current state versus the AI-augmented state. The counterfactual is what makes AI ROI defensible. Without it, every efficiency claim is an assertion. With it, it’s arithmetic. **What’s the scaling math?** If the pilot showed a 3:1 return with 50 users, what does the model look like with 5,000? Scaling math isn’t linear. Some costs decrease at scale (per-unit licensing), while others increase (integration complexity, change management, support volume). The CFO wants to see the curve, not just the current point. And she wants to see sensitivity analysis — what happens to the return if adoption is 60% instead of 90%, or if the efficiency gain is 25% instead of the 40% the pilot showed. **What are the 90-day gates?** Enterprise CFOs don’t write blank checks. They fund in stages, with checkpoints tied to measurable outcomes. A 90-day gate structure might look like: month one, deploy to the first full department and validate that pilot-level performance holds at 10x scale; month two, measure the workflow redesign impact and compare against the counterfactual; month three, present the production economics to the executive committee with a recommendation for the next stage of expansion. Each gate has a defined KPI, a target, and a decision: continue, adjust, or stop. The enterprises that get CFO buy-in for scaling don’t present dashboards. They present business cases with operational economics, counterfactuals, scaling curves, and stage gates. [Building this financial frame](/blog/ai-roi-framework/) before asking for scaling budget is the single most effective way to accelerate AI investment. ## Framework 2: The Cloning Playbook Once the first AI initiative scales successfully, the question becomes: how do you replicate that success across the organization? This is where most enterprises lose momentum. Each new AI project starts from scratch — new vendors, new integrations, new measurement frameworks, new governance reviews. The result is that scaling the second initiative takes almost as long as scaling the first. The Cloning Playbook treats your first successful AI deployment as a template. It identifies the five elements that made it work — what we call the success DNA — and systematically replicates them in adjacent use cases. **The business case structure.** Not just “we saved money” but the specific format: counterfactual baseline, measured outcome, cost-to-value ratio, risk profile. When the first deployment proved value using this structure, don’t reinvent the wheel for deployment two. Use the same template. The CFO already trusts it. **The measurement infrastructure.** The hardest part of proving AI ROI is building the instrumentation that connects AI activity to business outcomes. If you built that infrastructure for customer service AI, most of it translates to sales AI or operations AI with minor modifications. The data pipelines, the KPI frameworks, the reporting cadences — these are organizational assets, not project artifacts. **The governance framework.** Your [governance approach](/blog/ciso-governance-checklist/) — data classification, security review, compliance validation, risk assessment — was designed and tested during the first deployment. Applying the same framework to deployment two eliminates months of security and legal review. The governance team already knows what “good” looks like. **The change management pattern.** How did you train users? How did you redesign workflows? How did you handle resistance? What worked and what didn’t? The human side of AI deployment is where most organizations lose the most time. Cloning the change management playbook that worked — right down to the communication cadence and the training format — compresses rollout timelines dramatically. **The executive sponsorship model.** Who championed the first deployment? What organizational authority did they need? How did they maintain momentum through obstacles? The sponsorship structure that works for one AI initiative typically works for others, because the organizational dynamics are the same: competing priorities, resource constraints, and stakeholder skepticism that only yields to demonstrated results. The math is compelling. Organizations that clone their success DNA from first deployment to second see 70-80% reduction in time-to-value compared to starting from scratch. The first initiative might take nine months to prove ROI. The second takes two to three months, because the infrastructure, governance, measurement, and organizational muscle are already built. By the third and fourth, you’re operating with a repeatable scaling engine. The key is identifying adjacent workflows — use cases that share enough similarity with your proven deployment that the success DNA transfers cleanly. If your customer service AI succeeded, the adjacent workflows might be internal helpdesk, partner support, or onboarding. If your sales AI proved value, adjacent workflows might be account management, renewals, or lead qualification. Start with the 70-80% that transfers directly and customize only the 20-30% that’s unique to the new context. ## Framework 3: The Operating Rhythm Scaling AI isn’t a project. It’s an operating discipline. The enterprises that sustain AI value over time build measurement and governance into their regular business cadence rather than treating it as a separate workstream. The Operating Rhythm runs on three cycles: **Monthly: Performance Review.** Every AI initiative that has passed the DECIDE gate gets reviewed monthly against its defined business KPIs. Not technical metrics — business outcomes. Revenue influenced, costs avoided, risk events prevented, cycle time reduced. This is the same review cadence your organization already uses for other operational metrics. AI just gets added to the agenda. The monthly review catches performance degradation early, identifies optimization opportunities, and keeps executive attention on AI value rather than AI activity. If an initiative’s KPIs are declining, the monthly review triggers investigation before the quarterly review. **Quarterly: Portfolio Assessment.** Every quarter, the AI portfolio gets assessed as a whole. Which initiatives are exceeding their ROI targets? Which are underperforming? Where should the next investment go? This is where the [portfolio view](/analytics-kpis/) that CFOs want becomes actionable. The quarterly assessment looks across all AI investments and asks: given what we now know about performance, risk, and cost, is our portfolio allocation optimal? Should we shift resources from an underperforming initiative to one showing stronger returns? Should we expand a successful deployment to new business units or geographies? **Annual: Strategic Reset.** Once a year, step back from operational metrics and assess the AI strategy against the business strategy. Are the use cases you’re scaling still aligned with where the business is heading? Has the competitive landscape changed in ways that require new AI capabilities? Are there emerging technologies — new model architectures, new vendor offerings, new integration patterns — that create opportunities your current portfolio doesn’t capture? The annual reset prevents the common trap of optimizing last year’s AI strategy while the business has moved on to new priorities. The Operating Rhythm does something that ad hoc AI management cannot: it creates organizational accountability. When AI performance is reviewed monthly alongside other business metrics, it signals that AI is a business function, not an experiment. When portfolio allocation is assessed quarterly, it prevents the resource fragmentation that kills scaling momentum. And when strategy is reset annually, it keeps AI investment aligned with business direction. ## The Convergence of Measurement and Governance Here’s what becomes clear at the ACT stage: measurement and governance aren’t separate disciplines. They’re two faces of the same capability. The enterprises with the strongest AI ROI data are also the ones with the most rigorous governance frameworks. Not because governance is a compliance exercise, but because governance forces the discipline that measurement requires. Defining what AI is allowed to do means defining what it should be doing. Instrumenting how AI performs for compliance also instruments how it performs for ROI. Maintaining audit trails for regulators also maintains the data trails that prove business value. This convergence is Olakai’s thesis: that [unified visibility](/complete-ai-monitoring/) across measurement and governance enables enterprises to scale AI with confidence rather than scaling AI and hoping for the best. When you can see every AI system, measure its business impact, govern its risk profile, and control its costs from a single platform, the ACT step becomes dramatically simpler. You’re not stitching together data from five different tools to answer a board question. You’re looking at one dashboard that shows value, risk, and cost together. The SEE, MEASURE, DECIDE, ACT playbook isn’t just a methodology. It’s an operating system for enterprise AI. And the ACT step is where that operating system proves its worth — not in a pilot, not in a board presentation, but in sustained, measurable business outcomes that compound quarter over quarter. ## Start Acting With Data The 74% of enterprises that want AI revenue growth but can’t prove it share a common failure mode: they act without the infrastructure to know whether their actions are working. They scale without counterfactuals. They expand without cloning success patterns. They operate without cadences that catch problems before they become write-offs. The 20% who prove AI ROI do something different. They build the CFO conversation before they ask for scaling budget. They clone their success DNA rather than reinventing each deployment. And they embed AI measurement into their monthly, quarterly, and annual operating rhythms so that AI value isn’t a one-time proof point — it’s a continuous, visible, defensible track record. That’s the ACT framework. And it’s the final step that turns AI from an investment line item into a measurable operating advantage. **Ready to scale your AI investments with confidence?** [Talk to an expert](/schedule-a-demo/) and we’ll show you how Olakai’s [measurement and governance platform](/platform/) turns the SEE, MEASURE, DECIDE, ACT playbook into an operating system for enterprise AI. [What Is AI Analytics? The Definitive Enterprise Guide](https://olakai.ai/blog/what-is-ai-analytics/) [NVIDIA Surveyed 3,200 Leaders. 30% Still Can’t Measure AI ROI.](https://olakai.ai/blog/nvidia-ai-report-roi-measurement/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Roi Finance Cfo Source: /blog/ai-roi-finance-cfo [← Back to Olakai's Blog](/blog/) # AI Can Do Math After All: Finance Is the \#2 AI ROI Function and Nobody’s Talking About It ![Chart showing 51% of companies see measurable AI ROI, with Finance at 42% as the second highest function](https://olakai.ai/wp-content/uploads/2026/04/ai-roi-finance-cfo-featured.png) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. April 13, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) A year ago, the knock on AI in finance was simple: it can’t do math. And honestly, the critics had a point. A [University of Waterloo study](https://techcrunch.com/2024/10/02/why-is-chatgpt-so-bad-at-math/) found that GPT-4o got basic multiplication wrong more than 70% of the time. The internet’s favorite example was even simpler than that: ask ChatGPT how many R’s are in “strawberry” and it would confidently tell you two. For CFOs and finance leaders watching from the sidelines, the message was clear. If this thing can’t count letters, it’s not touching our books. That was twelve months ago. The tools caught up faster than almost anyone predicted. Reasoning models, code execution, structured outputs, and vertical-specific AI applications have closed the gap between “can’t do math” and “cuts your financial close by a week.” And now we have the data to prove it. ## Finance Is the Second-Biggest AI ROI Story Nobody’s Talking About Silicon Valley Bank just published their [2026 State of the VC-Backed CFO report](https://www.svb.com/globalassets/trendsandinsights/reports/state-of-the-vc-backed-cfo/vc-backed-cfo-2026.pdf), surveying 230 finance leaders at high-performing venture-backed companies. The headline finding on AI: 51% of companies that budgeted for AI tools last year report measurable ROI from that spending. But the more interesting number is the breakdown by function. Product and Engineering leads at 73%, which surprises no one. The AI coding assistant market has been the loudest story in enterprise software for two years. But right behind it, at 42%, is Finance. Ahead of Marketing (41%), Customer Support (41%), Sales (34%), and Legal (27%). Finance teams are quietly generating more measurable AI returns than almost every other function in the company, and the conversation hasn’t caught up yet. Most of the media coverage, the conference panels, and the vendor marketing around AI ROI have centered on engineering productivity. That makes sense — that’s where the tooling matured first. But the SVB data tells a different story. The CFO’s office is becoming one of the most productive proving grounds for AI in the enterprise, and the returns are showing up in places that directly affect the bottom line. ## Where AI Is Delivering Real Returns in Finance So where exactly is the 42% coming from? The gains are concentrated in a handful of core finance operations that share a common trait: they’re repetitive, data-heavy, and historically consumed enormous amounts of skilled human time. **The monthly close.** A joint study from MIT Sloan and Stanford GSB, published in August 2025, analyzed hundreds of thousands of transactions across 79 companies and found that AI cuts the monthly financial close by 7.5 days on average. For anyone who’s lived through the close process, that number speaks for itself. A week back is a week of analysis, planning, and decision-making that finance teams didn’t have before. **FP&A and forecasting.** Financial planning and analysis teams are running forecast cycles 30-40% faster with AI-assisted modeling. The FP&A function has historically been one of the most strategic roles in finance but also one of the most time-constrained. When your team spends less time building the model and more time interpreting what it says, the quality of the output changes. According to a 2025 FP&A Trends survey, 53% of organizations still don’t use AI in any FP&A process, which means the early movers have a significant head start. **Accounts payable and cost analytics.** McKinsey found that 44% of CFOs now use generative AI across five or more finance use cases, up from just 7% the year before. AP processing, cost analytics, variance analysis, and fraud detection are among the most common deployments. These aren’t moonshot applications. They’re the blocking and tackling of corporate finance, automated at scale for the first time. The SVB report adds another layer to this: companies that reported ROI from AI in customer service applications showed the highest median revenue per employee at $327K, followed by Marketing at $311K and Finance at $259K. Finance may not top that particular metric, but the breadth of its AI adoption across multiple sub-functions — close, FP&A, AP, audit, compliance — makes it one of the most versatile AI verticals inside any company. ## The Spending Is Accelerating. The Measurement Isn’t. The SVB report reveals just how aggressively companies are investing in AI. Median spending on AI platforms and tools jumped from $2K in 2024 to $20K in 2025 — a 10x increase in a single year. CFOs expect that to double again to $50K in 2026. And 65% of the companies surveyed plan to spend more on AI this year than they spent on accounting software last year. That’s a striking data point. AI budgets are approaching parity with one of the most established categories in enterprise finance software. But here’s the tension: while spending is doubling, only about half of companies can actually demonstrate that the investment is working. The other 49% are spending without a clear picture of return. This is a familiar pattern in enterprise technology adoption. The budget moves faster than the infrastructure to [measure what it’s actually producing](/blog/ai-metrics-that-matter/). Deloitte’s Q4 2025 CFO Signals survey reinforces this gap. Among 200 North American CFOs at companies with $1B+ in revenue, 87% said AI would be “extremely or very important” to finance operations in 2026. Technology transformation displaced enterprise risk management as CFOs’ top priority for the first time. Yet only 21% of active AI users in finance said it had delivered clear, measurable value. The ambition is there. The measurement infrastructure, for most companies, is not. This is the core problem we’re building [Olakai](/platform/) to solve. Not running the AI, but giving finance leaders — and every other function — visibility into whether their AI investments are actually delivering returns. When you can [measure AI ROI](/ai-roi/) across tools, teams, and use cases from a single platform, the conversation with the board changes from “we think AI is working” to “here’s exactly what it’s producing.” ## Why This Matters for CFOs and Board Members Right Now The SVB data carries an implication that goes beyond operational efficiency. Companies that have demonstrated ROI from AI implementation are half as likely to have raised a bridge round or extension round in the last 12 months compared to those that haven’t. AI isn’t just saving time in the back office — it’s becoming a signal of operational discipline that investors are watching for. Meanwhile, 91% of the VC-backed companies surveyed now encourage employees to use AI at work, up from 68% last year. One in three companies is already hiring fewer junior-level employees because of AI. The workforce implications are real and accelerating, and they’re landing squarely on the CFO’s desk — headcount planning, budget reallocation, productivity benchmarking, all of it. For CFOs and board members who haven’t yet engaged deeply with AI in their own function, the SVB report should be a catalyst. The question is no longer whether AI can handle finance work. The “strawberry” era is over. The question is whether your organization can measure the value it’s already generating — and whether you can build the [framework to prove ROI](/blog/ai-roi-framework/) before your next board meeting. ## Getting Started: Three Steps for Finance Leaders If the SVB data resonates and you’re thinking about where to start, the playbook is more straightforward than it appears. First, audit what your team is already using. Gartner’s 2025 data shows that 59% of finance functions have already adopted some form of AI, but in many cases leadership doesn’t have full visibility into what tools are deployed, who’s using them, and what they’re accomplishing. Start with a [visibility audit](/blog/ai-visibility-audit/) — you can’t measure what you can’t see. Second, pick one high-volume process and measure it. The monthly close is the most obvious candidate based on the data, but AP processing and FP&A forecasting are equally strong starting points. Define a baseline, deploy an AI tool, and track the delta. The companies seeing 42% ROI in the SVB survey didn’t transform their entire finance stack overnight. They [ran structured pilots](/blog/30-day-ai-pilot/), measured the results, and scaled what worked. Third, build the measurement layer before you scale. The 49% of companies that can’t demonstrate AI ROI aren’t necessarily failing at AI — they’re failing at measurement. Put the infrastructure in place to track what your AI tools are doing across finance before you double the budget. That’s how you turn the SVB report’s 42% from a benchmark into a floor. The CFO has always been the person in the room who measures everything — revenue, burn, margins, headcount efficiency. Now that same discipline needs to be applied to AI itself. The finance leaders who figure out how to [measure their own AI investments](/use-cases/cfo/) are going to be the ones driving the next conversation with their boards. **[Talk to an Expert](/schedule-a-demo/)** about how Olakai gives finance leaders visibility into AI ROI across every tool and team. [What Your Employees Are Actually Using: The Shadow AI Opportunity](https://olakai.ai/blog/shadow-ai-opportunity/) [Inside the AI Impact Dashboard: How Olakai Turns PR Data Into Proof of AI Value](https://olakai.ai/blog/ai-impact-dashboard-explained/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Roi Framework Source: /blog/ai-roi-framework [← Back to Olakai's Blog](/blog/) # How to Measure AI ROI: A Framework for Enterprise Leaders ![Business executives reviewing AI ROI metrics in modern boardroom](https://olakai.ai/wp-content/uploads/2025/11/ai-roi-framework-photo.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. November 5, 2025 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) “What’s the ROI on our AI investments?” It’s the question every board asks, every CFO needs to answer, and every AI leader dreads. Despite billions invested in AI, most enterprises can’t answer it with confidence. Pilots proliferate, costs accumulate, and proof of value remains elusive. The scale of this measurement gap is striking. According to [McKinsey’s 2025 State of AI report](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai), 88% of organizations report regular AI use in at least one business function. But only 39% report EBIT impact at the enterprise level. Organizations are spending on AI; they’re struggling to prove it’s working. S&P Global data shows that 42% of companies abandoned most of their AI projects in 2025—up from just 17% the year prior—often citing cost and unclear value as the primary reasons. This guide provides a practical framework for measuring AI ROI—one that works whether you’re evaluating a single chatbot or an enterprise-wide AI program. ## Why AI ROI Measurement is Hard Before diving into the framework, it’s worth understanding why AI ROI is harder to measure than other technology investments. Benefits are often indirect. When AI helps an employee work faster, the benefit shows up as productivity—not a direct cost reduction. Unless you’re tracking time saved and connecting it to business outcomes, the value remains invisible. The employee doesn’t disappear; they just do more. Proving the “more” matters requires discipline most organizations lack. Costs are distributed across model APIs, infrastructure, development time, training, change management, and ongoing maintenance. Without careful tracking, it’s easy to undercount the total investment. The API costs are visible; the engineering time spent debugging prompt failures often isn’t. Baselines are missing. How long did invoice processing take before AI? What was the error rate? Without pre-AI measurements, you can’t calculate improvement. Yet most organizations deploy AI first and ask measurement questions later—by which point the baseline is lost forever. Attribution is complex. When a sales team closes more deals, is it the AI-powered lead scoring, the new sales methodology, the improved economy, or the new sales leader? Isolating AI’s contribution requires experimental rigor that few commercial settings permit. ## The AI ROI Framework Effective AI ROI measurement requires four components working together: quantifying value created, capturing total cost of ownership, calculating ROI with appropriate rigor, and benchmarking against meaningful comparisons. ![The AI ROI Framework — $3.50 average return per $1 invested, four steps: value created, total cost, ROI calculation, benchmarking](https://olakai.ai/wp-content/uploads/2026/02/ai-roi-framework-inline.webp) ### 1\. Value Created Quantify the benefits AI delivers across four categories. **Time Saved:** Calculate hours saved multiplied by fully-loaded labor cost. If an AI agent saves an accountant 5 hours per week on invoice processing, and that accountant costs $75/hour fully loaded, that’s $375/week or approximately $19,500/year in value. The formula is straightforward: hours saved per week times weeks per year times fully-loaded hourly cost. According to research, AI adoption is delivering 26-55% productivity gains for enterprises that measure carefully—but only if that saved time converts to productive work. **Errors Avoided:** Calculate the cost of errors prevented. If AI reduces invoice processing errors from 5% to 0.5%, and each error costs $150 to correct, and you process 1,000 invoices monthly, that’s $675/month or approximately $8,100/year in avoided rework. The formula: error rate reduction times monthly volume times cost per error times twelve months. **Revenue Impact:** For customer-facing AI, measure impact on conversion, upsell, or retention. If AI-powered lead qualification increases conversion from 3% to 4%, and average deal size is $50,000, and you process 100 leads monthly, that’s an additional $50,000/month or $600,000/year. This is where the biggest ROI potential lies—but also where attribution gets most difficult. **Risk Reduction:** For governance and compliance use cases, calculate the expected value of risk reduction. If AI reduces the probability of a $1M compliance violation from 5% to 1%, the expected value is $40,000 annually. Risk reduction is real value, even though it’s harder to celebrate than revenue gains. ### 2\. Total Cost of Ownership Capture all costs associated with the AI investment—not just the obvious ones. Direct costs include model API costs (per-token or per-call charges from AI providers), infrastructure (cloud compute, storage, networking), and software licenses (AI platforms, tools, orchestration software). These are the easy ones to track because they show up on invoices. Development costs include engineering time spent building, integrating, and testing; data preparation including cleaning, labeling, and pipeline development; and training and prompting work to fine-tune models and optimize outputs. These costs often get buried in general engineering budgets where they’re invisible to ROI calculations. Operational costs include maintenance (ongoing updates, monitoring, bug fixes), support (helpdesk and user support for AI tools), and change management (training, communication, adoption programs). Organizations consistently underestimate these ongoing costs. Hidden costs include governance overhead (compliance, audit, risk management), opportunity cost (what else could the team have built?), and technical debt (costs of workarounds and shortcuts that accumulate). These rarely appear in ROI models but determine whether AI investments compound or drain resources over time. ### 3\. ROI Calculation With value and cost quantified, calculate ROI using the formula: value created minus total costs, divided by total costs, times 100. For a more complete picture, also calculate payback period (months until cumulative value exceeds cumulative cost), net present value (present value of future benefits minus present value of costs), and internal rate of return (discount rate at which NPV equals zero). According to [Gartner research](https://www.gartner.com/en/newsroom/press-releases/2025-06-30-gartner-survey-finds-forty-five-percent-of-organizations-with-high-artificial-intelligence-maturity-keep-artificial-intelligence-projects-operational-for-at-least-three-years), 45% of high AI maturity organizations keep initiatives in production for three years or more, compared to only 20% in low-maturity organizations. The difference isn’t luck—it’s rigorous measurement. IBM’s research found companies realize an average return of $3.50 for every $1 invested in AI, but that average masks wide variation between disciplined organizations and those hoping for magic. ### 4\. Benchmarking Context matters. Compare your metrics against pre-AI baseline (how did the process perform before AI?), industry benchmarks (how do similar organizations perform?), and alternative investments (what ROI could you get from other uses of capital?). Without benchmarks, even impressive-sounding numbers may represent underperformance. ## Key Metrics by Use Case Different AI use cases require different metrics. For customer support agents, track adoption rate (percentage of eligible users actively using the AI), task success rate (tasks completed without errors or escalation), cost per interaction (total cost divided by number of interactions), and user satisfaction (customer and employee ratings). For invoice processing, track data extraction accuracy (percentage of fields correctly extracted), touchless processing rate (invoices processed without human intervention), exception rate (invoices requiring human review), and cost per invoice (target: $2-6 versus $15-25 for manual processing). For sales research and lead qualification, track research completeness (required data points gathered), qualification accuracy (agreement with actual sales outcomes), time to completion (minutes from assignment to delivery), and intelligence freshness (average age of data sources). For governance and compliance, track policy compliance rate (interactions complying with policies), [shadow AI](/blog/shadow-ai-risk/) detection rate (unauthorized usage identified), and audit pass rate (success rate on AI-related audits). ## Common Pitfalls Avoid these mistakes when measuring AI ROI. **Counting activity, not outcomes:** “The chatbot handled 10,000 conversations” sounds impressive—but did it actually resolve issues? Were customers satisfied? Did it reduce support costs? Activity metrics are easy to collect but often misleading. Focus on whether the activity produced the business outcome you wanted. **Overestimating time saved:** “The AI saves 30 minutes per task” only matters if that time converts to productive work. If employees fill saved time with low-value activities—or if the organization doesn’t capture the savings through higher output—the benefit is illusory. Organizations getting good results invest 70% of AI resources in people and processes, not just technology, ensuring that time savings translate to business outcomes. **Ignoring maintenance costs:** Pilot costs are easy to track; ongoing maintenance often gets lost in general IT budgets. Make sure you’re capturing the full lifecycle cost, including the engineering time spent fixing edge cases and handling failures. **Missing the baseline:** Without pre-AI measurements, you can’t prove improvement. Establish baselines before deploying AI, not after. This is the single most common and most fatal measurement mistake. **Cherry-picking metrics:** It’s tempting to highlight the metrics that look good and ignore the rest. Present a complete picture—including metrics that show room for improvement. Selective reporting destroys credibility when the full picture eventually emerges. ## Getting Started Ready to measure AI ROI? Begin by establishing baselines now—for any process you’re considering automating, measure current performance including time, cost, error rate, and volume before AI enters the picture. Define success metrics upfront. Before deploying AI, agree on what success looks like. What specific metrics will you track? Who owns them? How will you report? McKinsey found that CEO oversight of AI governance is the factor most correlated with higher self-reported bottom-line impact—especially at larger companies where executive attention ensures metrics connect to outcomes that matter. Instrument from day one. [Build measurement into your AI deployment](/ai-roi/). Capture logs, track costs, and monitor outcomes from the start. Adding instrumentation after deployment is always harder than including it from the beginning. Review regularly. AI ROI isn’t a one-time calculation. Review monthly, adjust for learnings, and report to stakeholders quarterly. Gartner found that 63% of leaders from high-maturity organizations run financial analysis on risk factors, conduct ROI analysis, and concretely measure customer impact—that discipline separates them from the majority still struggling to prove value. Connect to business outcomes. Tie AI metrics to the numbers executives care about: revenue, margin, customer satisfaction, risk exposure. Technical metrics matter for optimization; business metrics matter for funding and support. The [Future of Agentic guide to agent economics](https://futureofagentic.com/agentic-ai-101/agent-economics/) provides additional frameworks for connecting AI investment to business value. ## The Bottom Line Measuring AI ROI is harder than measuring other technology investments—but it’s not impossible. With clear frameworks, consistent measurement, and a focus on business outcomes rather than technical metrics, you can prove the value of AI investments and make informed decisions about where to invest next. BCG research shows only 4% of companies have achieved “cutting-edge” AI capabilities enterprise-wide, with an additional 22% starting to realize substantial gains. The 74% struggling to show tangible value despite widespread investment aren’t failing because AI doesn’t work—they’re failing because they can’t prove it works. Measurement is the differentiator. The enterprises that master AI ROI measurement will scale AI with confidence while others remain stuck in [pilot purgatory](/blog/ai-experimentation-impact/). *Need help measuring AI ROI across your organization? [Talk to an expert](/schedule-a-demo/) to see how Olakai provides the visibility and analytics you need to prove AI value and govern AI risk.* [Shadow AI: The Hidden Risk in Your Enterprise](https://olakai.ai/blog/shadow-ai-risk/) [AI Governance Checklist for CISOs](https://olakai.ai/blog/ciso-governance-checklist/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Roi Measurement Tools Source: /blog/ai-roi-measurement-tools [← Back to Olakai's Blog](/blog/) # 5 Tools Enterprises Actually Use to Measure AI ROI — And What None of Them Get Right ![CFO analyzing fragmented AI analytics dashboards in corporate conference room](https://olakai.ai/wp-content/uploads/2026/04/ai-roi-measurement-tools-featured.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. April 2, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Picture the quarterly board meeting at a Fortune 500 company. The CFO pulls up a slide: $12 million spent on AI tools in the past year. Copilot licenses. Cursor seats. ChatGPT Enterprise. A handful of custom agents. Three pilots that turned into [“ongoing experiments.”](/blog/ai-pilot-to-production/) Then the question: *What did we get for it?* Silence. Not because the tools aren’t being used — they are, more than anyone expected. Because nobody in the room can answer that question with a number. That’s the gap this post is about. Enterprise AI measurement today exists at three layers: tool usage and adoption (who’s using what), workflow and productivity impact (are they faster), and business outcomes (did revenue, margin, or retention actually move). The problem is structural. Every measurement tool on the market lives at layer one or two — and calls it ROI. None of them connect to layer three. That’s not a product limitation. It’s a [measurement philosophy problem](/blog/ai-metrics-that-matter/). ## 1\. Microsoft Copilot Analytics Microsoft’s built-in Copilot Dashboard tracks M365 Copilot usage across the organization: prompts submitted, documents generated, meetings summarized, emails drafted. It’s native to the Microsoft ecosystem, which means zero integration effort and instant visibility for IT admins. For a 10,000-person org paying $30–60 per seat per month, that visibility matters — you’re looking at $3.6 to $7.2 million a year in Copilot licensing alone. ![Microsoft Viva Insights Copilot Analytics dashboard showing usage metrics](https://olakai.ai/wp-content/uploads/2026/04/microsoft-copilot-analytics-1.webp) Microsoft Viva Insights Copilot Analytics — tracks usage activity, not business outcomes. The weakness is fundamental. The dashboard provides a 28-day aggregated view with no per-user ROI correlation and no connection to business outcomes. You know Copilot is being used. You know how often. You have no idea whether it’s helping. Microsoft also disclosed a metric computation bug that underreported email engagement data for nine months — a quiet reminder that vendor-reported metrics aren’t always reliable, even from the vendor itself. Activity is not impact. ## 2\. GitHub Copilot and GitLab Duo Metrics GitHub Copilot reports code suggestion acceptance rates (averaging 27–30%), time saved per developer (roughly 3.6 hours per week), and suggestion frequency across your engineering org. GitLab Duo offers similar dashboards for its AI features. Developer teams love this data. Engineering leaders use it to justify expansion, track adoption curves, and identify which teams are getting the most value from AI-assisted coding. ![GitHub Copilot Metrics dashboard showing acceptance rate, active users, and adoption data](https://olakai.ai/wp-content/uploads/2026/04/github-copilot-metrics.png) GitHub Copilot Metrics — acceptance rates and usage charts, but no connection to business outcomes. The limitation is scope. These tools measure developers — and only developers using that specific tool. Your marketing team running campaigns through ChatGPT? [Invisible](/blog/shadow-ai-risk/). Your finance team using Gemini for forecasting models? Invisible. Your legal team reviewing contracts with Claude? Invisible. And “acceptance rate” is a product metric, not a business metric. A 30% acceptance rate tells you developers kept 30% of suggestions. It says nothing about whether those suggestions shipped faster, reduced bugs, or moved a revenue number. Dev-only measurement in an enterprise where every department uses AI is a partial answer at best. ## 3\. GetDX, Pluralsight Flow, and LinearB These platforms measure developer productivity through DORA metrics, developer experience scores, PR cycle time, and deployment frequency. They’re legitimate engineering intelligence tools — [McKinsey’s 2025 State of AI report](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) found that 88% of organizations have adopted AI, but only 39% can report any EBIT impact. These developer platforms didn’t cause that gap, but they don’t close it either. ![DX developer intelligence platform architecture showing system data, experience sampling, metrics, and benchmarks](https://olakai.ai/wp-content/uploads/2026/04/dx-platform.webp) DX platform architecture — strong developer intelligence, but scoped to engineering teams. Image courtesy of DX. The positioning is explicit: these are developer productivity tools, not AI ROI platforms. Some vendors have started rebranding DORA metrics as “AI measurement,” adding overlays that compare AI-assisted versus non-AI-assisted PRs. That’s useful context for an engineering VP. It’s not what the CFO means when she asks about AI ROI. DORA metrics existed before AI coding tools did. Relabeling them doesn’t make them an AI measurement strategy. ## 4\. Workday and ServiceNow Built-In AI Analytics Both Workday and ServiceNow — along with Salesforce Einstein, SAP Joule, and dozens of other enterprise platforms — now report on their own AI feature usage. Workday shows you AI-generated job descriptions and skills recommendations. ServiceNow tracks virtual agent deflection rates and case summarization usage. The strength is obvious: zero integration effort, immediate availability, and perfect accuracy within that vendor’s walls. ![ServiceNow AI Control Tower dashboard showing productivity hours, AI users, and daily actions](https://olakai.ai/wp-content/uploads/2026/04/servicenow-ai-control-tower.webp) ServiceNow AI Control Tower — comprehensive within ServiceNow, but silent on every other AI tool in the stack. Image courtesy of ServiceNow. The weakness is equally obvious: each platform is a silo. Workday tells you about Workday AI. ServiceNow tells you about ServiceNow AI. Salesforce tells you about Salesforce AI. Nobody tells you about all of them together. For an enterprise running AI across fifteen platforms, you’d need to log into fifteen dashboards, normalize fifteen different metric definitions, and somehow reconcile them into a single view. Most don’t try. The result is that enterprise AI measurement defaults to whoever shouts the loudest in the vendor review. ## 5\. Custom BI Dashboards (Tableau, Power BI) This one isn’t a product — it’s a pattern. Many enterprises, frustrated by the limitations above, decide to build their own AI measurement dashboard. Pull API data from each AI tool into a data warehouse, model it in dbt or Databricks, visualize it in Tableau or Power BI. The appeal is total customization: you define the metrics, you own the schema, you control the narrative. The reality is expensive and slow. Enterprise-grade BI implementations take three to six months for multi-source deployments, and first-year costs for a 5,000-person org run between $510K and $1.2 million — often more than the AI tools being measured. There’s no standardized schema for AI usage data, no external benchmarks to compare against, and every API change from every vendor breaks something. Most custom dashboards become the responsibility of one or two analysts, and when they leave, the dashboard dies with them. You’ve built a measurement tool that costs more than what it measures. ## The Real Problem: A Measurement Philosophy Gap Each of the tools above measures AI in isolation. Microsoft measures Microsoft. GitHub measures GitHub. Workday measures Workday. The custom dashboard tries to stitch them together but creates a maintenance burden that’s unsustainable at enterprise scale. Meanwhile, the actual ROI question is cross-enterprise: which teams adopted which tools, what changed in their output, and did any of it [move a business metric](/blog/ai-visibility-audit/)? That question requires connecting three dots: adoption data (who’s using what), productivity signals (what changed in their work), and business outcomes (did it matter). [Forrester’s 2026 Predictions report](https://www.forrester.com/blogs/predictions-2026-ai-moves-from-hype-to-hard-hat-work/) found that fewer than one in three AI decision-makers can tie AI value to P&L changes. Not because they aren’t trying — because their tools don’t connect those layers. That’s not a product gap. It’s a measurement philosophy gap. You can’t vibe-code accountability. ## What Olakai Does Differently This is the problem we built [Olakai](/blog/ai-roi-framework/) to solve. Not another vendor-specific dashboard. Not another developer productivity overlay. A vendor-neutral analytics and governance platform that works across your entire AI stack — ChatGPT, Copilot, Gemini, Cursor, Claude, custom agents, and the AI features embedded in your SaaS applications — and connects what’s being used to what it’s actually producing. Olakai is structured around three product lines, each covering a category that the tools above treat in isolation. Assistive IQ measures adoption, productivity, and [shadow AI](/shadow-ai/) across chatbots and copilots — deployed through a Chrome extension that takes minutes, not months. Coding IQ connects to your GitHub org and AI coding tool providers to unify cycle time data, AI-assisted PR rates, developer adoption cohorts, and cost-per-PR across Copilot, Cursor, Claude Code, and Windsurf in a single view. Agent IQ tracks custom agentic workflows with execution metrics, success rates, and cost-per-execution tied to business KPIs you define. None of these exist in separate tools. They exist in one platform, measured against the same outcomes. ![Olakai Agent IQ dashboard showing cross-enterprise AI analytics with custom KPIs and business outcomes](https://olakai.ai/wp-content/uploads/2026/04/olakai-agentic-measure.png) Olakai — unified AI analytics across assistive, coding, and agentic AI in a single platform. The difference isn’t just breadth — it’s the connection between layers. Every tool in this article measures activity. Olakai connects that activity to business outcomes through [custom KPIs](/analytics-kpis/) that map AI usage to the metrics your CFO actually reports on: revenue influenced, cost avoided, time recaptured, risk reduced. When the board asks what $12 million in AI spend produced, Olakai is the platform that gives you the answer — not a usage chart, not an acceptance rate, but a number tied to a business result. We’re not replacing the tools above — most of our customers use several of them. Microsoft Copilot Analytics still tells you how Copilot is being used. GitHub Copilot Metrics still shows acceptance rates. ServiceNow’s AI Control Tower still tracks its own AI features. What none of them do is answer the cross-enterprise question: across all of these tools, all of these teams, all of these investments — are we getting ROI, and where? That’s the layer Olakai provides. And with [Kai](/platform/), anyone on the team can ask that question in plain language and get a reasoned, data-backed answer in seconds — no analyst required, no dashboard to build. ![Olakai Kai conversational AI assistant answering What is my AI ROI this month with data-backed summary](https://olakai.ai/wp-content/uploads/2026/04/olakai-kai-assistant-scaled-1.webp) Kai — ask “What’s my AI ROI this month?” and get a reasoned, data-backed answer in seconds. [See how Olakai connects AI adoption to business outcomes →](/schedule-a-demo/) [Is Your $500K AI Coding Tool Investment Paying Off? What the Data Shows](https://olakai.ai/blog/ai-coding-tool-roi/) [What Your Employees Are Actually Using: The Shadow AI Opportunity](https://olakai.ai/blog/shadow-ai-opportunity/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Sales Use Cases Source: /blog/ai-sales-use-cases [← Back to Olakai's Blog](/blog/) # 5 AI Use Cases Every Sales Team Should Know ![5 AI use cases for sales teams - intelligent pipeline automation](https://olakai.ai/wp-content/uploads/2025/10/ai-sales-use-cases-featured.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. October 17, 2025 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) When a regional director at a Fortune 500 technology company analyzed where his sales team actually spent their time, the results were sobering. His top performers—the reps closing the biggest deals—were spending only 35% of their day actually selling. The rest went to research, data entry, follow-up emails, and preparing forecasts that were often wrong anyway. This isn’t unusual. Sales teams are under constant pressure to do more with less: more calls, more meetings, more deals—with the same headcount. According to [research on AI in sales](https://www.cirrusinsight.com/blog/ai-in-sales), 83% of sales teams using AI experienced growth in 2025, compared to 66% of teams without AI—a 17 percentage point performance gap. Teams that frequently use AI report a 76% increase in win rates, 78% shorter deal cycles, and a 70% increase in deal sizes. AI agents are changing the game by automating the tedious work that eats into selling time while improving the quality of every customer interaction. But not all AI use cases are created equal. Some deliver quick wins with minimal risk; others require significant investment but promise transformative results. Here are five [AI use cases](/use-cases/) every sales leader should understand—from practical starting points to advanced implementations. ## Overview: Sales AI Use Cases at a Glance Use Case Typical ROI Complexity Time to Value Lead Qualification 6-10x Low 3-5 weeks Account Research 8-10x Low 2-3 weeks Deal Acceleration 10-15x Medium 3-5 weeks Sales Forecasting 12-15x Medium-High 4-6 weeks Competitive Intelligence 5-8x Low 2-4 weeks ## 1\. Lead Qualification: Score, Route, and Follow Up Automatically Marketing generates thousands of leads monthly, but sales teams waste precious time sifting through unqualified prospects instead of engaging with high-intent buyers. Response times stretch from hours to days, killing conversion rates. The vast majority of sales teams now use AI daily, with 52% using it specifically for data analysis including lead scoring, pipeline analysis, and forecasting. An agentic lead qualification workflow receives leads from forms, events, and campaigns, then scores them based on firmographic fit and engagement signals. It routes qualified leads to the appropriate sales representative by territory or expertise, then sends personalized follow-up emails within minutes rather than hours. Predictive lead scoring driven by AI enhances lead-to-customer conversion rates by as much as 28%—that’s not incremental improvement, it’s transformational. The impact compounds across the funnel. Organizations see a 30% increase in sales-qualified leads reaching reps, a 50% reduction in lead response time, and 6-10x ROI through sales productivity gains. For a deeper framework on measuring these gains, see our guide to [measuring AI ROI in the enterprise](/blog/ai-roi-framework/). This is an ideal first AI use case for sales. The workflow is straightforward (score, route, follow up), integrations are standard (CRM, email, marketing automation), and the risk is low. You can start with simple scoring rules and add sophistication over time. ## 2\. Account Research and Buyer Intelligence: Enter Every Call Prepared Sales reps often enter calls unprepared, missing key stakeholders and failing to understand buyer context. Manual research takes hours and produces incomplete information, leading to weak first impressions and missed multi-threading opportunities. The reality is that selling time is precious, and every minute spent on research is a minute not spent building relationships. An account research agent changes this calculus entirely. It researches target accounts automatically, surfaces decision-maker profiles from LinkedIn, identifies all stakeholders involved in the buying process, maps organizational hierarchies, and analyzes buyer priorities based on news, financials, and company announcements. Reps receive comprehensive account briefs moments before calls—context that would take hours to compile manually, delivered in seconds. According to [research on AI sales agents](https://superagi.com/from-lead-scoring-to-forecasting-how-ai-sales-agents-are-revolutionizing-sales-efficiency-in-2025/), sales representatives save 2-5 hours per week with AI, and teams report up to 44% more productivity. The impact on meeting quality is substantial: 30% reduction in research time, 20% higher meeting engagement scores, and 8-10x ROI through more effective conversations. Start with the most critical data points—company news, key executives, recent funding—and expand from there. Integration with LinkedIn Sales Navigator and news APIs is straightforward, and the use case delivers value from week one. ## 3\. Deal Acceleration and Bottleneck Detection: Revive Stalled Opportunities Deals often sit idle for weeks as reps forget follow-ups or lack clarity on next steps. Without visibility into engagement gaps, deals slip through cracks or extend sales cycles unnecessarily. By the time anyone notices, the opportunity may be lost to a faster competitor—or simple inertia. A deal acceleration agent continuously monitors velocity across the pipeline, identifying stalled deals that haven’t progressed in specific timeframes. It analyzes engagement history to find gaps, recommends specific next best actions based on deal context and stakeholder responses, and auto-generates personalized follow-up messages. The system learns from successful deals to improve recommendations over time. The numbers are compelling. According to research, 69% of sellers using AI shortened their sales cycles by an average of one week, while 68% said AI helped them close more deals overall. ZoomInfo documented a 30% increase in average deal sizes and a 25% faster sales cycle after adopting AI-driven pipeline management. The impact adds up: 25% faster sales cycles, 15% higher close rates on stalled deals, 40% reduction in lost opportunities, and 10-15x ROI through recovered revenue that would otherwise have slipped away. Getting started is straightforward. Define what “stalled” means for your business—7 days without activity? 14 days in the same stage?—then build rules to surface at-risk deals. Start with notifications before adding automated outreach. ## 4\. Sales Forecasting and Pipeline Inspection: Predict with Confidence Manual sales forecasting is time-consuming, frequently inaccurate (often off by 20% or more), and reactive to pipeline problems rather than anticipating them. Sales leaders struggle to identify which deals are truly at risk, leading to missed forecasts, revenue surprises, and difficult conversations with finance and the board. An AI forecasting agent continuously monitors the sales pipeline, analyzing deal progression and identifying risks like stalled activity, budget changes, and competitive threats. It predicts close probabilities using machine learning trained on your historical data, and flags deals requiring immediate attention. For deals forecasted to close within 30 days, leading AI systems achieve 90-95% accuracy—far better than gut instinct or spreadsheet models. Companies integrating AI into forecasting have seen accuracy improve by 40%, enabling better strategic decisions about hiring, capacity, and resource allocation. AI-driven CRM analytics result in a 20% increase in sales forecasting accuracy, improving operational decision-making across the organization. The impact is substantial: 30% increase in forecast accuracy, 40% reduction in forecast preparation time, 30% increase in average deal sizes through early intervention on at-risk opportunities, and 12-15x ROI through better resource allocation. This is a more advanced use case requiring clean CRM data and historical outcomes to train models. Start with rule-based risk flags, then layer in machine learning predictions as you accumulate data. The [Future of Agentic use case library](https://futureofagentic.com/use-cases/) includes detailed sales forecasting architectures. ## 5\. Competitive Intelligence: Know Your Battleground Reps encounter competitors in nearly every deal but lack current intelligence on positioning, pricing, and weaknesses. (For how AI is transforming the other side of the revenue equation, see our guide to [AI use cases for customer success teams](/blog/customer-success-ai/).) Competitive information is scattered across wikis, Slack channels, and tribal knowledge—often outdated or incomplete by the time it reaches the frontline. A competitive intelligence agent continuously monitors competitor activity: website changes, press releases, product updates, and pricing changes. It synthesizes intelligence into battle cards that reps can access in the moment. It surfaces relevant competitive insights within deal context, and alerts reps when competitors are mentioned in accounts they’re working. The broader AI for sales and marketing market is forecasted to grow from $57.99 billion in 2025 to $240.58 billion by 2030, and competitive intelligence is one of the fastest-growing segments. Organizations see higher win rates against key competitors, faster ramp time for new reps who don’t need to absorb years of tribal knowledge, and 5-8x ROI through improved competitive positioning. Start by identifying your top 3-5 competitors and implementing basic monitoring (website changes, news mentions). Layer in win/loss analysis from closed deals to surface what’s actually working in competitive situations. ## Governance Considerations for Sales AI As you implement these use cases, governance matters more than you might expect. Data quality is foundational. Agents are only as good as the data they’re built on. Clean CRM data, accurate contact information, and complete deal records are prerequisites. Gartner (2025) finds that cross-functional alignment reduces AI implementation time by 25-30%, and much of that alignment involves ensuring data is reliable enough to power AI recommendations. Keep humans in the loop for high stakes. For deal acceleration and forecasting, consider maintaining human oversight for recommendations that could affect customer relationships or major resource decisions. AI should inform judgment, not replace it entirely. Measure outcomes, not just activity. Track whether AI-qualified leads actually convert, whether recommended actions actually accelerate deals, whether forecast accuracy actually improves. The goal is business results, not impressive-sounding metrics. For a framework on connecting AI activity to business outcomes, see our guide to [AI ROI measurement](/blog/ai-roi-framework/). Start simple, then scale. Begin with one use case, prove value, build governance foundations, then expand. Trying to do everything at once is a recipe for failure. ## Getting Started If you’re ready to bring AI to your sales organization, start by auditing your current process. Where do reps spend time on non-selling activities? Where do deals stall? What data is missing or unreliable? Pick one use case—lead qualification or account research are ideal starting points with low complexity, high impact, and fast time to value. Define success metrics upfront, tying measurements to business outcomes (revenue, conversion, cycle time) rather than just activity. Build governance from day one by establishing logging, measurement, and oversight before deploying to production. For industry-specific guidance, see our [technology and software](/industries/technology-software/) industry page. The sales organizations that master AI will close more deals, faster, with fewer wasted hours. Salesforce reports that sales teams leveraging AI are 1.3 times more likely to experience revenue growth. That’s the gap between thriving and struggling in an increasingly competitive market. *Want to see how leading sales organizations are implementing these use cases? [Talk to an expert](/schedule-a-demo/) to learn how Olakai helps you measure ROI and govern AI agents across your sales stack.* [What is Agentic AI? A Guide for Enterprise Leaders](https://olakai.ai/blog/what-is-agentic-ai/) [Shadow AI: The Hidden Risk in Your Enterprise](https://olakai.ai/blog/shadow-ai-risk/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ai Visibility Audit Source: /blog/ai-visibility-audit [← Back to Olakai's Blog](/blog/) # The AI Visibility Audit: What You Can’t See Is Costing You ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. February 19, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) The CIO of a mid-market financial services firm thought she had a handle on AI adoption. Her team had sanctioned three tools, trained 200 employees, and built a governance policy around them. Then she ran an AI visibility audit. The audit found 23 AI tools running across the organization — seven times what she expected. Customer service had adopted a chatbot through a free trial. Marketing was using three different content generators. Two engineering teams were running code assistants that had never been security-reviewed. And an entire business unit had been piping client data through an AI summarization tool that stored data on external servers. She’s not unusual. According to the [Torii 2026 Benchmark Report](https://www.globenewswire.com/news-release/2026/02/24/3243646/0/en/Torii-2026-Benchmark-Report-AI-Isn-t-Consolidating-SaaS-It-s-Expanding-Shadow-IT.html), 84% of organizations consistently discover more AI tools than expected during audits. And 31% find new unsanctioned tools every single month. This is the SEE problem — the first and most foundational step in the [SEE, MEASURE, DECIDE, ACT framework](/blog/enterprise-ai-roi-playbook/) for proving AI ROI. (This is the first of four companion deep-dives — see also [MEASURE](/blog/ai-metrics-that-matter/), [DECIDE](/blog/30-day-ai-pilot/), and [ACT](/blog/ai-roi-act-framework/).). You cannot measure what you cannot see. And in most enterprises today, the AI landscape is far larger, more fragmented, and more exposed than anyone in the C-suite realizes. ## The Visibility Crisis by the Numbers The scale of unsanctioned AI usage has grown faster than most security and IT teams anticipated. A 2025 UpGuard study found that [more than 80% of workers — including nearly 90% of security professionals](https://www.cybersecuritydive.com/news/shadow-ai-employee-trust-upguard/805280/) — use unapproved AI tools on the job. That last part bears repeating: the people responsible for protecting the organization are themselves using tools that haven’t been vetted. Deloitte’s 2026 State of AI survey tells the supply side of this story. Workforce access to AI tools expanded by 50% in a single year, from fewer than 40% of employees to roughly 60%. But that figure only counts sanctioned tools. The actual adoption rate — including shadow AI — is far higher. Research from Portal26 found that 73.8% of ChatGPT accounts used in the workplace are non-corporate accounts that lack enterprise security and privacy controls. For Gemini, that figure is 94.4%. The result is an AI ecosystem that leadership cannot see, security cannot govern, and finance cannot account for. Only 38% of organizations report knowing which AI applications their employees actually use. ## What Invisibility Actually Costs The cost of this visibility gap isn’t hypothetical. IBM’s 2025 Cost of a Data Breach report found that [breaches involving shadow AI add $670,000 to the average breach cost](https://www.joneswalker.com/en/insights/blogs/ai-law-blog/the-ai-oversight-gap-ibms-2025-data-breach-report-reveals-hidden-costs-of-ungov.html) compared to organizations with low or no shadow AI exposure. The average organization now experiences 223 AI-related data security incidents per month — incidents that range from sensitive data shared with external AI services to policy violations that create compliance exposure. But security costs are only one dimension. Hitachi Vantara research estimates that data infrastructure issues — many driven by ungoverned AI tooling — contribute to $108 billion in wasted annual AI spend across enterprises. When teams adopt AI tools independently, they duplicate capabilities, fragment data flows, and create redundant infrastructure costs that nobody tracks because nobody can see the full picture. Then there’s the opportunity cost. If you don’t know what AI your organization is running, you cannot measure whether it’s working. You cannot identify which tools deliver value and which ones burn budget. You cannot rationalize spending, consolidate licenses, or negotiate enterprise agreements. And you cannot answer the one question the board increasingly cares about — [what’s the return on our AI investment](/blog/ai-roi-framework/) — because you don’t even know what the investment includes. ## Why Traditional Discovery Fails Most IT organizations approach AI discovery the same way they approach software asset management: check the procurement records, run a network scan, send out a survey. None of these methods work for AI. Procurement records miss AI tools that employees adopt through free tiers, browser extensions, or personal accounts. Network scans miss browser-based AI tools that look like regular web traffic. Surveys depend on employees self-reporting usage they may not think of as “AI” — or usage they know isn’t sanctioned and don’t want to disclose. The deeper problem is velocity. Employees adopt new AI tools faster than security teams can evaluate them. Eighty-three percent of organizations report that employees install AI tools faster than security can track, according to industry surveys. A quarterly discovery audit is fundamentally mismatched against a weekly adoption cycle. And the challenge is getting more complex, not simpler. Embedded AI features — AI capabilities built into tools employees already use, like email clients, CRM platforms, and productivity suites — fly under the radar entirely. An employee isn’t “adopting a new AI tool” when their email client adds AI-powered reply suggestions. But the data exposure risk is real, and the cost shows up in per-seat licensing increases that finance sees but can’t attribute. ## What a Real AI Visibility Audit Looks Like A proper AI visibility audit goes beyond inventory. It answers four questions that are prerequisites to everything else in the [AI ROI playbook](/blog/enterprise-ai-roi-playbook/): **What AI is running?** A complete catalog of AI tools, models, and capabilities across the organization — including assistive AI (copilots, chatbots, content generators), agentic AI (autonomous agents executing workflows), and embedded AI (features within existing software). This isn’t a one-time list. It’s a continuously updated inventory that captures new tools as they appear. **Who is using it?** Usage patterns by team, department, role, and individual. Not to police employees, but to understand where AI adoption is concentrated, where training gaps exist, and where usage patterns suggest risk or opportunity. If 60% of your customer success team uses an AI tool daily but 5% of your sales team does, that’s a signal worth understanding. **What data is it touching?** The critical question from both a security and compliance perspective. Which AI tools have access to customer data, financial records, intellectual property, or regulated information? Are employees sharing sensitive data with external AI services? The [shadow AI risk](/blog/shadow-ai-risk/) isn’t just that unauthorized tools exist — it’s that unauthorized tools often handle the most sensitive data, because employees turn to AI precisely when they’re working with complex, high-value information. **What is it costing?** The total cost of AI across the organization, including sanctioned licenses, API consumption, infrastructure, and the hidden costs of shadow AI — duplicate tools, wasted capacity, and the remediation costs when things go wrong. Until you can see the full cost picture, you cannot calculate ROI. ## From Visibility to Value The SEE step isn’t an end in itself. It’s the foundation that makes everything else possible. Once you have visibility into your AI ecosystem, you can move to MEASURE — connecting AI activity to business outcomes. You can identify which tools are delivering value and which are creating risk. You can rationalize spending, consolidate tooling, and negotiate from a position of knowledge rather than ignorance. The enterprises that close the AI revenue gap — the 20% who prove AI drives results, according to [Deloitte’s 2026 survey](https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html) — start here. Not with measurement. Not with governance. With visibility. Because every dollar of AI ROI you can prove is built on a foundation of knowing what AI you have, who’s using it, what data it touches, and what it costs. The visibility audit typically reveals three immediate value opportunities: tool consolidation (reducing redundant AI spending by 20-30%), risk reduction (identifying unvetted tools handling sensitive data), and measurement readiness (instrumenting high-value AI workflows for ROI tracking). Most enterprises find that the audit pays for itself through spend rationalization alone. **Ready to see what AI is actually running across your organization?** [Talk to an expert](/schedule-a-demo/) and we’ll show you how Olakai provides [unified visibility](/complete-ai-monitoring/) across your entire AI ecosystem — sanctioned and shadow, assistive and agentic. [The Enterprise AI ROI Playbook: See, Measure, Decide, Act](https://olakai.ai/blog/enterprise-ai-roi-playbook/) [The Enterprise AI Revenue Gap: What 3,235 Leaders Reveal](https://olakai.ai/blog/enterprise-ai-roi-gap-2026/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Anthropic Mythos Compute Trap Source: /blog/anthropic-mythos-compute-trap [← Back to Olakai's Blog](/blog/) # Anthropic’s Mythos Crisis: What a $900B Raise Tells Enterprise AI Buyers ![Vast server farm receding to a horizon with only a small cluster glowing — capacity exists but compute is rationed](https://olakai.ai/wp-content/uploads/2026/05/anthropic-mythos-compute-trap.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. May 4, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) The most safety-conscious frontier AI lab on earth just told the market it cannot economically serve 120 customers on a single model. Then it set out to raise the largest private funding round in history to fix the math, and the White House looked at the numbers and said no. If you are an enterprise leader being pressured to chase the next frontier capability, this is the data point that should land on your desk this week. ## The story so far On April 7, [Anthropic](https://www.anthropic.com/) announced Claude Mythos, a frontier model so capable at finding and exploiting software vulnerabilities that the company chose not to release it publicly. Instead, it stood up Project Glasswing, a controlled-access program of about 50 vetted partners. The list reads like a Fortune 50 cybersecurity wishlist: Apple, Microsoft, Google, AWS, Nvidia, JPMorgan Chase. Within days of the announcement, Bloomberg reported that unauthorized users had already accessed Mythos through a third-party vendor environment, leveraging publicly available techniques and information from the earlier Mercor breach. They reportedly also had access to other unreleased Anthropic models. Week one, fifty partners, breached. ## What changed this week Then came two stories that, together, reframe the entire conversation. First, The Wall Street Journal reported, with Bloomberg confirmation, that Anthropic proposed expanding Mythos access to roughly 70 additional companies, bringing the total to about 120. The White House told Anthropic, privately, that they oppose the move. The first reason is the obvious one: security. The system was compromised in week one with 50 partners, and adding 70 more increases the attack surface in ways the administration is not comfortable with. The second reason has not had nearly enough oxygen in the coverage. The administration also told Anthropic, in plain English, that the company does not have enough computing power to serve 120 customers without degrading the U.S. government’s own access to the model. Read that sentence twice, because it is the entire story. ## The compute math Now follow the money on the same news cycle. Bloomberg reported the same week that Anthropic is in early talks for a funding round that would value the company at over $900 billion, more than double its current $350 billion mark and enough to leapfrog [OpenAI](https://openai.com/)‘s $850 billion valuation and make Anthropic the most valuable AI startup in the world. What is the capital for? Per reports tied to the WSJ coverage, part of the raise is specifically aimed at funding the compute capacity required to scale Mythos. So here is the math, the way it actually reads. You have the most safety-conscious frontier lab on earth, what the lab itself describes as the most powerful model it has ever built, and a customer list of 50 hand-picked partners, every one of them a Fortune 50 or critical-infrastructure player. And you cannot serve them, plus 70 more, without one of two things happening: either the U.S. government’s access gets degraded, or you raise potentially the largest private funding round in history to buy enough compute to make the math work. The administration looked at that math and said no, and that is not a security-only objection. It is a market signal. ## What this means for your enterprise AI roadmap If you are a CIO, CISO, CHRO, or CFO anywhere near AI strategy right now, this story should land on your desk with one question attached. If Anthropic, with Google’s $40 billion commitment and Amazon’s $25 billion commitment behind it, cannot economically serve 120 customers on one model, what makes you think you should be in line to chase the next frontier capability layer right now? There is a story about compute scarcity that the AI vendor narrative has been quietly papering over for two years. The pitch decks talk about agentic this and frontier that and capability the other, while the compute reality is that even the leaders of the field cannot meet demand at the scale they have already promised, let alone the scale they are selling you for tomorrow. When the seller of the most powerful model on earth says, even at a $900 billion valuation, that it still cannot serve more than a few dozen customers without rationing, the buyer-side translation is direct: you are not behind, you are not missing out, you are being sold AI futures the vendor cannot deliver compute for. ## Foundation first, again The Mythos story is not anti-innovation, and it is not even anti-frontier. The capability is real, the breakthroughs are real, and the cybersecurity implications, both defensive and offensive, will reshape the next decade. The pace of stacking is the question, because every enterprise leader I talk to is being pressured, from above by boards and from below by ambitious teams, to be on the next thing. The Mythos story is a hard data point for pushing back, because even the people building the next thing cannot economically deliver it at the scale they are promising. The market is rationing this capability whether you want to participate or not. The right move, the move I see actually working when I look at every guest who has come on the main show, is the unsexy one. Build a measurement layer that tells you what your current AI is actually doing — that is [the SEE step](/blog/ai-visibility-audit/) from our [enterprise AI ROI playbook](/blog/enterprise-ai-roi-playbook/): full visibility before any scaling conversation. Build a governance posture that includes your third-party vendor chain, because that is exactly where Mythos itself was breached, and the same logic that flags [unauthorized shadow AI](/shadow-ai/) inside your walls applies to vendor environments outside them — [Olakai’s governance layer](/ai-governance/) exists for that reason. Build a strategy that names what AI is for in your business, not what AI is in the market, because [the MEASURE step](/blog/ai-metrics-that-matter/) tells you what to track when the board asks “is any of this paying off.” Prove value in 30 to 60 days with [a structured pilot](/blog/30-day-ai-pilot/) before any scaling commitment, so the unit economics are real numbers and not slide-deck promises. Then, when the next frontier capability becomes economically deliverable at the scale you actually need, you will be ready to stack it on a foundation that holds — and the [measurement layer](/ai-roi/) you built will tell you which capabilities are actually worth stacking. If you are running ahead of that, the question is no longer whether you are taking on too much risk; the question is whether your vendor can even serve you. This week’s news suggests, increasingly, that the answer is no. ## Coming next on Enterprise AI Unlocked I sat down last week with Jason Smith, AI Lead EMEA at Publicis Groupe, and Rob Saltrese, Co-Founder and COO of Lyra Labs, for a full Roundtable on the Mythos breach and what it tells every enterprise about foundation-first AI strategy. The White House news arrived after we hit stop on the recording, and we could not have planned a sharper data point if we had tried. The episode is now live on [Enterprise AI Unlocked](/podcast/the-mythos-reckoning/), and the full conversation goes deeper than this article on vendor-chain risk, board-level AI literacy, and what foundation-first looks like in practice. In the meantime, the math is on the table. It is not pretty, and it is telling you something important about where enterprise AI actually is, versus where the headlines say it should be. Want help building the measurement and governance foundation before you stack the next frontier capability? [Talk to an Expert](/schedule-a-demo/) about how Olakai measures AI ROI and governs risk across your stack. [How Olakai Detects AI Coding Tool Usage Without Installing a Single Agent](https://olakai.ai/blog/how-olakai-detects-ai-usage/) [The Real Bill, Not a Guess: How Olakai Reconciles Google Vertex AI Costs to BigQuery](https://olakai.ai/blog/google-vertex-ai-cost-reconciliation/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ask Kai Conversational Control Plane Source: /blog/ask-kai-conversational-control-plane [← Back to Olakai's Blog](/blog/) # Ask Kai: Inside Olakai’s Conversational Control Plane ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. July 1, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Every vendor in enterprise AI analytics now claims some version of “ask questions in plain English.” Most of what that actually means, once you look closely, is a chat window bolted onto an existing dashboard — a nicer way to ask for a chart you could already find yourself. [Kai](/kai/), Olakai’s assistant, makes a different and more testable claim: it can also take action, with the exact change surfaced for approval before anything actually happens. That’s worth proving with the real catalog of things Kai can do, not just asserting. ## One data layer, five ways to hear the answer Kai sits on top of the same underlying data as [Olakai Agentic](/coding-iq/) (Coding IQ and Agent IQ) and [Olakai Assistive](/assistive-iq/), answering questions across both in a single conversation instead of forcing a switch between dashboards. Every answer comes with transparent reasoning — the logic chain behind the conclusion, not just the number — which is a stated design principle, not an incidental feature. What makes Kai’s answers actually usable across a company, rather than just for the person who built the dashboard, is Kai Lens: a perspective you choose once per conversation that reshapes how the same underlying data gets framed. Balanced is the default, adapting depth to the question. Executive leads with bottom-line ROI and strategic recommendations and skips implementation detail. Finance & Operations leads with cost figures and budget projections, in tables built for comparison. Legal & Compliance leads with compliance status and risk exposure in audit-ready language. Technical includes configuration details and API references. Ask the same question — “How are our AI agents performing?” — through each lens and you get four genuinely different answers: an Executive framing highlights overall ROI, top performers to scale, and risks to address for a leadership briefing; Finance & Operations shows cost-per-agent and month-over-month spend trends in tables; Legal & Compliance surfaces governance compliance rates and policy gaps; Technical lists agents by execution count, failure rates, and specific configuration issues. The lens is auto-suggested from the asker’s job title — a VP of Engineering sees Executive suggested by default, a Staff Engineer sees Technical — but it’s always overridable. ## What Kai can actually do, not just answer The differentiated part of Kai isn’t the natural-language question-answering — it’s the action catalog behind it. Kai can manage users directly: “Add \[redacted\] as an Analyst,” “Make Sarah an Admin,” “Deactivate John’s account.” It can manage [Shadow AI governance](/blog/shadow-ai-app-catalog-policy-alerts/): “Approve Grammarly as officially licensed,” “Mark ChatGPT as high risk,” and even bulk actions like “Block all AI tools rated high risk that have fewer than 10 interactions” — a request that would otherwise mean clicking through a table row by row. It can draft and manage acceptable-use policies, including generating one from a named compliance framework: “Create governance policies aligned with the EU AI Act for our HR department.” And it can handle enforcement follow-through — sending a reminder to users who violated a policy, or an escalation like “Sarah’s had 3 violations this month — send an escalation to her manager.” Kai’s reach extends into [AI spend governance](/blog/inside-ai-spend-governance/) too: it can create, update, and archive Coding IQ cost-center projects and assign a service API key into one, which turns a long backlog of unassigned keys from a tedious manual triage session into a short conversation. ## Confirmation-first, not silent None of this works, from a trust standpoint, if a chat interface can quietly reassign a user’s role or block an AI tool the moment someone phrases a request slightly wrong. Kai’s write actions are ADMIN-gated and confirmation-first: every change Kai proposes gets surfaced explicitly for approval before it’s applied, not executed silently the moment the request is understood. That design choice is the actual answer to the obvious objection — “you’re letting a chatbot make changes to my governance policy?” — and it’s the reason the honest framing for Kai isn’t “an AI that runs your platform,” it’s “an AI that tells you exactly what it’s about to do, and waits.” ## Why this matters beyond convenience The pitch to a CIO or Chief AI Officer isn’t “ask questions in English” — every vendor says that now, and it doesn’t differentiate anything. It’s “ask a question in English and get an answer that comes with an offer to fix what it found, in the same conversation, gated by a permission check and a confirmation step.” That’s a materially different product than a read-only chat wrapper, and it’s the reason Kai belongs to both Olakai Agentic and Olakai Assistive rather than being siloed to one product — a governance question rarely respects the boundary between coding tools and chatbots, and neither should the assistant answering it. Kai is available immediately to any account with Coding IQ, Agent IQ, or Assistive IQ data flowing in — no separate setup required. It’s the closest thing on the platform to a [business-friendly interface](/platform/) in the literal sense: a Legal & Compliance leader and a Staff Engineer can ask the exact same underlying data the exact same question and both walk away with an answer built for them. Want to see what Kai can tell you — and do for you — with your own AI usage data? [Talk to an Expert](/schedule-a-demo/). [3 Token Cost Metrics Every CFO Should Be Watching](https://olakai.ai/blog/token-cost-metrics-cfo/) [How to Be a Smarter Token Manager: Model Routing, Explained](https://olakai.ai/blog/model-routing-explained/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Build Or Buy Self Hosting Ai Cost Source: /blog/build-or-buy-self-hosting-ai-cost [← Back to Olakai's Blog](/blog/) # Build or Buy: The Arithmetic on Running Your Own Model ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. July 28, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) From the AI ROI Series, recorded 28 July 2026. Two things happened that week, and they are more connected than they look. [Visa](https://www.visa.com) cut 2,600 jobs, about 7% of the company, and the cuts fell mostly on technology and product teams. The CEO’s memo said AI is accelerating the evolution of how work gets done, although Visa’s own people were careful to say AI was not the only reason, and I am not going to overstate it. Hold on to which teams got cut, though, because technology and product is the exact group that burns almost all of an enterprise AI budget. The second thing is that [Anthropic](https://www.anthropic.com) published a position on open-weight models after taking a beating for not signing the open letter. Somewhere between those two stories, half of LinkedIn decided the answer is to stop paying vendors and run the models yourself. So let us do what we do here, and price it. ## First, what Dario actually wrote The version going around is not the version he wrote, so this part is worth getting right before any arithmetic. He did not call for a ban. He said it plainly: “Anthropic has never advocated for banning open-weight models.” He called open models without the dangerous capabilities a public good, and the safety testing he asked for would apply to Anthropic’s own models too. He asked for three things: keep advanced chips away from authoritarian governments, stop industrial-scale distillation, which is copying frontier models through the API, and test any sufficiently capable model before release, open or closed. Is he neutral? Of course not, he sells closed models, and you should read him like an S-1. But if this were straight protectionism he would have backed the ban, because banning Chinese open models inside US companies is the single policy that most helps his revenue, and he turned it down. The sentence everyone quoted instead was the one about open models costing nothing besides the compute needed to run them. So we priced the compute. ## What actually changed in the pricing A developer on a flat subscription costs about $200 a month. No meter, no visibility, burn as much as you like. That same developer, doing identical work, billed by the token, costs $1,200 a month or more. Six times, for the same output. Nobody’s usage exploded, the visibility did, and that invoice is what sent everyone hunting for a cheaper answer in the first place. ## The arithmetic, on a composite company The company I carry through these episodes is 250 people, 40 of them developers, running about 115 billion tokens a year. The open-weight example is Kimi K3, the 2.8 trillion parameter model everybody points at. Three scenarios, with every assumption bent in favour of building. Scenario Annual cost vs buying Buy it from a vendor $620,000 baseline Build it, engineers already on payroll and reassigned, no new salaries $748,000 \~1.2x Build it, with three specialists who can run it in production $1.47M \~2.4x *Composite company, 250 people, 40 developers, 115 billion tokens a year. Recorded 28 July 2026. This is directional. Full disclosure, it is not a true business case, and you should check my math against your own numbers.* On the metal alone it is nearly competitive at 1.2 times, which honestly surprised me. Then you put the people back and the arithmetic stops working, because you will need those people whether or not you have budgeted for them. The third scenario also carries $1.58 million of hardware on day one, locked to one model, in a market where something changes every month. ## Where the $620,000 actually sits Before anyone asks whether that figure is just the developers, no, it is all 250 people. The 40 developers burn $576,000 of it. Everybody else accounts for $44,000. So developers are 93% of the spend, which brings us back to Visa, because the teams getting cut are the teams generating almost the entire AI bill. Where the money goes when you build Share People about half Metal about a third Power 3% *Averages, and my own assumptions. Recorded 28 July 2026.* Power at 3% is the number I got most wrong going in, and I expected it to be much bigger personally. I do not know about you, but if anyone is selling you self-hosting on an energy argument, they have not built one, and that holds even against an 18% year-on-year increase in US electricity prices. ## The one line to take to your board Your entire annual AI bill, all 250 people, is $620,000. The three engineers you need to run the thing yourself cost $722,000. Maybe you already have them and maybe you do not, but three engineers cost more than the whole company’s AI bill, before a single server, before power, hosting, or support. All of that assumes $1,200 a month per developer, which is the assumption most likely to be wrong for your team, so run it with your own number. Push the developer usage rate as hard as you like and the metal gets you down to about 1.1 times in my analysis. The staff number never gets there. It is worth saying that Kimi K3 is not cheap to buy either. It prices the same as Sonnet 5, and it is very capable, but cheap is the wrong word for it at the end of the day. The Wall Street Journal ran a piece the same week arguing AI pricing had peaked and that self-hosting was the era we were entering, which I found quite surprising from that masthead. ## Four reasons to build, and cost is not one of them There are four cases where building is the right call: an air gap, sovereignty, the model being your actual product, or idle hardware of this class that you already own. Those are real, and they are edge cases. Everything else is a measurement problem wearing a procurement costume. Token prices genuinely are falling, that part is true, but consumption per task is climbing faster, and the subsidised era is over. Usage-based pricing is a cash business rather than philanthropy. What has been bothering me for weeks is the speed of the round trip: a non-expert explains enterprise AI to other non-experts, and within seventy-two hours it is an urban legend that you should just deploy Kimi K3 yourself because it is free. That is a story your uncle tells you after a few beers at the family barbecue. ## What to check before you price a build If you are chasing open weights to save money without knowing your own baseline, you have nothing to compare against, and you will not save anything because you never knew what you were spending. So the check is the boring one. Do you know your current cost per developer per month, measured rather than assumed? Do you know what share of your total AI spend sits with the 40 or so people who generate most of it, which is a question about [your own engineering usage data](/coding-iq/) rather than a vendor’s console? And do you know your cost per unit of output well enough that you could tell whether a build actually beat it a year from now? Those who do not measure end up exposed, which is the same pattern behind [the AI bill crowding out other budget lines](/blog/ai-bill-eating-everything-else/) and behind most of the [tool sprawl](/blog/ai-coding-tool-sprawl/) I see in engineering organisations. A baseline is what makes the build-or-buy question answerable at all, and keeping that baseline across every tool and every token is the part I would put in place before a procurement exercise rather than after one. It is the same discipline that makes [routing](/blog/model-routing-explained/) and [AI ROI](/ai-roi/) measurable instead of anecdotal, and it is why [the metrics layer](/analytics-kpis/) comes first. One question, answerable from your last quarter without looking anything up: if you self-hosted tomorrow, what number would you compare the result against? I’m Paul, co-founder of Olakai. Measuring what AI actually costs and what it actually returns, on your own workload, is the work I spend my days on. Maybe I am completely wrong here and it works for you, in which case I would genuinely like to hear about it. [Your AI is an investment, so let’s measure it like one](/schedule-a-demo/). [Custom KPIs: The Four-Layer System Behind Olakai’s Metrics](https://olakai.ai/blog/custom-kpis-ai-measurement/) [Uber Blew Through a Year of AI Budget in Four Months. The Guardrail It Built Next Already Existed.](https://olakai.ai/blog/uber-ai-budget-blowout/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Cfo Ai Use Cases Source: /blog/cfo-ai-use-cases [← Back to Olakai's Blog](/blog/) # AI in Finance: 5 Use Cases Every CFO Should Know ![AI in finance - intelligent analytics dashboard for CFO use cases](https://olakai.ai/wp-content/uploads/2025/12/cfo-ai-use-cases-featured-1.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. December 17, 2025 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) When a Fortune 500 technology company’s finance team finally tallied the numbers, they were staggered. Their accounts payable department was processing 47,000 invoices monthly—at an average cost of $19 per invoice and a 17-day processing time. That’s nearly $900,000 annually in AP processing costs alone, not counting late payment penalties, missed early payment discounts, and the strategic opportunity cost of having skilled finance professionals manually keying data into ERP systems. Finance teams everywhere face this same paradox. CFOs are under relentless pressure to close faster, forecast more accurately, and provide real-time visibility into financial health. Yet their teams spend the majority of their time on manual work that machines could handle: invoice processing, expense reviews, reconciliations, and forecasting updates. According to the [Deloitte Q4 2025 CFO Signals Survey](https://www.deloitte.com/us/en/insights/topics/business-strategy-growth/4q-2025-cfo-signals-survey.html), 87% of CFOs believe AI will be extremely or very important to their finance department’s operations in 2026—only 2% say it won’t be important. More than half of CFOs say integrating AI agents in their finance departments will be a transformation priority this year. The shift from experimentation to enterprise-wide deployment is happening now. ## Overview: Finance AI Use Cases Use Case Typical ROI Complexity Time to Value Invoice Processing 8-12x Medium 6-10 weeks Expense Review 6-10x Low 4-6 weeks Cash Flow Forecasting 10-15x Medium 8-12 weeks Accounts Receivable 8-12x Medium 6-10 weeks Financial Close 6-10x Medium-High 10-14 weeks ## 1\. Invoice Processing: From Manual to Touchless Manual invoice processing is one of the most expensive routine operations in finance. According to [HighRadius research](https://www.highradius.com/finsider/ap-automation-2025-stats-for-cfos/), the average cost to process an invoice manually ranges from $12.88 to $19.83 per invoice, with processing times stretching to 17.4 days for organizations without automation. Best-in-class AP departments using AI-powered automation spend just $2-3 per invoice—an 80% reduction—with processing times of 3.1 days. The numbers get more compelling at scale. A single AP employee can handle more than 23,000 invoices annually with automation, compared to just 6,000 with manual processing. That’s nearly a 4x productivity improvement per person. The global accounts payable automation market is projected to reach $1.75 billion by 2026, reflecting how rapidly finance organizations are moving to eliminate manual invoice handling. An AI agent transforms invoice processing by extracting data from invoices regardless of format—vendor, amount, date, line items—then validating against purchase order data and contracts. It routes for appropriate approvals based on amount and category, flags anomalies and potential fraud, and processes straight-through when validation passes. At maturity, organizations achieve 60-75% touchless processing rates, where invoices flow from receipt to payment without human intervention. Key metrics to track include data extraction accuracy (target: 95-98% for structured invoices), touchless processing rate, exception rate, cost per invoice, and fraud detection rate. Most organizations see payback within 6-12 months. ## 2\. Expense Review: Policy Enforcement at Scale Manual expense review is tedious, inconsistent, and often delayed. Finance teams spend hours on low-value approval work while policy violations slip through. The inconsistency is particularly problematic: one manager approves expenses that another would reject, creating frustration and compliance gaps. An AI expense agent reviews submissions against company policies in real-time, flags violations (missing receipts, over-limit spending, wrong categories), and auto-approves compliant expenses within predefined thresholds. It routes exceptions for human review with full context and identifies patterns that suggest policy abuse—like employees consistently submitting expenses just below approval thresholds or splitting single expenses across multiple submissions. The impact extends beyond efficiency. Organizations report 80% reduction in manual review time, consistent policy enforcement across the organization, faster reimbursement for employees, and 6-10x ROI through efficiency and compliance improvements. The consistency alone can reduce employee complaints and improve satisfaction with the expense process. ## 3\. Cash Flow Forecasting: See What’s Coming Cash flow forecasting is where AI moves from cost reduction to strategic value creation. Traditional forecasting is manual, time-consuming, and often wildly inaccurate—relying on historical averages and gut instinct when what finance leaders need is predictive insight. An AI forecasting agent analyzes historical payment patterns, incorporates seasonality and trends, and predicts customer payment timing based on actual behavior—not optimistic assumptions. It models different scenarios (best case, worst case, expected) and updates forecasts continuously as new data arrives. For a deeper framework on measuring AI-driven improvements, see our guide on [how to measure AI ROI in the enterprise](/blog/ai-roi-framework/). The business impact is substantial: 25-35% improvement in forecast accuracy, earlier visibility into cash crunches, better working capital management, and 10-15x ROI through avoided borrowing costs and optimized investment timing. When you can predict cash positions weeks in advance rather than days, treasury operations transform from reactive crisis management to proactive optimization. ## 4\. Accounts Receivable: Collect Faster, Chase Smarter Collections are often reactive—chasing payments after they’re overdue. This hurts cash flow and strains customer relationships. Nobody enjoys making or receiving collection calls, and the awkwardness often leads finance teams to delay or avoid necessary follow-ups. An AI collections agent predicts payment likelihood based on customer behavior and history. It sends proactive reminders before due dates—when customers can still pay easily—rather than after-the-fact demands. It personalizes collection approaches based on customer segment and relationship, prioritizes collection efforts by likelihood and amount, and tracks payment commitments and follows up automatically when they’re missed. Organizations report 10-20 day reduction in DSO (Days Sales Outstanding), 15-25% reduction in bad debt write-offs, fewer uncomfortable collection conversations, and 8-12x ROI through improved cash flow. The relationship preservation matters as much as the cash: customers appreciate respectful reminders more than aggressive collection efforts. ## 5\. Financial Close: Faster, More Accurate Month-end close is a fire drill at most organizations. Reconciliations, adjustments, and reviews pile up. Teams work overtime, errors slip through, and the process takes 5-10 days that could be spent on analysis and planning. CFOs know that every day spent on close is a day not spent on forward-looking work. An AI close agent automates bank reconciliation—the tedious matching of transactions that consumes hours of staff time. It identifies and investigates discrepancies, prepares standard journal entries, flags unusual items for review, and tracks close tasks and deadlines. The system learns which discrepancies resolve themselves versus which require investigation, reducing noise over time. The impact includes 30-50% reduction in close time, fewer errors and restatements, more time for analysis and strategic work, and 6-10x ROI through efficiency and accuracy. Some organizations have compressed their close from 10 days to 4, freeing their teams to focus on variance analysis and forward planning rather than data reconciliation. ## Governance Considerations for Finance AI Finance AI requires careful governance given the sensitivity of financial data and the regulatory requirements surrounding financial reporting. This isn’t optional—it’s table stakes for any AI deployment in finance. SOX compliance demands audit trails for all AI-touched transactions. Every automated decision needs to be traceable, explainable, and reviewable. Segregation of duties must be maintained: AI shouldn’t both approve and execute payments, just as no single human should. Data retention requirements for financial records apply equally to AI-generated data. Build your control framework with immutable logging where every AI decision is recorded and cannot be altered. Establish clear exception handling with escalation paths for anomalies. Set threshold controls on what AI can process without human review—start conservative and expand as trust is established. Conduct regular audits to verify AI is performing as expected and catching what it should catch. Fraud detection deserves particular attention. Monitor for duplicate payments, flag unusual vendor patterns (new vendors with large invoices, vendors with addresses matching employee addresses), detect invoice anomalies, and track user behavior changes. AI can catch patterns that humans miss when processing thousands of transactions. ## Getting Started If you’re ready to bring AI to your finance organization, start with invoice processing. It’s high-volume, well-defined, and delivers clear ROI. Most organizations see payback within 6-12 months, and the use case is mature enough that vendors have proven solutions. Build governance from day one. Finance data is sensitive and regulated. Establish audit trails, controls, and compliance documentation before production—not after an auditor asks for them. The [Future of Agentic use case library](https://futureofagentic.com/use-cases/) includes detailed finance automation scenarios with governance frameworks. Define success metrics upfront. Track cost per transaction, accuracy rates, processing time, and exception rates. Without measurement, you can’t prove value—and according to Deloitte, only 21% of active AI users say the technology has delivered clear, measurable value. Be in that 21%. Plan for exceptions. AI won’t handle 100% of cases. Design clear escalation paths for edge cases and train staff on when to intervene. The goal is appropriate automation, not total automation. ## The Finance Transformation The CFO role is evolving from scorekeeper to strategic partner. [AI-powered automation](/industries/financial-services/) handles the routine work, freeing finance teams to focus on analysis, planning, and decision support. According to [Fortune’s CFO survey](https://fortune.com/2025/12/24/ai-in-2026-cfos-predict-transformation-not-just-efficiency-gains/), finance chiefs broadly expect AI to shift from experimentation to proven, enterprise-wide impact in 2026—transforming the finance function rather than just trimming costs. The numbers bear this out: 50% of North American CFOs say digital transformation of finance is their top priority for 2026, and nearly two-thirds plan to add more technical skills—AI, automation, data analysis—to their teams. Automating processes to free employees for higher-value work is the leading finance talent priority, cited by 49% of CFOs. The finance organizations that embrace AI will operate faster, more accurately, and with better visibility. Those that don’t will struggle to keep up with the pace of business—and increasingly, with their competitors who’ve made the leap. *Ready to transform your finance operations? [Talk to an expert](/schedule-a-demo/) to see how Olakai helps you measure the impact of finance AI and govern it responsibly.* [AI Risk Heatmap: Matching Governance to Business Value](https://olakai.ai/blog/ai-risk-heatmap/) [How AI Agents Are Revolutionizing Cybersecurity](https://olakai.ai/blog/ai-cybersecurity-agents/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Ciso Governance Checklist Source: /blog/ciso-governance-checklist [← Back to Olakai's Blog](/blog/) # AI Governance Checklist for CISOs ![Security team discussing AI governance in corporate boardroom](https://olakai.ai/wp-content/uploads/2025/11/ciso-governance-checklist-photo.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. November 14, 2025 · [AI Governance](https://olakai.ai/blog/category/ai-governance/) AI is no longer an IT experiment—it’s an enterprise reality. Your employees are using AI tools (sanctioned or not), your vendors are embedding AI into their products, and your board is asking about AI strategy. For CISOs, this creates a challenge with no easy answers: How do you govern AI without blocking innovation? How do you protect data without slowing business? How do you maintain compliance when the technology moves faster than regulations? The stakes are high. According to the [2025 CSA AI Security Report](https://www.trustcloud.ai/the-cisos-guide-to-ai-governance/), only about a quarter of organizations have comprehensive AI security governance in place—the remainder rely on partial guidelines or policies still under development. Meanwhile, 100% of organizations plan to incorporate generative AI, and Gartner predicts over 100 million employees will interact with AI by 2026. The gap between AI adoption and AI governance represents real risk. This checklist provides a structured framework for evaluating and improving your organization’s [AI governance maturity](/ai-governance/). ## How to Use This Checklist For each question, score your organization from 0 (not in place—no capability or process exists), to 1 (partial—some capability exists but gaps remain), to 2 (mature—fully implemented and operational). Add scores within each category to identify strengths and weaknesses. ## Category 1: Visibility *Can you see what AI is doing in your organization?* **Audit and Logging:** Can we audit every agent decision? Do we have centralized logging for all AI interactions, including inputs, outputs, and decisions made? The ability to answer “what did this system do and why” is foundational to everything else in governance. **Complete inventory:** Do we have a complete inventory of all AI agents and tools in use—including [shadow AI](/blog/shadow-ai-risk/) that employees may be using without approval? According to research, 78% of CISOs believe AI is affecting cybersecurity, but 45% admit they’re still not ready to address the problem. You can’t govern what you can’t see. **Data lineage:** Can we trace data lineage for any agent interaction? Do we know what data sources each agent accessed and what data it produced? This becomes critical during incidents and audits. **Sensitive data access:** Do we know which agents access sensitive data sources? Is there a registry mapping agents to the data they can access? Sensitive data exposure ranks as the leading AI security concern among survey respondents. **Shadow AI detection:** Can we detect shadow AI usage—unapproved tools that employees are using? Do we monitor for this actively? Given that most organizations lack formal AI risk management programs, shadow AI often operates completely below radar. **Category 1 Score: \_\_\_ / 10** ## Category 2: Control *Can you control what AI does and who can change it?* **Deployment authority:** Who can deploy agents? Who can change their prompts? Is there clear ownership and authorization for AI deployments? Without clear authority, agents proliferate without oversight. **Role-based access:** Do we have role-based access control (RBAC) for agent capabilities? Can we limit what different agents can do based on sensitivity? Not every agent needs access to every system. **Approval workflows:** Is there an approval process for new agents entering production? Do security, legal, and compliance review before deployment? The [SANS report](https://swimlane.com/blog/ciso-guide-ai-security-impact-sans-report/) highlights a concerning lack of security team involvement in governing GenAI—many believe they should play a role but few organizations have formal processes. **Policy enforcement:** Can we enforce policies programmatically—not just through guidelines? Are guardrails built into the infrastructure? Policies that rely solely on human compliance will fail. **Security testing:** Do we test agents for security vulnerabilities before deployment? Do we check for prompt injection, jailbreaking, and data leakage risks? According to research, 62% of AI-generated code is either incorrect or contains a security vulnerability. **Category 2 Score: \_\_\_ / 10** ## Category 3: Data *Is sensitive data protected when AI accesses it?* **Data source mapping:** Which data sources can each agent access? Is there a clear registry of permissions and restrictions? Data access should be explicit, not assumed. **PII protection:** Do we have PII detection and masking in place? Can we prevent agents from exposing personally identifiable information? This is table stakes for any customer-facing AI. **Regulatory compliance:** Are we compliant with GDPR, CCPA, and other data regulations for AI-processed data? Have we verified this with legal? As of mid-2025, state legislatures had introduced some 260 AI-related bills during the 2025 legislative sessions—the regulatory landscape is rapidly evolving. **Data retention:** Do we have data retention policies for agent interactions? Do we know how long logs are kept and when they’re deleted? Compliance requirements vary by jurisdiction and data type. **Right to deletion:** Can we fully delete user data on request (right to be forgotten)? Does this include data in AI training sets and logs? This is a legal requirement in many jurisdictions and technically complex to implement. **Category 3 Score: \_\_\_ / 10** ## Category 4: Incident Response *Can you respond when something goes wrong?* **Rollback capability:** How do we roll back a rogue or compromised agent? Can we quickly revert to a previous version or disable an agent entirely? The faster you can respond, the smaller the impact. **Incident runbooks:** Do we have runbooks for common AI incidents—data leaks, hallucinations, prompt injection attacks, model compromise? AI introduces failure modes that traditional security runbooks don’t cover. **Kill switch:** Can we disable an agent in less than 5 minutes? Is this tested regularly? When an agent is causing harm, every minute matters. **On-call ownership:** Who is on-call for AI security incidents? Is there a clear escalation path and 24/7 coverage? AI systems don’t fail during business hours only. **Post-mortems:** Do we conduct post-mortems and share learnings after AI incidents? Is there a continuous improvement process? Learning from incidents prevents repetition. **Category 4 Score: \_\_\_ / 10** ## Category 5: Compliance and Audit *Can you prove compliance to auditors and regulators?* **Audit readiness:** Can we pass an AI audit today? If regulators asked to see our AI governance, could we demonstrate compliance? The [CSA AI Controls Matrix](https://cloudsecurityalliance.org/blog/2025/08/08/strategic-implementation-of-the-csa-ai-controls-matrix-a-ciso-s-guide-to-trustworthy-ai-governance) provides 243 control objectives across 18 security domains—a useful benchmark. **Immutable logs:** Do we have immutable logs for sensitive operations? Can we prove logs haven’t been tampered with? Immutability is critical for legal and regulatory purposes. **Policy documentation:** Are AI governance policies documented and communicated? Do employees know what’s expected? Documentation is the foundation of demonstrable compliance. **Compliance metrics:** Do we measure and report Governance Compliance Rate? Can we show the percentage of AI interactions that comply with policies? Metrics make governance tangible. **Board visibility:** Is AI governance represented at the board level? Do executives understand AI risk exposure? AI risk is business risk and belongs in board conversations. **Category 5 Score: \_\_\_ / 10** ## Scoring Interpretation Total Score Maturity Level Recommended Action 0-10 Foundational Start with visibility: establish inventory and basic logging before adding controls 11-25 Developing Fill critical gaps: prioritize based on risk—data protection and incident response are typically highest priority 26-40 Established Optimize and scale: strengthen existing capabilities and prepare for audit 41-50 Advanced Lead: share practices, influence industry standards, and continue innovation ## Priority Actions by Risk Level **If you’re processing customer PII:** Prioritize PII detection and masking, comprehensive logging, RBAC, right to deletion capability, and regulatory compliance verification. Data protection failures have immediate regulatory and reputational consequences. **If you’re in a regulated industry:** Prioritize immutable audit logs, policy documentation, compliance metrics, approval workflows, and audit readiness. Key compliance pathways include mappings to the EU AI Act, NIST AI 600-1, ISO 42001, and BSI AIC4 Catalogue. **If you’re scaling AI rapidly:** Prioritize complete inventory, shadow AI detection, programmatic policy enforcement, kill switch capability, and incident runbooks. Speed without governance creates technical and compliance debt. **If you’re just starting:** Prioritize basic logging, agent inventory, clear ownership, simple approval process, and documentation. Foundation comes before sophistication. ## The AI Risk Heatmap Not all AI use cases carry equal risk. Prioritize governance based on both business value and risk sensitivity—a concept we explore in depth in our [AI risk heatmap framework](/blog/ai-risk-heatmap/). **High Value, High Risk (Govern Tightly):** Customer support agents with PII access, financial data analysis agents, contract review and drafting, and HR policy chatbots need RBAC, PII protection, comprehensive logging, human-in-the-loop review, and regular audits. **High Value, Medium Risk (Govern Moderately):** Code assistants and copilots, sales research assistants, and AI meeting note takers need zero data retention agreements, code review requirements, consent mechanisms, and approved vendor lists. **Medium Value, Low Risk (Govern Lightly):** Internal knowledge assistants and content drafting tools need basic logging, user feedback loops, and source citation requirements. ## Getting Started If you scored below 25, focus on these immediate actions. First, conduct an AI inventory. Survey departments, review expense reports, analyze network traffic. You can’t govern what you can’t see, and the gap between what security teams believe is deployed and what’s actually in use is often substantial. Second, establish basic logging. Ensure all production AI agents have logging enabled. Centralize logs where possible. This creates the audit trail everything else depends on. Third, define ownership. Assign clear owners for AI governance. Create an AI governance committee if needed. Without ownership, governance becomes everyone’s problem and no one’s priority. Fourth, document policies. Write down acceptable use guidelines. Communicate them to all employees. Documentation transforms implicit expectations into enforceable standards. Fifth, plan for incidents. Create basic runbooks for data leaks, hallucinations, and unauthorized access. Incident response planned in advance is dramatically more effective than improvisation under pressure. For measuring the business impact of your governance investments, see our [AI ROI measurement framework](/blog/ai-roi-framework/). ## The Bottom Line AI governance isn’t about blocking innovation—it’s about enabling it responsibly. The organizations that build strong governance foundations now will scale AI with confidence, while others will hit walls of compliance violations, security incidents, and audit failures. This checklist is a starting point. The goal isn’t perfection; it’s continuous improvement toward a governance posture that matches your AI ambitions. The [Future of Agentic guide to agent characteristics](https://futureofagentic.com/agentic-ai-101/characteristics/) provides additional context on what makes AI systems increasingly autonomous—and why governance becomes more critical as autonomy increases. *Ready to improve your AI governance maturity? [Talk to an expert](/schedule-a-demo/) to see how Olakai provides the visibility, controls, and compliance tools CISOs need.* [How to Measure AI ROI: A Framework for Enterprise Leaders](https://olakai.ai/blog/ai-roi-framework/) [From AI Experimentation to Business Impact](https://olakai.ai/blog/ai-experimentation-impact/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Claudeforce Ai System Of Record Source: /blog/claudeforce-ai-system-of-record [← Back to Olakai's Blog](/blog/) # ClaudeForce, and the One AI Input You Cannot Buy ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. August 31, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) From Enterprise AI Weekly, recorded 28 August 2026. Three things happened in enterprise AI that week, and read together they settle an argument the industry has been having for about two years. [Nvidia](https://www.nvidia.com) reported $96.2 billion of revenue for the quarter, up 106% year on year, guided to 70% growth for next year, and said it is still supply-constrained. That last part is the interesting one. A company can sell $96.2B of anything in ninety days and still tell the market it cannot build fast enough, which puts the constraint somewhere physical, in fabs and power and memory, rather than anywhere a purchase order can reach. Almost all of the coverage took these as three separate stories, filed to three separate desks: a semiconductor story, a CRM story, and a valuation story. I look at them from where I sit, which is with the people who have to put a number in a 2027 AI budget and then defend it, and from that seat they are quite obviously one. ## Capability stopped being the interesting variable Before the arithmetic, the mechanism, because most people still carry the wrong mental model here. The assumption underneath a great deal of enterprise AI procurement is that the frontier models are separated by large capability gaps, and that paying more buys you proportionally more machine. On the current numbers that assumption no longer holds. Ten frontier models now sit about 16 points apart on capability and are priced about 12 times apart, according to the Artificial Analysis leaderboard. Put those two spreads next to each other and the shape of the decision changes. A 12x price range across a 16 point capability range means the premium at the top of the menu is buying a modest amount of measurable capability at a very immodest multiple, and it means the model you pick matters far less to your outcome than the discipline with which you route work to it. I have written the arithmetic on that routing decision [at length before](/blog/model-routing-explained/), and none of the week’s news changes it. If anything the convergence makes the case stronger, because the cheaper end of the ladder keeps closing the gap while the expensive end keeps its price. ## Compute is being funded by the trillion The second variable is the one being solved with capital rather than with cleverness. Nvidia cannot build fast enough at $96.2B a quarter, and [Anthropic](https://www.anthropic.com) is reportedly targeting a $2 trillion IPO, per the Financial Times. Whatever you think of that number, and I have [written separately about what that kind of raise signals to buyers](/blog/anthropic-mythos-compute-trap/), the direction of travel is not ambiguous. The compute layer is being financed at a scale that no individual enterprise participates in and no individual enterprise needs to, because you rent it by the token. It is a commodity with a public rate card, and what you buy when you sign an AI contract is a place in a queue somebody else is paying to lengthen on your behalf. ## Then Salesforce put Claude on your CRM data The third story is the one that ties the other two off. [Salesforce](https://www.salesforce.com) announced ClaudeForce, putting Claude directly onto customer CRM data, and the market liked it: the stock rose about 22% on the day and about 40% across recent weeks, on a strong quarter and the Anthropic partnership, per The Motley Fool. The line I would keep from Salesforce’s own announcement is this one, because it is a vendor conceding something vendors do not usually concede: > Probabilistic intelligence alone doesn’t run a company. The reasoning has to be fused with trusted data, with workflows, and with governance. Which is to say that the model on its own, however capable, is an input rather than an outcome, and the thing that turns it into an outcome is the material you point it at and the controls you wrap around it. ## The one input with no market Here is the comparison that reorganised how I think about 2027 budgets. Capability is converging and is available to your competitors on the same terms it is available to you. Compute is a commodity, priced publicly, and financed by people with more capital than any of us. Your own data, and the record of what your own AI has actually done with it, has no market at all. Nobody sells it, nobody can lend it to you, and there is no procurement cycle that gets you it faster. You either have it or you spend the next two years building it. That asymmetry is the whole reason measurement stopped being a reporting exercise and became an asset question. If two companies rent the same models at the same prices, the difference between them is what each can see about its own usage, its own outcomes, and its own costs. This is the missing piece, and it is a [system of record for enterprise AI](/blog/what-is-an-ai-system-of-record/) rather than [another dashboard](/platform/). A dashboard shows you a view of something. A record is the thing itself, kept over time, in one data model, across coding tools, assistants, and [autonomous agents](/agent-iq/) alike. ## What to check before you write the 2027 number Directional as always, and check my math. But if the three stories above are one story, then a few things follow for anyone building next year’s budget, and they are checks rather than recommendations. Can you say, without a project, which models your organisation is currently paying for and what each one produced? Can you attribute AI spend to a team, a workload, and an outcome, rather than to a vendor invoice? If the capability gap between the cheapest capable model and your default model is genuinely 16 points, do you know what your own workload loses by moving down the ladder, or are you paying the 12x premium as insurance against an uncertainty you have never measured? Those are answerable from your last quarter, and the answers are usually more uncomfortable than the headlines are. Most organisations find they are carrying the premium and cannot say what it bought, which is the same thing I keep running into when I look at [AI ROI](/ai-roi/) properly, and it is why [the measurement layer](/analytics-kpis/) is the part I would fund first. So the question I would put to you, and you can answer it from what you already know: if every model your competitors use is available to you at the same price tomorrow morning, what is left that is actually yours? I’m Paul, co-founder of Olakai. Measuring what AI actually costs and what it actually returns, on your own workload, is the work I spend my days on. Tell me if you see it differently, and if you would rather see it than argue about it, [your AI is an investment, so let’s measure it like one](/schedule-a-demo/). [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Companies Cutting Jobs To Pay For Ai Source: /blog/companies-cutting-jobs-to-pay-for-ai [← Back to Olakai's Blog](/blog/) # Companies Are Cutting Jobs to Pay for AI. Can They Prove It’s Working? ![Abstract visualization of a declining organizational chart dissolving into a rising investment graph, representing corporate budget tradeoffs between layoffs and AI capital expenditure](https://olakai.ai/wp-content/uploads/2026/07/post2-microsoft-layoffs.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. July 8, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) The jobs apocalypse arrived, and it is being funded by payroll. If a company is trading people for an AI bet, the bar to prove that bet is working just became the highest it has ever been. On Monday, [Microsoft](https://www.microsoft.com) cut about 4,800 jobs, roughly 2.1% of its workforce, with its Xbox division heading toward 3,200 cuts, a fifth of that organization, on top of more than 15,000 the year before. Across the tech sector, more than 123,000 jobs have been cut in 2026 so far, up 66% from the prior year, and for three months running, outplacement firms have named AI as the leading driver. Now hold that next to the other number. Microsoft is projecting around $190 billion in capital expenditure this year, more than $100 billion of it on AI and cloud, two-thirds of that on AI chips. Across Big Tech, AI outlays are set to top $700 billion in 2026. This is the same pattern we flagged in April when Meta paired a $53 billion capex increase with 14,000 job cuts and the market barely blinked, a story we broke down in [Meta’s AI capex bet versus the market’s muted reaction](/blog/meta-layoffs-ai-capex-math/). Microsoft’s version of the trade is bigger, and it is not an isolated data point. It is the pattern becoming the norm. ## Why this is scarier than replacement If AI were simply doing the work better, the math would be clean. It is not clean. It is a bet, an enormous one, and the market has started asking whether it pays. Microsoft’s stock fell 23% in the first half of 2026, wiping out roughly $1.2 trillion in value, as investors openly questioned whether an AI outlay of that scale will return in proportion to its size. The largest, most sophisticated software company on earth is being punished for spending it cannot yet prove. These jobs are not being replaced by AI that got too good. They are being traded to fund the bet that it eventually will. An analyst quoted by [D.A. Davidson](https://www.dadavidson.com) put the quiet part out loud: Microsoft has been managing down its workforce in order to pay for its AI investments. That is the story of this moment in one sentence. And the timing could not be more brutal. At the exact moment the pressure to show returns is highest, roughly one in five leaders admit the AI reports reaching them are rosier than reality: bad news softened, failures kept quiet. That gap between the story leadership hears and the truth on the ground is where the next round of cuts gets justified on numbers that were never real. It is exactly the blind spot we described in [the AI visibility audit](/blog/ai-visibility-audit/) — you cannot govern what you cannot see, and a rosy dashboard is worse than no dashboard at all. ## The accountability bar went up, not down For anyone holding an AI budget, the implication is direct. If a company is trading headcount for an AI bet, the bar to prove that AI is actually delivering is not lower now. It is the highest it has ever been. That is owed to the people whose roles paid for it, and to the ones still there, watching. Proving it is a discipline, not a slogan, and none of the five pieces are exotic: - Tie every AI dollar to an outcome, not activity. “We deployed it” is not a result. “It shipped this, saved this, earned this” is. - Measure value per dollar, per team, per workflow. Know what is actually delivering and what is theater, by name. - See it across every vendor in one place. A bet spread over four tools nobody can total is a bet nobody can evaluate. - Stress-test the trade. If this AI does not deliver what the business case promised, what got given up to fund it, and what is the plan. - Report the truth, not the rosy version. The optimistic dashboard is the thing that eventually mugs a leadership team, and it takes people down with it. ## Why this matters most in the CFO’s office For a CFO signing off on the next AI budget line, “we think it’s working” stopped being an acceptable answer the moment a real person’s job became the funding source. The CFO now needs the same rigor applied to AI spend that gets applied to any other capital allocation decision: outcome per dollar, by workflow, reconciled against what was promised in the original business case. That is the exact gap we mapped in [AI metrics that matter to CFOs](/blog/ai-metrics-that-matter/), and it is a bigger gap than most finance teams realize until the layoffs start. The giants are learning the hard way that scale without proof gets punished. If Microsoft can lose more than a trillion dollars of market value on an AI bet its own investors cannot verify, a company betting its payroll on the same faith, at a fraction of Microsoft’s balance sheet, is playing with fire. ## The move None of this is an argument against AI investment. It is an argument for measuring it, because the stakes stopped being just budget. When the funding for a company’s AI ambitions comes out of people’s jobs, “we think it’s working” is not good enough. Measurement is the difference between a strategy a leadership team can defend and a gamble it will eventually answer for — which is exactly why Olakai exists as the vendor-neutral layer that ties AI spend to proven outcomes, across every tool, before the next round of cuts gets greenlit on a story nobody checked. One question worth sitting with: if your company cut a single role to fund AI this year, can you prove the AI returned more than that role did? [**Talk to an Expert →**](/schedule-a-demo/) [How to Be a Smarter Token Manager: Model Routing, Explained](https://olakai.ai/blog/model-routing-explained/) [Your Engineering Team Uses 3+ AI Coding Tools. What You’re Missing.](https://olakai.ai/blog/ai-coding-tool-sprawl/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Cost To Serve A Token Source: /blog/cost-to-serve-a-token [← Back to Olakai's Blog](/blog/) # The Cost to Serve a Token ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. September 9, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) From the AI ROI Series, recorded 9 September 2026. Two stories broke this week that most people filed under separate headlines: OpenAI’s Astra, and [Anthropic](https://www.anthropic.com) walking away from a six billion dollar acquisition. I want to make the case that they are the same story, and that the story is about the one number that decides who wins in enterprise AI. Anthropic was reportedly ready to pay up to $6 billion for Decart, a lab whose optimisation engine runs agents at about eight times the industry average. Then, after full due diligence and right before its IPO, it reportedly walked. Both halves of that are reported rather than confirmed, so hold them accordingly. ## The number underneath both headlines The mechanism before the arithmetic, because the phrase doing the work here is one most buyers never see on an invoice. Cost to serve is what it costs a vendor to answer your request, the compute burned turning your tokens into their output. It sits underneath the rate card you are quoted, and it separates a model business that compounds from one that merely grows. An engine running agents at eight times the throughput moves that number directly, which is why it is worth billions to somebody who serves inference for a living. ## Anthropic’s own cost to serve, halved in a year A year ago Anthropic’s margin on serving inference was around 38%. Today it is around 70%. In plain dollars, running the AI used to eat about 62 cents of every revenue dollar and now it eats about 30, which is the same fact said twice rather than two separate findings. They cut their own cost to serve nearly in half in a single year. These are reported figures, so hold them loosely, and note what they cover: this is the margin on serving inference, before training, research, and staff, and it is not net profit. That qualifier matters if you read [what I wrote about Anthropic’s valuation](/blog/revenue-forecast-ai-budget/) a fortnight ago, where the figure in play was a company-wide gross margin of around 44%. The two are different measures and they sit comfortably together, since gross margin carries a great deal of cost that serving a token does not. The direction of travel is the interesting part. My argument then was that every enterprise pushing work down the price curve takes a point of the vendor’s margin. Here is the same vendor defending that margin from the other side by making each token cheaper to serve, one variable squeezed from both ends. ## Why they walked, three honest reads So you can see why an engine that gives you eight times the throughput is worth billions, and you can also see why they might still walk. There are three honest reads, and they do not contradict each other. 1. They already proved they can cut the cost themselves, so why pay $6 billion for more of a lever they are good at pulling? 2. The engine comes bundled with a whole video business, which is not their strategy, and you cannot cleanly buy just the meter. 3. Walking away from your biggest deal ever, in the week the market started grading on return rather than spend, is a discipline signal, and that is good finance management. Pick whichever you like. What survives all three is the reason Astra and Decart are the same story. The model on your desk will keep flipping, OpenAI this quarter, Anthropic the next, somebody else after that. But whoever is winning the capability race, every one of them is measuring its cost to serve down to the cent, and one of them ran $6 billion of diligence to move it. The capability race is loud. The economics race is silent, and it is permanent. ## Now turn it around, because you live on the other side You are going to switch model this year based on who is best, and most enterprises will. But can you tell me what any of them actually cost you per outcome, per task? I am not asking about invoice totals. What did one completed task, one shipped feature, one resolved ticket cost you in AI, and what did it give back? For almost everyone I talk to, the answer is no. And it is not because the teams are not sharp. It is because that data was never captured as a record in the first place. It sits scattered across four vendor consoles that do not talk to each other and were not built to tell you very much, so you cannot even see where to spend less, and none of them know what that token was actually for. This is the same gap that lets [a falling rate card sit next to a rising bill](/blog/your-ai-got-cheaper-your-bill-didnt/), and the same one that made [a vendor’s pricing change land under an agent budget](/blog/the-cache-tax/) without anyone noticing for a week. ## Same tokens, two lenses, and they almost never meet The dilemma is the same one whichever chair you sit in, and it splits cleanly down the middle of most organisations. If you are in [finance](/use-cases/cfo/), you watch the invoice climb and you cannot say whether that is a problem or a good investment. If you are in [engineering](/use-cases/vp-engineering/), you watch the workload climb and you cannot put a dollar on it. It all runs on the same tokens. They are completely different lenses on one number, and the two almost never meet in one place, which is how you end up with two teams arguing from two screens and two spreadsheets. I do see a shift here that I like a great deal, with people building their own dashboards and their own small solutions, and some of it is genuinely good work. There is always a story about somebody saving 20 minutes on a task. It is real, and it might even be statistically significant, but a pointy saved minute is a long way from a firm-level answer, let alone a board-level one, especially once you are past the pilot and into scale. Put the two sides together and the asymmetry is the whole point. The seller measures every token to the cent, across every model, and will spend $6 billion of diligence to move the number by a few points. The buyer measures a good afternoon on whichever model is fashionable this quarter. That gap is the reason Olakai exists. We capture every AI interaction and every outcome across an organisation, down to the token, structure it into one attributed record, and make it usable through your own AI, so you can see [cost per completed task and value per outcome](/analytics-kpis/) by team, by agent, by model, and by vendor. It is one record read through whichever lens is yours: finance reads it as return and budget, engineering reads it as throughput and the cost of what shipped. Data is the product. The record is the instrument. ## FY27 budget season, and the questions that matter The model on your desk is going to keep changing. The one thing that should not change is your ability to measure what it is worth. We are all getting the same question this year whether we like it or not, which is what did it return, and most of us cannot answer it cleanly yet. That is a measurement gap rather than a failing on anyone’s part, and measurement gaps are fixable. So these are the table stakes questions for this year, and they are for anyone who owns a piece of the AI budget, which means finance, engineering, and AI leaders alike. 1. When you switch to Astra, or to whatever comes next, will you know whether it costs you more or less per outcome than the model it replaced? 2. Did it make your agents better, and can you show the difference? 3. If you are in finance, could you put that number in front of the board on Monday with the evidence behind it? 4. If you are in engineering, could you show which agents and which models earned their cost, and which did not? If any of those is a no, that is the work. Directional as always. The data is public, so check my math and tell me if you see it differently, because I welcome that all day long. I’m Paul, co-founder of Olakai. [Your AI is an investment, so let’s measure it like one](/schedule-a-demo/). [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Cursor Cfo Council Source: /blog/cursor-cfo-council [← Back to Olakai's Blog](/blog/) # The CFO Just Walked Into the Coding Room ![Abstract visualization of finance meeting software engineering with bar-chart and code motifs](https://olakai.ai/wp-content/uploads/2026/07/cursor-cfo-council.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. July 10, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) [Cursor](https://cursor.com) built a CFO council this week, and quietly proved a thesis Olakai has been on for months. The quick version of the week first: Microsoft cut another wave of jobs largely to fund its AI bet, continuing the pattern we broke down in [why the accountability bar for AI spend just went up](/blog/companies-cutting-jobs-to-pay-for-ai/). The big labs kept softening their 2025 predictions about half of knowledge-worker jobs vanishing. xAI shipped Grok 4.5, OpenAI pushed a new ChatGPT release after a federal review, and — the one that drew a smile around here — Elon Musk publicly called Anthropic “obviously the leader in AI right now,” adding “I was clearly wrong,” a rival conceding the lead in writing days after launching his own model. But none of that is the story worth unpacking today. ## Cursor built a CFO council This week Cursor, the AI coding tool, launched a CFO Council, a working group for chief financial officers, and published a stack of data alongside it. Sit with that for a second: a coding tool built a forum for finance leaders. Here is why it matters. Cursor, more than almost any other AI coding vendor, turned coding into a metered cost. It was early and aggressive on usage-based pricing — its top consumer tier taps out around forty dollars a month, and past that, users are into custom, usage-based territory fast, even as individuals. Cursor made tokens a line item, and now it is walking straight into the CFO’s office to help make sense of the bill it helped create. That is the point we have been making for months: the responsibility for winning at AI, the tokens, the consumption, the return, has moved into the CFO’s office. Coding used to be the CTO’s world, engineering’s world, and finance stayed out of it. Not anymore. Cursor just pulled the CFO directly into the coding room, in public, because somebody has to answer for the spend — a shift we mapped out in detail for [what CFOs need from AI ROI reporting](/use-cases/cfo/). ## The data is fascinating, and a warning Cursor’s headline number, published on its [CFO Council blog](https://cursor.com/blog/cfo-council): companies in the top quintile of token usage saw 16.5% year-over-year revenue growth, versus 5.1% for the bottom quintile. Use more tokens, grow faster — and AI genuinely does create real value. But that is a correlation, seen from thirty thousand feet. High token usage lining up with high revenue growth does not mean the tokens caused the growth. A thousand other things drive a company’s top line. What is missing in between is measurement: the step-by-step evidence that connects a token to an outcome. Cursor’s own [Developer Habits Report](https://cursor.com/insights) makes the case just as clearly from the other direction. The top 1% of users generate 46 times more AI-assisted code per day than the median developer. Value is wildly concentrated, so “we used a lot of tokens” tells a finance team almost nothing on its own. The real questions are who, what, and did it ship. Cursor’s data also shows cost per agent request swinging nearly nine times across model families — from roughly $1.57 on the most expensive model down to $0.18 on the cheapest — so the identical request is priced completely differently depending on where it gets routed, the exact dynamic we unpacked in [why acceptance rate is the wrong metric for coding-tool ROI](/blog/ai-coding-tool-roi-metrics/). Put it together, and Cursor has accidentally proven the thesis it did not set out to prove. The correlation is interesting. It is not proof. Proof comes from measurement — who, what, and why, token by token, tied to what actually shipped. That is the game for 2026 and 2027. ## What this means beyond coding tools Cursor is a coding-specific example, but the same trap applies to every AI vendor a company runs, from customer-support copilots to [autonomous agents](/agent-iq/) handling multi-step workflows. A vendor’s dashboard will always show the metrics that make the vendor look good. A quintile chart on revenue growth is a marketing asset for Cursor, not an ROI audit for the buyer. The only way to get an honest answer is a measurement layer that sits above any single vendor, pulling cost-per-outcome data across every tool a company runs, which is precisely the gap [custom KPI tracking](/analytics-kpis/) is built to close — visibility a vendor’s own blog post will never hand over voluntarily. ## The read The headline this week is simple: the CFO just walked into the coding room, and Cursor held the door open. The number that should stick, though, is not the 16.5% versus 5.1%. It is the fact that Cursor felt it needed to build a CFO council at all. When the company selling the tokens starts speaking finance’s language unprompted, that is the clearest signal yet that unmeasured AI spend has become too large a line item to leave unmanaged, and vendor-neutral proof, not vendor-supplied correlation, is what the CFO’s office actually needs. One question worth asking internally: is the CFO already in the AI tokens conversation at your company, or still on the outside looking in? [**Talk to an Expert →**](/schedule-a-demo/) [Your Engineering Team Uses 3+ AI Coding Tools. What You’re Missing.](https://olakai.ai/blog/ai-coding-tool-sprawl/) [Gartner: Only 28% of AI Projects Deliver ROI. Here’s Why the Rest Don’t.](https://olakai.ai/blog/gartner-ai-roi-28-percent/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Custom Kpis Ai Measurement Source: /blog/custom-kpis-ai-measurement [← Back to Olakai's Blog](/blog/) # Custom KPIs: The Four-Layer System Behind Olakai’s Metrics ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. July 22, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) “Custom KPIs” sounds like a settings screen — pick a formula, name a metric, done. What’s actually interesting about Olakai’s KPI system for AI agents is the four-layer architecture underneath that screen, and specifically the parts of it you’re not allowed to customize. That restriction is the feature, not a limitation, and it’s worth understanding why before assuming more configurability would automatically be better. ## Four layers, decreasing rigidity At the base sit Raw Metrics — pure aggregations straight from event data, always visible on every agent, zero configuration required. Interaction Volume counts total prompt requests; Token Consumption sums tokens across all of them. Neither can be overridden, because there’s nothing to argue about: they’re direct counts, not judgment calls. Above that sit Metric Slots — standardized measurement points that every new agent gets automatically provisioned with, no setup required. Each slot has an enforced output contract: a fixed unit that can never change, paired with a formula that can. Execution Cost always reports in USD, by default calculated from total tokens times cost per million tokens, with market-rate pricing applied automatically when a recognized model like Claude Sonnet or GPT-4o is detected instead of a flat default rate. Time Saved always reports in minutes, by default estimated through an AI classifier that reads the conversation and buckets it into one of five tiers, from zero minutes for a trivial exchange up to sixty for something that would have taken an hour manually — and coding-agent sessions get a purpose-built variant of that classifier with an 480-minute ceiling that reads structural signals like tool calls and files edited, because most of the real work in a coding-agent session lives in tool calls and file edits, not in the visible transcript text. Value Created always reports in USD, calculated from time saved times an hourly rate. Governance Compliance always reports as a percentage, measuring the share of interactions under a configurable risk threshold. You can change how each slot calculates its number. You can never change what unit it reports in. Composites sit above the slots, computed automatically and not directly editable at all — their values come entirely from the slots feeding them. The flagship composite is ROI: Value Created divided by Execution Cost, expressed as a multiplier. Below 1x means the agent costs more than it saves. 1x to 5x is good, worth continued investment. Above 5x is excellent, worth expanding to new use cases. You can’t tune ROI directly — the only way to improve it is by refining the Execution Cost and Value Created slots feeding it, adjusting the underlying cost formula or hourly rate assumption rather than nudging the output number itself. Custom KPIs sit at the top, fully open: your own formula, classifier, or LLM-based extraction, any name, any unit, any aggregation. No output contract, no restriction. ## Why the restriction is the point Raw Metrics and Metric Slots being non-fully-configurable is exactly what makes cross-agent benchmarking and portfolio-level ROI mean anything at all. Every agent’s Execution Cost reports in USD no matter how it’s calculated internally, so a Head of AI comparing thirty agents across different teams is comparing genuinely comparable numbers, not thirty differently-defined “cost” figures that happen to share a column header. Full flexibility everywhere would look more powerful in a demo and break the one thing that makes the ROI composite trustworthy at scale — the constraint is a deliberate design choice, not a missing feature waiting to be built. ## Assistive IQ measures the same question a different way This four-layer system is specifically how [Agent IQ](/agent-iq/) measures autonomous agents. [Assistive IQ](/assistive-iq/) — chatbots, copilots, browser-monitored tools — answers the same executive question, “is AI creating more value than it costs,” through a genuinely different measurement pipeline, and Olakai says so directly rather than pretending it’s one unified system end to end. Assistive’s value signal comes from Advanced Analytics estimating time saved per interaction, not from a formula-slot architecture; its cost signal is app-level subscription and licensing economics, since most assistive tools are billed per seat rather than per token. Run the same shape of calculation through that pipeline — 10,000 monthly interactions, 5 minutes saved each, an $55 hourly rate, against a $2,000 monthly subscription — and you get 833 hours saved, $45,815 in value created, and a 22.9x ROI. Same ROI shape, value divided by cost, applied to a different cost basis because the underlying billing reality is different. Assistive’s answer to “one number for executive reporting” isn’t a Productivity Score — it’s the [OLA Index](/blog/ai-impact-dashboard-explained/), a 0-100 adoption score built from user penetration, engagement depth, use-case breadth, and consistency of usage. Different math, same instinct: give a non-technical executive one trustworthy number instead of a dashboard full of raw counts. ## Why the honesty is worth more than a unified story It would be a cleaner marketing story to claim one KPI engine spans every AI use case on the [platform](/analytics-kpis/). It would also be false, and false in a way that would eventually get caught the moment someone tried to compare an Agent IQ ROI figure against an Assistive IQ one and found the cost basis didn’t reconcile. Cost basis is the part that’s genuinely cross-cutting here — per-token billing and subscription billing both show up inside Agentic traffic and Assistive traffic alike, not neatly split one basis per product — which is exactly the kind of nuance that only survives if the documentation, and the content built on top of it, admits the system isn’t unified yet rather than smoothing over the seam. Out-of-the-box defaults that work immediately, full customization available exactly where precision matters, and honesty about where two products still measure differently — that combination is what a genuinely business-friendly interface looks like in practice, not a slogan on a features page. Want to see how Agent IQ’s KPI slots and Assistive IQ’s OLA Index would read against your own AI usage? [Talk to an Expert](/schedule-a-demo/). [Your AI Got Cheaper. Your Bill Didn’t.](https://olakai.ai/blog/your-ai-got-cheaper-your-bill-didnt/) [Build or Buy: The Arithmetic on Running Your Own Model](https://olakai.ai/blog/build-or-buy-self-hosting-ai-cost/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Customer Success Ai Source: /blog/customer-success-ai [← Back to Olakai's Blog](/blog/) # 7 AI Use Cases for Customer Success Teams ![AI use cases for customer success teams - intelligent automation](https://olakai.ai/wp-content/uploads/2026/01/customer-success-ai-featured.png) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. November 28, 2025 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) When a mid-market SaaS company’s customer success team realized they were losing customers, they discovered a painful pattern: by the time usage declined enough to trigger alerts in their CRM, customers had already mentally checked out. The decline started months earlier, but the signals were scattered across product analytics, support tickets, and billing data that no one was connecting. They were always too late. This reactive approach to customer success is common—and increasingly uncompetitive. According to the [2025 Customer Revenue Leadership Study](https://churnzero.com/blog/customer-success-platform-increase-nrr/), teams using customer success platforms average 100% net revenue retention versus 94% without. That six-point difference compounds dramatically over time: retained customers expand, while churned customers require expensive replacement. Customer success teams are the guardians of recurring revenue. They retain customers, drive expansion, and prevent churn. But they’re often stretched thin—managing hundreds of accounts with limited bandwidth for proactive engagement. [AI agents](/use-cases/) can change this equation fundamentally. By automating routine tasks and surfacing insights that would otherwise remain hidden in siloed data, they enable CS teams to focus their energy on high-impact customer relationships. ## Overview: Customer Success AI Use Cases Use Case Typical ROI Complexity Time to Value Churn Risk Detection 20-30x Medium 8-12 weeks Customer Health Scoring 10-15x Medium 4-6 weeks Onboarding Automation 8-12x Medium 4-6 weeks QBR Automation 5-8x Low 2-4 weeks Expansion Opportunity Detection 15-20x Medium 6-10 weeks Renewal Management 10-15x Medium 4-6 weeks Sentiment Analysis 5-8x Low 2-4 weeks ## 1\. Churn Risk Detection: Save Customers Before They Leave Churn often becomes visible only when it’s too late—the customer has already decided to leave. Yet usage data contains early warning signals weeks or months in advance. In 2025’s AI-driven landscape, churn rate has evolved from a lagging indicator to a predictive metric. According to [industry research](https://www.everafter.ai/glossary/customer-churn-rate), machine learning models can now forecast customer attrition 3-6 months in advance, giving CS teams time to intervene rather than simply react. An AI churn agent continuously monitors product usage and engagement metrics, identifying declining patterns that predict departure before customers stop responding to outreach. It scores each customer’s risk level based on behavioral signals—login frequency drops, feature abandonment, support ticket tone shifts—and alerts CSMs with prioritized lists of at-risk accounts. More importantly, it suggests specific intervention tactics based on what’s worked for similar accounts in similar situations. Organizations report 15-25% reduction in customer attrition through AI-powered early warning systems. For a subscription business with significant revenue per customer, that translates to 20-30x ROI through preserved revenue that would otherwise have walked out the door. ## 2\. Customer Health Scoring: Know Who Needs Attention Generic health scores miss segment nuances. A one-size-fits-all metric doesn’t capture the different patterns of healthy enterprise versus SMB customers, or new versus mature accounts. What looks like declining health in one segment might be perfectly normal in another. An intelligent health scoring agent builds segmented models that understand what “healthy” looks like for different customer types. It monitors usage and engagement in real-time, predicts future churn based on current trend trajectories, and alerts CSMs when health declines in ways that matter for each specific segment. The models improve over time as they learn which patterns actually precede churn versus which are false alarms. Organizations with sophisticated health scoring report 30% more accurate churn prediction and 25% reduction in actual churn through early intervention. The 2025 Customer Revenue Leadership Study found that survey participants ranked NRR (51%), churn rate (48%), and GRR (40%) as their top three metrics for customer success teams—health scoring directly impacts all three. ## 3\. Onboarding Automation: Accelerate Time-to-Value Generic onboarding yields 40-60% activation rates. Customers get stuck at friction points—confusing configurations, unclear next steps, features they don’t know exist—without anyone noticing until it’s too late. By then, the customer has formed their impression of the product, and it’s not a good one. An onboarding agent monitors new customer behavior in real-time, identifying stumbling blocks as they happen rather than in post-mortem analysis. It sends targeted in-app guidance when customers hesitate at known friction points. It personalizes onboarding based on role and use case—a finance user needs different guidance than an operations user. CSMs receive alerts when customers struggle, allowing human intervention before frustration sets in. The impact compounds: 30-40% improvement in activation rates means more customers reach the “aha moment” where they understand the product’s value. Time-to-value improvements of 50% mean customers see returns faster, strengthening the relationship before the first renewal conversation. That translates to 8-12x ROI through retention gains that start on day one. ## 4\. QBR Automation: Prepare Reviews in Minutes Quarterly Business Reviews are essential for strategic relationships, but CSMs spend hours preparing slides and gathering metrics for each customer. It’s high-value time spent on low-value work—pulling data from five different systems, formatting charts, writing narratives that say the same things slightly differently for each account. A QBR automation agent handles the mechanical work. It automatically pulls usage metrics, identifies wins worth celebrating and concerns worth discussing, and generates presentation drafts that highlight discussion topics based on customer goals. It tracks action items from previous reviews and surfaces their status. The CSM’s job shifts from data gathering to insight refinement—editing and personalizing rather than creating from scratch. Organizations report 80% reduction in QBR prep time. More importantly, the reviews become more consistent and data-driven. When every QBR includes the same depth of analysis, customers notice the professionalism—and CSMs can actually focus on the strategic conversation rather than defending their data sources. ## 5\. Expansion Opportunity Detection: Grow What You Have Expansion revenue is the most efficient revenue — and closely tied to what [sales teams are doing with AI](/blog/ai-sales-use-cases/) on the acquisition side — but CSMs often miss signals that customers are ready for more. Increased usage, new team members, questions about advanced features, approaching plan limits—these signals exist in the data but rarely surface in time for action. An expansion agent monitors usage patterns for signals that indicate readiness. It identifies customers approaching plan limits before they hit them (the perfect moment for an upgrade conversation). It detects interest in additional products or features based on browsing behavior and support questions. It alerts account teams with specific expansion recommendations tailored to each customer’s actual usage patterns. The impact is substantial: 20-30% increase in expansion revenue from timely, relevant upsell conversations that feel helpful rather than pushy. According to the 2025 study, only 15% of teams currently use AI for predictive expansion signals—the opportunity is wide open for early adopters. ## 6\. Renewal Management: Never Miss a Renewal Renewal discussions often start too late. By the time the CSM reaches out 60 days before expiration, the customer has already been evaluating alternatives for months. The “renewal” conversation becomes a retention battle rather than a relationship affirmation. A renewal management agent tracks renewal dates across the entire portfolio, initiating sequences at appropriate times based on customer segment and contract value. It monitors sentiment and usage in the months leading up to renewal, flagging at-risk renewals early enough for meaningful intervention. It suggests renewal strategies based on customer health—the approach for a healthy, expanding account should differ from one that’s been quiet for months. Organizations report 15-20% improvement in renewal rates through earlier engagement with at-risk renewals. The math is straightforward: for subscription businesses, improving renewal rates by even a few percentage points has massive impact on lifetime value and growth efficiency. ## 7\. Sentiment Analysis: Understand How Customers Feel Customer satisfaction surveys provide snapshots, but miss the ongoing sentiment expressed in support tickets, emails, and chat conversations. A customer might give you a 9 on an NPS survey while simultaneously writing frustrated support tickets that signal impending churn. A sentiment agent analyzes tone across all customer communications, tracking sentiment trends over time. It identifies frustrated customers before they escalate complaints or simply stop engaging. It correlates sentiment shifts with churn risk and health scores, creating a more complete picture of customer state than any single metric provides. According to [Gartner research](https://www.gartner.com/en/newsroom/press-releases/2025-12-17-customer-service-and-support-leaders-must-prioritize-blending-human-strengths-with-ai-intelligence-in-2026), 91% of customer service leaders are under executive pressure to implement AI specifically to improve customer satisfaction. Sentiment analysis provides the continuous monitoring that makes satisfaction improvement measurable and actionable. ## Getting Started with CS AI If you’re ready to bring AI to your customer success organization, start with the data you have. Most CS AI use cases require product usage data (logins, feature usage, API calls), CRM data (accounts, contacts, activities), support data (tickets, response times, resolutions), and financial data (contract values, renewal dates). The good news: you probably already have this data scattered across systems—AI’s job is connecting it. Pick one high-impact use case rather than trying to do everything at once. Churn risk detection or health scoring are often good starting points—they have clear ROI and build the foundation for other use cases. Once you can predict churn, expansion and renewal optimization become natural next steps. Define success metrics upfront. Common CS AI metrics include churn rate improvement, net revenue retention, expansion revenue per account, CSM productivity (accounts per CSM), and time to value for new customers. For a framework on connecting AI metrics to business outcomes, see our [AI ROI measurement guide](/blog/ai-roi-framework/). Build governance from day one. CS data often includes sensitive customer information—usage patterns, business communications, financial details. Ensure proper data handling, access controls, and audit trails before deployment, not after. Our [CISO governance checklist](/blog/ciso-governance-checklist/) covers the security considerations. ## The Retention Imperative In subscription businesses, retention is everything. A 5% improvement in retention can drive 25-95% profit improvement according to classic research by Bain & Company. The [Future of Agentic use case library](https://futureofagentic.com/use-cases/) includes detailed customer success scenarios with architecture patterns you can adapt. AI doesn’t replace the human relationships that drive retention—the empathy, the strategic guidance, the trust that comes from knowing your customers. But it ensures CSMs focus their limited energy where it matters most: on the relationships that need attention, armed with the context to make that attention valuable. The customer success teams that master AI will protect more revenue, drive more expansion, and manage more accounts per CSM. Those that don’t will fall behind as competitors automate their way to better retention numbers. *Ready to bring AI to your customer success team? [Talk to an expert](/schedule-a-demo/) to see how Olakai helps you measure the impact of CS AI initiatives and govern them responsibly.* [From AI Experimentation to Business Impact](https://olakai.ai/blog/ai-experimentation-impact/) [The Evolution of Enterprise AI: From Prediction to Action](https://olakai.ai/blog/enterprise-ai-evolution/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Desktop Renaissance Ai Measurement Gap Source: /blog/desktop-renaissance-ai-measurement-gap [← Back to Olakai's Blog](/blog/) # The Return of the Desktop App: And the AI Measurement Gap It Creates ![Vintage desktop computer with glowing AI neural network on CRT screen](https://olakai.ai/wp-content/uploads/2026/04/desktop-renaissance.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. April 21, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) For 25 years, the entire direction of travel in enterprise software was the same: everything moved to the browser. Salesforce on CDs gave way to Salesforce in a tab, Office gave way to Google Docs, Sketch gave way to Figma, and every installer eventually got replaced by a URL. The logic behind that shift was airtight. Zero friction to distribute, one codebase across every operating system, native multiplayer, continuous deployment, and a subscription revenue model that buyers actually preferred. The web won so decisively that even Adobe capitulated to subscription pricing in 2013, and Microsoft declared itself “cloud-first” within 52 days of Satya Nadella taking over in 2014. If you were building software in 2020 and told a VC you were shipping a desktop app, you were laughed out of the room. And then, somewhere in the last 18 months, every AI-native company that could have stayed browser-only started shipping desktop apps instead. OpenAI released a ChatGPT Mac app in May 2024, before they had reached feature parity on mobile. Anthropic followed with Claude desktop in November, alongside the [Model Context Protocol](https://www.anthropic.com/news/model-context-protocol), which went from around 2 million to 97 million monthly SDK downloads in 16 months. The entire point of MCP is giving AI access to the local filesystem that browsers cannot reach. Perplexity shipped a native Mac app. Cursor, a desktop IDE you download and install the old-fashioned way, is reportedly in talks to raise at a $50 billion valuation, which is roughly the last price tag attached to a desktop-first software company when that company was Microsoft. Meanwhile Ollama, which exists purely to run AI models locally on your laptop with no API call involved, went from around 100,000 monthly downloads in early 2023 to over 52 million in early 2026. That is a 520x increase in three years for a product whose defining feature is that it does not touch the cloud. And Microsoft, the same Microsoft that was cloud-first, now mandates that every Copilot+ PC ship with 40 trillion operations per second of on-device AI silicon. The company that spent a decade telling its customers to move everything to Azure is now re-engineering consumer PCs around local inference. ## The Web Won on Five Things, and AI Wants All Five Reversed Every piece of enterprise software that moved to the browser did so because the browser offered five structural advantages: zero-friction distribution, SaaS economics, native multiplayer collaboration, cross-device access, and continuous deployment. For most categories, those advantages were decisive. Desktop software only held on in the handful of places where GPU access, filesystem access, offline reliability, or sub-10-millisecond latency were in the critical path. Video editing stayed on the desktop. So did CAD, IDEs, gaming, and anything that needed to push pixels or bits in real time. Those constraints were not ideological. They were physics. And every serious AI workload happens to sit squarely inside them. AI agents need to read your actual codebase, not whatever you remembered to paste into a chat window. They run for minutes or hours, not the lifetime of a browser tab that the operating system feels free to suspend the moment you switch windows. Meeting copilots need raw screen and audio access that browsers wall off by design, for good security reasons. Voice AI and autocomplete UX fall apart the moment you introduce a network round-trip, which is why Cursor feels instant and most browser-based AI tools feel laggy. The same constraints that kept Premiere on the desktop in 2005 are now shaping the entire AI application layer in 2026, which means for the first time in a generation the list of software categories that have to live on your machine is growing rather than shrinking. ## And That Creates a Measurement Problem Here is where this gets interesting for anyone trying to run an enterprise AI program. When AI lived in the browser, you could measure it. Your employees logged into ChatGPT through a centralized account, or they used a SaaS tool whose admin console told you exactly who used what and when. Single sign-on, audit logs, API gateway usage reports, the entire governance stack that evolved for SaaS could be pointed at AI with a few configuration tweaks. The web’s centralization was a pain point for vendors in 2000 and a gift to CIOs in 2020. Everything flowed through a known endpoint, and everything left a trace. The desktop renaissance is dismantling that model, category by category, in a matter of months. A developer using Cursor is running AI inference against your codebase on their local machine, and your IT team cannot see what they are doing through any centralized log. A knowledge worker using Claude desktop is having conversations with a foundation model that may or may not touch your network. A sales leader using Granola is recording every meeting on their device, with no browser session to inspect. A product team experimenting with Ollama is pulling seventy-billion-parameter models down from Hugging Face and running inference entirely offline, with no API call that your network observability tools can capture. The [shadow AI problem](https://olakai.ai/blog/shadow-ai-risk/) that was already keeping CISOs up at night is about to get qualitatively worse, because the new generation of AI tools is specifically engineered to bypass the centralized chokepoints that corporate governance depends on. You cannot measure what you cannot see, and you cannot govern what you cannot measure. The measurement gap that enterprises are already struggling to close in their [AI ROI programs](https://olakai.ai/blog/ai-roi-framework/) is about to widen significantly, precisely at the moment when boards and CFOs are starting to demand proof of value. ## What the Old Playbook Got Wrong For years, the default AI governance playbook at most enterprises has been some version of: restrict access to sanctioned tools, route traffic through an approved gateway, and generate usage reports from the gateway logs. That playbook works reasonably well when the AI tool in question is a cloud-hosted chatbot that an employee reaches through a browser. It falls apart the moment the AI tool is a desktop app that talks directly to a foundation model provider, or worse, runs inference on the laptop itself. The uncomfortable truth is that measurement and governance in an AI-first enterprise cannot be built from the network layer or from SaaS admin consoles alone. You need a [visibility layer](https://olakai.ai/blog/ai-visibility-audit/) that works across cloud, browser, desktop, and local-inference environments, and that treats each AI interaction as an observable event regardless of where the compute happened. You need [metrics that CFOs actually want to see](https://olakai.ai/blog/ai-metrics-that-matter/) rather than vanity counts of API calls. And you need a governance model that assumes AI usage is heterogeneous and distributed by default, not centralized and inspectable by default. ## What Leaders Should Do Now The shift to AI-native desktop is not a reason to panic, and it is not a reason to try to block desktop AI apps. Every serious study on enterprise AI adoption points to the same conclusion: knowledge workers will use the tools that make them productive, and the companies that lean into that rather than fighting it capture disproportionate value. The question is not whether to allow your teams to use Cursor and Claude and Ollama. The question is whether you can see enough of what is happening across all of them to understand the [true ROI of agentic AI](https://olakai.ai/blog/ai-agent-roi-lessons/), catch governance failures before they become incidents, and make informed decisions about where to invest next. That starts with accepting that your AI measurement layer needs to extend into the desktop, the IDE, and the on-device inference runtime, not just the browser. It continues with building [unified AI analytics](https://olakai.ai/blog/what-is-ai-analytics/) that aggregate events from across environments into a single view. And it ends with a governance model that is resilient to heterogeneity, because the direction of travel for the next five years is more AI, in more places, running on more devices, against more models, not less. The desktop is back. The browser is not going away. Most enterprises will run both, permanently. The organizations that win will be the ones that can see across both, measure across both, and make decisions grounded in that visibility. If you are thinking about how to build that measurement layer inside your organization, [we would love to talk](https://olakai.ai/schedule-a-demo/). [Tokenmaxxing Is the New Lines of Code: Why Token Leaderboards Won’t Prove AI Value](https://olakai.ai/blog/tokenmaxxing-claudeonomics/) [Meta’s $53B AI Capex Bet vs. 14,000 Layoffs: When the Market Stops Cheering](https://olakai.ai/blog/meta-layoffs-ai-capex-math/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Enterprise Ai Evolution Source: /blog/enterprise-ai-evolution [← Back to Olakai's Blog](/blog/) # The Evolution of Enterprise AI: From Prediction to Action ![CTO reviewing agentic AI workflow automation diagrams on screen in modern office](https://olakai.ai/wp-content/uploads/2025/12/featured-post-1223.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. December 4, 2025 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Three years ago, ChatGPT launched and changed everything. Or did it? The reality is more nuanced. According to [McKinsey’s 2025 State of AI report](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai), 88% of enterprises now report regular AI use in their organizations. That’s remarkable progress. But here’s the sobering counterpoint: over 80% of those same respondents reported no meaningful impact on enterprise-wide EBIT. AI has gone from experimental to operational, but for most organizations, it hasn’t yet become transformational. Understanding why requires understanding how enterprise AI has evolved—and where it’s heading next. What started as specialized machine learning models for prediction has evolved into autonomous agents capable of taking action on behalf of the organization. Each era has built on the last, and each has demanded different capabilities from the organizations deploying it. ## The Four Eras of Enterprise AI ### Era 1: Traditional AI (2020-2022) This was AI as most enterprises first knew it—sophisticated machine learning models trained on historical data to make predictions. A fraud detection model could flag suspicious transactions. A demand forecasting system could predict inventory needs. But the key limitation was fundamental: these systems provided scores and classifications. They couldn’t take action. These traditional AI systems excelled at passive prediction—providing scores or classifications that required human interpretation. Each model was single-purpose, built for a specific task, and demanded substantial data requirements for training. They had limited adaptability to new situations and couldn’t learn from conversational feedback. Think fraud detection scoring, demand forecasting, customer churn prediction, image classification, and recommendation engines. These systems were powerful but required significant data science expertise and infrastructure investment. Value came from better predictions, but humans still made all decisions and took all actions. The barrier to entry was high—you needed specialized talent and years of data to train effective models. ### Era 2: Chat AI (2023) ChatGPT’s November 2022 launch marked a turning point. Suddenly, any employee could interact with AI using natural language—no data science degree required. Within months, generative AI went from curiosity to corporate priority. According to the [Stanford HAI 2025 AI Index Report](https://hai.stanford.edu/ai-index/2025-ai-index-report), U.S. private AI investment grew to $109.1 billion in 2024—nearly 12 times China’s investment and 24 times the U.K.’s. Chat AI delivered an interactive Q&A interface with natural language understanding and generation, broad general knowledge, and remarkable accessibility. But it had no ability to take action and maintained only stateless conversations. ChatGPT for research and drafting, customer service chatbots, content creation tools, and code explanation and debugging became commonplace. ChatGPT made AI accessible to everyone. But these systems could only provide information—they couldn’t take action in business systems. The knowledge was impressive; the capability to act on it was absent. ### Era 3: Copilots (2024) Copilots represented the first real integration of generative AI into daily work. Code became AI’s first true “killer use case”—50% of developers now use AI coding tools daily, according to Menlo Ventures research, rising to 65% in top-quartile organizations. [Menlo Ventures reports](https://menlovc.com/perspective/2025-the-state-of-generative-ai-in-the-enterprise/) that departmental AI spending on coding alone reached $4 billion in 2025—55% of all departmental AI spend. Copilots brought context-aware suggestions while keeping humans in control of every decision. They provided real-time assistance during work and integrated into existing tools like IDEs, productivity apps, and CRMs. But they required constant human oversight—the AI suggested, the human decided. GitHub Copilot for code completion, Microsoft 365 Copilot for productivity, Salesforce Einstein GPT for sales, and Google Duet AI for workspace defined this era. Copilots showed AI could accelerate individual productivity. A developer with Copilot could write code faster; a sales rep could draft emails more quickly. But humans still made every decision and approved every action. The AI suggested; the human decided. ### Era 4: Agentic AI (2025-2026) This is where we are now—and where the transformation gets real. For a deeper understanding of what distinguishes agents from earlier AI systems, see our guide on [what agentic AI actually means](/blog/what-is-agentic-ai/). According to [Gartner](https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025), 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. That’s an 8x increase in a single year. McKinsey’s research shows 62% of organizations are already experimenting with AI agents, with 23% actively scaling agentic AI systems. The projected ROI is striking: organizations expect an average return of 171% from agentic AI deployments, with U.S. enterprises forecasting 192% returns. Agentic AI introduces goal-oriented autonomy—systems that can plan multi-step processes and execute them independently. They use tools and APIs, adapt through learning from feedback, and maintain contextual memory across sessions. Automated incident response, end-to-end invoice processing, supply chain optimization, multi-step sales workflows, and customer onboarding automation are emerging applications. Agents can complete entire workflows autonomously. They don’t just suggest the next email—they draft it, send it, track responses, and follow up. The human role shifts from execution to oversight. This is where AI finally starts delivering on the promise of true business transformation. ## What Changes with Each Era Dimension Traditional AI Chat AI Copilots Agents **Human role** Interpret & act Ask & evaluate Approve & edit Supervise & escalate **Autonomy** None None Limited High **Integration** Backend systems Chat interface Within apps Across systems **Expertise needed** Data scientists Anyone Anyone Anyone (with governance) **Risk profile** Low (no action) Low (no action) Medium (human approval) Higher (autonomous action) ## The Governance Imperative As AI gains more autonomy, governance becomes more critical. But here’s a warning from Gartner that every enterprise leader should heed: over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value, or inadequate risk controls. The enterprises that succeed will be the ones that treat governance as an enabler, not an afterthought. Traditional AI and Chat AI carried a low governance burden—they provided information but took no action. Main concerns centered on accuracy and appropriate use. Copilots require moderate governance—AI suggests actions but humans approve. Concerns include data handling, appropriate suggestions, and over-reliance on AI-generated outputs. Agentic AI demands high governance. AI takes action autonomously, which means you need [visibility into what agents do, controls to prevent inappropriate actions, and audit trails](/platform/) for compliance. Without these, agents become liabilities rather than assets. Knowing how to [measure AI ROI](/blog/ai-roi-framework/) becomes essential when autonomous systems are making decisions on your behalf. ## What This Means for Enterprise Leaders ### The Opportunity Each era has delivered more value than the last. The numbers tell the story: companies spent $37 billion on generative AI in 2025, up from $11.5 billion in 2024—a 3.2x year-over-year increase. That investment is flowing toward real productivity gains, not just experimentation. ### The Challenge More autonomy means more risk. An agent that can take action can take wrong action. And the failure modes are real: 42% of companies abandoned most AI initiatives in 2025, up sharply from 17% in 2024, according to research from MIT and RAND Corporation. The gap between AI adoption and AI value remains stubbornly wide — a phenomenon we explore in depth in our guide on moving [from AI experimentation to business impact](/blog/ai-experimentation-impact/). ### The Path Forward The enterprises that will win are those who embrace agentic AI for the right use cases—starting with low-risk, high-volume workflows where automation delivers clear value and mistakes are recoverable. They’ll build governance from day one, treating visibility, controls, and measurement as core requirements rather than afterthoughts. They’ll measure outcomes relentlessly, proving ROI and identifying problems before they become crises. And they’ll prepare their organization, helping employees understand how their roles will evolve from execution to oversight as agents take on more autonomous work. ## What’s Next The evolution isn’t over. By 2028, Gartner predicts at least 15% of day-to-day work decisions will be made autonomously through agentic AI—up from 0% in 2024. Additionally, 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024. Several emerging trends deserve attention. Multi-agent systems—agents that coordinate with each other to complete complex tasks—are moving from research to production. Continuous learning enables agents that improve from feedback without manual retraining. Deeper integration gives agents access to more enterprise systems and data. And industry-specific agents provide pre-built solutions for common workflows in specific industries. For a deeper exploration of the economics driving agent adoption, the [Future of Agentic guide to agent economics](https://futureofagentic.com/agentic-ai-101/agent-economics/) covers TCO analysis and ROI calculations. The enterprises that understand this evolution—and prepare for what’s coming—will be best positioned to capture value from AI. The ones that don’t will find themselves in that uncomfortable 80%: using AI everywhere, but struggling to show the ROI. *Ready to navigate the evolution of enterprise AI? [Talk to an expert](/schedule-a-demo/) to see how Olakai helps organizations measure and govern AI across all four eras.* [7 AI Use Cases for Customer Success Teams](https://olakai.ai/blog/customer-success-ai/) [AI Risk Heatmap: Matching Governance to Business Value](https://olakai.ai/blog/ai-risk-heatmap/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Enterprise Ai Roi Gap 2026 Source: /blog/enterprise-ai-roi-gap-2026 [← Back to Olakai's Blog](/blog/) # The Enterprise AI Revenue Gap: What 3,235 Leaders Reveal ![Enterprise AI revenue gap visualization — ascending investment fragmenting into scattered returns](https://olakai.ai/wp-content/uploads/2026/02/enterprise-ai-roi-gap-featured.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. February 21, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Deloitte just surveyed 3,235 business and IT leaders across 24 countries for its [State of AI in the Enterprise 2026](https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html) report, and the headline finding lands like a punch: 74% of organizations say they want AI to grow revenue. Only 20% have actually seen it happen. That is not a rounding error. That is a 54-point gap between ambition and reality — and it explains why boardrooms across every industry are shifting from “how much are we investing in AI?” to “what exactly are we getting back?” ## The Revenue Gap Is Not a Technology Problem The instinct is to blame the technology. Models hallucinate, integrations break, data is messy. But Deloitte’s data tells a different story. The enterprises stuck in that 80% are not failing because the AI does not work. They are failing because they cannot prove that it does. Consider the numbers: 37% of organizations in the survey are using AI at a surface level with minimal process changes. They have deployed copilots and chatbots across teams, but nothing fundamental has shifted. The AI runs alongside existing workflows instead of transforming them — and without transformation, there is no measurable business outcome to point to. When the CFO asks what the AI program returned last quarter, the answer is a shrug wrapped in anecdotes. The organizations in the 20% who are seeing revenue growth did something different. They tied AI deployments to specific business KPIs from day one. They instrumented their programs to [measure AI ROI](/ai-roi/) continuously — not in a quarterly review, but in real time. And critically, they built the governance structures that allowed them to scale safely from pilot to production. ## Pilot Purgatory: The Graveyard of AI Ambition Deloitte found that only 25% of organizations have moved 40% or more of their AI pilots into production. Let that sink in. Three out of four enterprises have the majority of their AI initiatives still sitting in pilot mode — consuming budget, occupying engineering time, and delivering precisely nothing to the bottom line. This is the phenomenon we have written about as the journey [from AI experimentation to measurable business impact](/blog/ai-experimentation-impact/). The pattern is consistent: a team builds a promising proof of concept, it performs well in controlled conditions, and then it stalls. The reasons vary — insufficient data pipelines, unclear ownership, missing security approvals — but they share a common root. Nobody established the measurement framework that would have justified the investment needed to cross the production threshold. Without hard numbers showing what a pilot delivered in its controlled environment, the business case for scaling it evaporates. And so the pilot sits. The team moves on to the next experiment. The cycle repeats. Deloitte’s survey confirms what many CIOs already feel: enterprise AI has become a graveyard of promising experiments that never grew up. ![Enterprise AI: Ambition vs Reality — four gaps from Deloitte State of AI 2026 survey showing revenue, pilot, governance, and access divides](https://olakai.ai/wp-content/uploads/2026/02/roi-gap-inline.webp) ## The Agentic AI Wave Is Coming — And Governance Is Not Ready If the current state of AI adoption is sobering, the next wave should genuinely concern enterprise leaders. Deloitte reports that [agentic AI usage is expected to surge from 23% to 74% of enterprises](https://siliconangle.com/2026/02/15/thecube-research-2026-predictions-year-enterprise-roi/) within two years. Eighty-five percent of companies are already planning to customize and deploy autonomous agents. The problem? Only 21% have mature governance frameworks for agentic AI. Agentic AI is fundamentally different from the chatbots and copilots most enterprises have deployed so far. [Agents](/blog/what-is-agentic-ai/) do not wait for a human to type a prompt. They take autonomous actions — executing multi-step workflows, calling APIs, making decisions, and interacting with production systems. An ungoverned chatbot might give a bad answer. An ungoverned agent might execute a bad decision at scale, with real financial and operational consequences. For a structured approach to governing agents proportionally, see our [AI risk heatmap framework](/blog/ai-risk-heatmap/). The governance gap for agentic AI is not abstract. It is the difference between an agent that autonomously processes customer refunds within policy and one that processes them without any guardrails at all. It is the difference between an agent whose cost-per-execution is tracked and one that silently racks up API bills nobody sees until the invoice arrives. ## What Separates the 20% From the 80% Across Deloitte’s data and our own experience working with enterprises deploying AI at scale, three patterns consistently separate organizations that achieve measurable returns from those that do not. **They measure from day one, not day ninety.** The enterprises delivering AI revenue growth did not bolt on measurement as an afterthought. They defined what success looks like before a single model was deployed — tying each initiative to a specific KPI, whether that is time saved per ticket, revenue influenced per campaign, or cost reduced per transaction. When Deloitte found that the 20% were disproportionately concentrated in organizations with mature AI programs, it was not because those programs had better technology. It was because they had better instrumentation. **They govern proportionally, not reactively.** The 21% with mature agent governance did not get there by locking everything down. They built tiered frameworks where low-risk AI applications move fast with light oversight, while high-risk autonomous agents face rigorous approval and monitoring. Our [CISO governance checklist](/blog/ciso-governance-checklist/) provides the template for building exactly this kind of tiered framework. This approach avoids the two failure modes that plague most enterprises: either everything is blocked by compliance reviews that take months, or everything is approved with a wave of the hand and nobody knows what is actually running. **They have a unified view.** Deloitte found that workforce access to sanctioned AI tools expanded 50% in a single year — from under 40% to roughly 60% of employees. That is a staggering increase in the surface area that needs visibility. The enterprises succeeding at AI are the ones who can answer, across their entire organization, which tools are being used, by whom, for what purpose, and with what result. The enterprises stuck in the 80% are managing each AI tool in its own silo, each with its own vendor dashboard, none of them talking to each other. ## The Clock Is Ticking Deloitte’s report arrives at a moment when patience for AI investment without returns is running out. This is no longer a technology-forward bet that boards are willing to make on faith. The $700 billion that the four major hyperscalers plan to spend on AI infrastructure in 2026 has already triggered an investor reckoning — Microsoft lost $360 billion in market cap in a single day when its AI spending outpaced its Azure revenue growth. If Wall Street is demanding AI ROI from the world’s most sophisticated technology companies, your board is not far behind. The enterprises that will thrive through this reckoning are not the ones spending the most on AI. They are the ones who can prove what their AI spending returns. That starts with measurement — real, continuous, outcome-tied measurement — and it scales with governance that grows alongside the program. When your CFO asks what the AI program delivered this quarter, what will your answer be? **[Talk to an expert](/schedule-a-demo/)** to see how Olakai helps enterprises measure AI ROI, govern risk, and close the gap between AI investment and business impact. [The AI Visibility Audit: What You Can’t See Is Costing You](https://olakai.ai/blog/ai-visibility-audit/) [AI Metrics That Matter: What CFOs Actually Want to See](https://olakai.ai/blog/ai-metrics-that-matter/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Enterprise Ai Roi Playbook Source: /blog/enterprise-ai-roi-playbook [← Back to Olakai's Blog](/blog/) # The Enterprise AI ROI Playbook: See, Measure, Decide, Act ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. February 17, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Half of CEOs believe their jobs are on the line if AI doesn’t pay off. Yet according to BCG’s AI Radar 2026 survey, 90% of chief executives believe agentic AI will deliver measurable ROI this year. That’s a remarkable level of conviction given what the data actually shows: IBM found that only 29% of executives can confidently measure their AI returns, and just 16% have scaled AI initiatives enterprise-wide. The confidence is there. The measurement capability is not. And that gap — between what leaders believe AI can do and what they can prove it has done — is where budgets get cut, pilots stall, and competitors pull ahead. This is why we built the SEE, MEASURE, DECIDE, ACT playbook — a four-step framework that takes enterprises from “we think AI is working” to “here’s exactly what it’s worth.” It’s the same methodology we use with every enterprise we work with, and the same framework that separates the [20% of organizations seeing real revenue impact from AI](https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html) from the 74% who want it but can’t prove it. ## The Playbook Gap Deloitte’s 2026 State of AI survey captured the problem in a single data point: 74% of enterprises say they want AI to drive revenue growth. Only 20% have achieved it. That’s 3,235 business leaders across 24 countries essentially saying the same thing — we’re investing heavily, but we can’t connect the investment to results. The issue isn’t the technology. AI models are more capable than ever. The issue is that most enterprises lack a systematic approach to proving value. They launch pilots without defining what success looks like. They measure activity (tokens processed, queries handled) instead of outcomes (revenue influenced, costs avoided). And when the CFO asks “what’s our return?”, the answer is a shrug wrapped in a slide deck full of usage charts. BCG found that companies plan to double their AI spending in 2026, pushing AI investment to roughly 1.7% of total revenues. CEOs are committing more than 30% of their AI budgets specifically to agentic AI. The money is flowing. But without a measurement playbook, most of it flows into a black box. ## Step 1: SEE — Map Your AI Ecosystem You can’t measure what you can’t see. And in most enterprises, the AI landscape is far more sprawling than leadership realizes. Workforce access to AI tools expanded by 50% in just one year, according to Deloitte — from fewer than 40% of workers to roughly 60% now equipped with sanctioned AI tools. That’s just the sanctioned ones. Factor in the tools employees adopt on their own — the [shadow AI](/blog/shadow-ai-risk/) that bypasses procurement and IT review — and the real number is significantly higher. The SEE step is an AI visibility audit. It answers three questions: What AI tools and models are running across the organization? Who is using them? And what data are they touching? This isn’t a one-time inventory. It’s an ongoing discovery process, because AI adoption in enterprises is a moving target — new tools appear weekly, usage patterns shift monthly, and the risk surface evolves with every new integration. Most enterprises discover during this step that they have three to five times more AI touchpoints than they thought. Customer service teams running chatbots that marketing doesn’t know about. Engineering teams experimenting with code assistants that security hasn’t reviewed. Sales teams piping prospect data through AI tools that legal hasn’t vetted. Until you see the full picture, every other step in this playbook is built on incomplete information. ## Step 2: MEASURE — Connect Activity to Business Outcomes Once you can see what’s running, the next step is measuring what matters. And “what matters” is almost never what teams measure first. The natural instinct is to track operational metrics: response time, tokens consumed, uptime, error rates. These are useful for engineering but meaningless to the CFO. The measurement step connects AI activity to the business KPIs that drive budget decisions — revenue influenced, costs reduced, risk mitigated, time recovered. This is where most enterprises stall. IBM’s research found that while 79% of organizations see productivity gains from AI, only 29% can measure ROI confidently. The productivity is real but unquantified. A customer success agent saves each rep 45 minutes per day — but nobody has connected that time savings to the additional accounts each rep can now manage, or the churn reduction that comes from faster response times. Effective AI measurement requires three elements. First, a baseline: what was the metric before AI? Without a counterfactual, you’re reporting output, not impact. Second, attribution: which portion of the improvement is actually due to AI versus other factors? Third, a time horizon that matches the business cycle. An AI agent that qualifies leads doesn’t show revenue impact in week one. It shows impact when those leads close, which in enterprise B2B might be 90 days later. The 20% of enterprises that prove AI revenue impact aren’t using more sophisticated models. They’re using more sophisticated measurement. They define the success KPI before deployment, not after. They instrument their AI systems to capture business outcomes, not just technical telemetry. And they present results in the [language the CFO speaks](/blog/cfo-ai-use-cases/) — dollars, not tokens. ## Step 3: DECIDE — Turn Data Into Scaling Decisions Measurement without decision-making is just reporting. The DECIDE step uses the data from MEASURE to answer the questions that actually move AI forward in an organization: Which pilots get promoted to production? Which get sunset? Where should the next investment go? This is where the 30-to-45-day structured pilot becomes critical. Rather than running open-ended experiments that drift for months, a time-boxed pilot with predefined KPIs produces a clear decision point. At the end of 30 days, you have data. Not opinions, not anecdotes — data that shows whether the AI investment is generating the business outcome you defined in the MEASURE step. The enterprises stuck in [pilot purgatory](/blog/ai-pilot-to-production/) almost always lack this decision framework. They have pilots running for six, nine, twelve months with no clear criteria for what constitutes success or failure. The result is the worst possible outcome: continued investment without conviction, where the AI initiative is too expensive to ignore and too poorly measured to champion. A proper DECIDE framework answers four questions with data: Is the AI system delivering the outcome KPI we defined? Is the cost-to-value ratio favorable? Can the governance and risk profile support scaling? And does the organization have the operational readiness to absorb the change? Google Cloud’s research found that top-performing enterprises generate [$10.30 in value for every dollar invested in AI](https://www.googlecloudpresscorner.com/2025-09-04-Google-Cloud-Study-Reveals-52-of-Executives-Say-Their-Organizations-Have-Deployed-AI-Agents,-Unlocking-a-New-Wave-of-Business-Value,1), while the average is $3.70. The difference isn’t luck. It’s disciplined decision-making about which investments to scale and which to cut — and that discipline is only possible with measurement data. ## Step 4: [ACT](/blog/ai-roi-act-framework/) — Scale With Confidence The final step is where measurement pays off: scaling the AI investments that prove their value while governing the entire portfolio continuously. Deloitte found that 25% of organizations now report AI having a “transformative” effect — up from just 12% a year ago. These are the enterprises that have moved through SEE, MEASURE, and DECIDE, and are now deploying AI at scale with the data to back every decision. They’re not guessing which use cases deserve investment. They know, because they measured. But scaling introduces new challenges that require continuous measurement. An AI agent that performs well with 100 users may behave differently with 10,000. Cost structures change at scale. [Risk profiles shift](/blog/ai-risk-heatmap/) as AI touches more sensitive data and higher-stakes decisions. The ACT step isn’t a one-time event — it’s an ongoing cycle of deploying, measuring, governing, and optimizing. This is where governance and measurement converge. The enterprises with the strongest ROI data are also the ones with the most rigorous governance frameworks. Not because governance is a checkbox exercise, but because governance forces the discipline that measurement requires: defining what AI is allowed to do, instrumenting how it performs, and maintaining the accountability structures that ensure continuous improvement. BCG reports that 72% of CEOs are now the primary decision-makers on AI, double the share from a year ago. These executives don’t want dashboards full of technical metrics. They want a portfolio view: which AI investments are generating returns, which ones need intervention, and where the next opportunity lies. The SEE, MEASURE, DECIDE, ACT framework gives them exactly that. ## Building Your Playbook The 74-to-20 gap Deloitte identified isn’t permanent. But it won’t close on its own. It closes when enterprises stop treating AI measurement as an afterthought and start treating it as the foundation of every AI initiative. Start with SEE: audit your AI ecosystem. You’ll likely find more than you expected. Move to MEASURE: define the business outcomes that matter and instrument your AI systems to capture them. Progress to DECIDE: use 30-day structured pilots to generate decision-quality data. And then ACT: scale what works, govern what runs, and keep measuring. The enterprises in the 20% didn’t get there with better AI. They got there with better measurement. The playbook isn’t complicated. The hard part is committing to it before the CFO asks the question you can’t answer. [Our AI ROI measurement framework](/blog/ai-roi-framework/) breaks down the methodology step by step, and [Future of Agentic’s KPI library](https://futureofagentic.com/success-kpis) offers specific metrics by use case to get you started. **Ready to build your AI ROI playbook?** [Talk to an expert](/schedule-a-demo/) and we’ll show you how enterprises are turning AI activity into measurable business outcomes. [AI Pilot to Production: Why Measurement Is the Decisive Factor](https://olakai.ai/blog/ai-pilot-to-production/) [The AI Visibility Audit: What You Can’t See Is Costing You](https://olakai.ai/blog/ai-visibility-audit/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Eu Ai Act Enforcement August 2026 Source: /blog/eu-ai-act-enforcement-august-2026 [← Back to Olakai's Blog](/blog/) # The EU AI Act Is Now Enforceable. Is Your AI Governance Ready? ![Compliance officer reviewing AI governance audit documentation under deadline pressure](https://olakai.ai/wp-content/uploads/2026/07/eu-ai-act-enforcement-august-2026.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. July 16, 2026 · [AI Governance](https://olakai.ai/blog/category/ai-governance/) On July 22, 2026, at 18:00 CEST, the window closes for AI providers to sign the EU’s Code of Practice on Transparency of AI-Generated Content. Eleven days later, on August 2, 2026, the obligations that Code was written to help with become enforceable anyway — whether a company signed or not. Article 50 disclosure requirements for chatbots and deepfakes go live, and the AI Office’s supervisory and fining powers over general-purpose AI providers activate. Fines reach €15 million or 3% of global annual turnover, whichever is greater. Most enterprise AI programs currently treat compliance as a project with a deadline. This one doesn’t have a deadline anymore. It has a start date. There’s a genuine amount of confusion circulating about which date matters and why, largely because two unrelated EU processes are landing in the same news cycle. It’s worth separating them, because they point enterprises toward very different actions. ## Two dates, two different clocks The first clock is the Code of Practice on Transparency of AI-Generated Content, and it closes July 22. Signing isn’t mandatory, but it isn’t free to skip either: signatories get a presumption of conformity with the transparency rules, which shifts the burden of proof onto regulators to show non-compliance rather than onto the company to prove compliance. Enterprises deploying assistive AI at scale — chat interfaces, generative content tools, customer-facing copilots — have a real decision to make here about whether that presumption is worth the commitment. The second clock, and the one that actually matters regardless of what anyone signs, is [Article 50](https://ai-act-service-desk.ec.europa.eu/en/ai-act/article-50), enforceable August 2, 2026. It requires that anyone deploying a chatbot or similar conversational AI disclose “you are talking to an AI” at the start of the interaction, in plain and accessible language, and that synthetic or deepfake content be labeled as such. This isn’t a footnote requirement. It touches every customer-facing assistive AI deployment an enterprise runs, and the general penalty ceiling under Article 99 — that same €15 million or 3% of global turnover — applies to non-compliance. The same date activates the AI Office’s supervisory and fining authority over general-purpose AI (GPAI) model providers. GPAI providers have technically been obligated since August 2025; what changes August 2 is that Brussels can now actually enforce it, and models released before that original date get until August 2027 to fully catch up. See the Commission’s own [GPAI provider guidelines](https://digital-strategy.ec.europa.eu/en/policies/guidelines-gpai-providers) for the underlying obligations. A third, entirely separate development has muddied the water further. The Digital Omnibus, which received final Council approval on June 29, 2026, defers high-risk AI system obligations out to December 2027. That’s a real and meaningful delay — but it applies to a different category of the Act. It does nothing to the Article 50 disclosure requirement or the GPAI enforcement powers landing August 2. If your compliance plan assumes the Omnibus bought you more runway on chatbot disclosure, it didn’t. Worth reading the actual [Council press release](https://www.consilium.europa.eu/en/press/press-releases/2026/06/29/artificial-intelligence-council-gives-final-green-light-to-simplify-and-streamline-rules/) rather than the secondhand summary making the rounds, because conflating these two threads is exactly how enterprises end up caught flat-footed on the wrong deadline. ## Why “we’ll handle it before August 2” is the wrong plan The instinct in a lot of legal and compliance teams right now is to treat this as a sprint: audit the chatbots, add a disclosure banner, check the box, move on. That instinct misses what regulators are actually going to ask for, which isn’t a banner — it’s evidence. Evidence that disclosure happened consistently, across every deployment, every time, not just on the interfaces someone remembered to check. Evidence that synthetic content got labeled before it shipped, not after a complaint. Evidence that policy enforcement is a standing, monitored practice rather than a one-time remediation before a known date. A disclosure banner added in July satisfies an audit that happens in July. It does not satisfy the audit that happens in October, after the banner quietly disappeared during a redesign nobody flagged. This is the structural problem with treating [AI governance](/ai-governance/) as a bolt-on project instead of an operating discipline. Enterprises running dozens of chatbots, copilots, and AI-enabled tools across departments can’t manually audit each one before a deadline and call it done — new tools get added, existing ones get reconfigured, and shadow AI keeps showing up in places IT never approved. Our own research into [shadow AI statistics](/blog/shadow-ai-statistics-2026/) shows just how much AI usage exists entirely outside sanctioned channels, which means it’s also outside whatever disclosure and labeling controls a compliance team thinks they’ve implemented. You can’t put a disclosure banner on a tool you don’t know is running. What actually holds up under regulatory scrutiny is a standing measurement layer — one that knows what AI tools are in use, whether disclosure requirements are being met at the point of interaction, and can produce an audit trail on demand instead of reconstructing one under pressure. This is precisely the gap Olakai was built to close. Olakai Assistive gives enterprises visibility into every chatbot, copilot, and employee-facing AI tool in use — including the shadow AI that never went through procurement — with governance and DLP controls built in as a Key Feature, not an afterthought. For engineering organizations, Olakai Agentic extends the same governance discipline to autonomous agents and AI coding tools, where disclosure and audit obligations are increasingly following the same logic as chatbot transparency rules. Both products are part of a single, vendor-neutral Enterprise AI Intelligence Platform, which matters here specifically because Article 50 and GPAI enforcement don’t stop at your own tooling — they extend to the vendor chain. If a third-party model provider embedded in your stack can’t demonstrate compliance, that exposure becomes yours too, and a governance approach that only covers tools your team built directly will miss it. ## What CISOs should actually be doing this month For security and compliance leaders, the practical sequence is straightforward even if the underlying work isn’t trivial. First, get a real inventory of every AI interface employees or customers touch, sanctioned and unsanctioned, because Article 50 doesn’t care whether IT approved the tool. Second, decide on the Code of Practice question before July 22 — sign for the presumption of conformity, or accept the higher evidentiary bar of proving compliance without it. Third, and most important, stop treating August 2 as a finish line. It’s the day enforcement starts, not the day the work ends; disclosure and labeling have to be continuously monitored and continuously provable, month over month, not demonstrated once and forgotten. Our [CISO governance checklist](/blog/ciso-governance-checklist/) walks through the broader set of controls this requires, and our [CISO use case page](/use-cases/ciso/) covers how policy enforcement and audit-ready evidence get built into day-to-day operations rather than bolted on before a deadline. The enterprises that treat August 2 as a governance maturity milestone — not a scramble — will be the ones who can answer a regulator’s questions in minutes instead of weeks. The ones still assembling their evidence trail after the fact will find that a banner slapped on in July doesn’t hold up to an audit in Q4. If your AI governance is still a spreadsheet and a promise, now is the time to change that. [Talk to an Expert](/schedule-a-demo/) [Shadow AI, Caught in the Act: Inside Olakai’s App Catalog and Policy Alerts](https://olakai.ai/blog/shadow-ai-app-catalog-policy-alerts/) [Even Google Can’t Ship Its Best AI](https://olakai.ai/blog/google-cant-ship-best-ai/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Four Agents 70 Percent Of The Return Source: /blog/four-agents-70-percent-of-the-return [← Back to Olakai's Blog](/blog/) # Four Agents Carried 70% of the Return ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. August 11, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) From the AI ROI Series, recorded 11 August 2026. Two headlines that week are worth translating into tokenomics. [Intel](https://www.intel.com) asked Wall Street for $15 billion and Wall Street handed over $20 billion, its first share sale since 1971, with the reason given in the filing being general corporate purposes. Then Mark Zuckerberg, in a manifesto about abundance, called compute finite and therefore carrying an opportunity cost, which is the line most people skipped. It is the same message from both. Compute gets more expensive from here, even while the unit price per token keeps falling on paper. Both of those are true, which is why the rate card is the wrong thing to watch. ## Waste rarely looks like failure We measure the value of AI, which means most of our time goes on measuring the waste, and the awkward thing about waste in an AI programme is that it usually looks like growth. Adoption climbs, usage climbs, and spend climbs with them, and everybody agrees it is going well. A rising bill on a good rollout and a rising bill on a wasteful one look identical from the outside. You cannot tell them apart without knowing what it costs to finish one piece of work, which is the number almost nobody has. ## Eighteen months, and the number nobody in the building had seen One of ours, no names. Eighteen months into an AI programme, adoption spreading, engineering shipping agents, coding proficiency already high, everybody pleased. Then around April the invoices started creeping as usage-based pricing kicked in. Not a spike, which somebody would have noticed. A bit more every month, always with a good reason attached, because more teams were using more of it. Somewhere upstairs, somebody finally asked what they were actually getting for it, which is the question we now get asked more than any other. They proved it themselves, as it happens, since they are proficient users and we only assisted with the initial lift. The thesis was what it cost them to finish one piece of work. Over those eighteen months, while spend tripled and adoption climbed, the cost of finishing one piece of work went up about 40%. Remember what the rate card was doing over the same period. Every model they used got cheaper per token, and the cost to finish a job still rose 40%. ## Then we split it by agent Which nobody had done either. They had dozens running. Four of them were carrying about 70% of the measurable return. The bottom third was consuming roughly 30% of the spend and returning almost nothing. Every one of those started life as an experiment, as it should have, and that is the pattern I think is defining enterprise AI right now. Teams were told to find AI use cases, engineering did what good engineering does and built agents, tried things, shipped them. Some worked brilliantly and some did not, and not one of them was ever switched off. They did not have an AI programme so much as dozens of experiments with a budget and no way to tell which was which. > We ended the bottom tier, moved that money into the four that were working, and they got next year’s growth out of this year’s envelope. The invoices stopped creeping. Which is the part worth keeping, because we are not here to save costs on enterprise AI. We are here to help organisations make the most out of it, and those are different jobs with different answers. This is directional and it is one company, so check it against your own estate rather than mine. ### A second example, from a different engagement An engineer built an agent to clean up a codebase. Not a production system, just a tool he wrote himself. He started it on a Thursday evening and went home for the weekend, and it ran for four days. About a quarter of the work it attempted actually finished. The rest was the same loop, over and over, on one file it was never going to fix, because nobody had told it when to stop trying. That came to $21,000 of compute across a long weekend, roughly three quarters of which bought nothing at all. Nobody noticed for four days, and it was not negligence. I know that team, and they are robust about process and QA. Even wasting three quarters of everything it touched, that agent was still cheaper than paying a person to do the same work, by a lot. Every dashboard in the building was green and the ROI was positive the entire time it was setting money on fire, which is exactly why it ran all weekend. The returns on agents are often good enough to hide almost any amount of waste and still show you a number you are happy with. This is not one clumsy tool, either. A paper published five days before we recorded priced every action and told agents their budget. The best one stayed inside it under 4% of the time, and doubling the budget barely changed the behaviour. Agents cannot manage their own money. Three fixes, none of them clever: give the agent a stopping rule, cache the part of the context that never changes, and stop sending every job to the most expensive model when a cheaper one finishes it. The cost of getting one job done fell by about seven eighths, for the same amount of work out of the other end. We did not make the agent smarter. We stopped paying for the work that never finished. ## Why this is a forecasting problem Those invoices began as a visibility problem, became a cost problem, and ended as a board question, in that order. Everyone I talk to has more agents planned for next year and nobody has fewer, and most are watching the same two numbers that company was, which will look excellent right up until somebody asks what they bought. That is survivable while compute is cheap. Intel just raised $20 billion because it will not be, and when that price moves it moves under all of it, including the agents returning nothing. Cheap tokens fund waste rather than fixing it, and at the moment they are funding the fabs too. So the question is not whether you will run more agents next year, because you will. It is whether you will be able to say which of them earned it, which is a question about [what your agents actually produced](/agent-iq/) rather than about how many you shipped. The same discipline that makes [a build-or-buy decision](/blog/build-or-buy-self-hosting-ai-cost/) answerable makes this one answerable, and it starts from the same place: a baseline you measured rather than assumed. ## Four questions to take into your next review 1. Do you know what is running in your enterprise? 2. Are you ready for what is next? 3. Are you ahead of the competition? 4. Are you making the most out of your tokens? Those are table stakes for every AI leader through to the CFO, and they need answering with data behind them rather than with confidence. If your answer to the first one is a list of tools rather than a list of outcomes, that gap is the same one behind [a falling rate card and a rising bill](/blog/your-ai-got-cheaper-your-bill-didnt/), and it is why [measured AI ROI](/ai-roi/) and [the metrics underneath it](/analytics-kpis/) are worth building before the next budget cycle rather than during it. I’m Paul, co-founder of Olakai. Measuring what AI actually costs and what it actually returns, on your own workload, is the work I spend my days on, and I am generally happy to be told I am wrong. [Schedule your AI evaluation](/schedule-a-demo/), and we will show you your own record, in your own environment. [Uber Blew Through a Year of AI Budget in Four Months. The Guardrail It Built Next Already Existed.](https://olakai.ai/blog/uber-ai-budget-blowout/) [The Cache Tax: Where DeepSeek’s Price Increase Is Concentrated](https://olakai.ai/blog/the-cache-tax/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Future Of Agentic Enterprise Toolkit Source: /blog/future-of-agentic-enterprise-toolkit [← Back to Olakai's Blog](/blog/) # The Enterprise Leader’s Toolkit for Navigating Agentic AI ![Enterprise leader exploring agentic AI analytics dashboard in modern corner office](https://olakai.ai/wp-content/uploads/2026/02/future-of-agentic-enterprise-toolkit-featured.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. February 25, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Last quarter, a CIO at a mid-market financial services firm told me something that stuck: “I have 14 browser tabs open right now—vendor whitepapers, analyst reports, a McKinsey deck from 2024, three Medium posts about agent architectures. None of them agree on anything, and none of them tell me what to actually *do* on Monday morning.” He’s not alone. According to [McKinsey’s 2025 State of AI survey](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai), 62% of organizations are experimenting with AI agents—but in any given business function, no more than 10% have actually scaled them. The gap between “we’re exploring agentic AI” and “we’re getting value from agentic AI” has become the defining challenge for enterprise leaders this year. ## The Practical Resource Gap The information problem isn’t a lack of content—it’s a lack of *useful* content. Vendor guides are biased toward their own platforms. Academic research is fascinating but rarely translates to a Monday morning action plan. And the consulting firms that produce genuinely practical frameworks charge $50,000 or more for the privilege of reading them. Meanwhile, Gartner predicts that [over 40% of agentic AI projects will be canceled by the end of 2027](https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027), citing escalating costs, unclear business value, and inadequate risk controls. Their analysts note that most agentic AI propositions today “lack significant value or return on investment, as current models don’t have the maturity and agency to autonomously achieve complex business goals.” When 40% of projects are headed for cancellation, the difference between success and failure often comes down to whether leaders had the right planning tools before they started. Enterprise leaders need something in between a sales pitch and an academic paper—practical, vendor-neutral resources that help them evaluate, plan, and govern agentic AI with clear eyes. That’s exactly what we built. ## Introducing Future of Agentic [Future of Agentic](https://futureofagentic.com/) is a free, comprehensive research site designed for enterprise leaders navigating agentic AI. No gating, no lead forms, no vendor spin. It’s the resource we wished existed when we started building Olakai—and the one we kept hearing customers ask for. Here’s what’s inside. ### A KPI Library Built for Business Leaders, Not Data Scientists One of the most common questions we hear is deceptively simple: “How do I know if my AI agent is actually working?” The [interactive KPI library](https://futureofagentic.com/success-kpis) provides 18 metrics across agentic, chatbot, and AI application categories—each with definitions, calculation methods, benchmarks, and guidance on when to use them. These aren’t abstract metrics. They’re the specific measurements that separate organizations scaling AI successfully from those stuck in [pilot purgatory](/blog/ai-pilot-to-production/). Think agent task completion rate, autonomous resolution percentage, and cost per automated decision—KPIs that connect directly to business outcomes your CFO will understand. ### ROI Calculators That Go Beyond Napkin Math Every enterprise leader considering [agentic AI](/blog/what-is-agentic-ai/) needs to answer two financial questions: What will this actually cost, and what happens when agents stop delivering value? The [Agent Economics section](https://futureofagentic.com/agentic-ai-101/agent-economics) includes two interactive calculators. The Agent TCO vs. FTE calculator models the real total cost of ownership—infrastructure, maintenance, monitoring, and iteration—against human equivalents over time. The Zombie Agent Cost calculator tackles a problem most vendors don’t want to discuss: the ongoing expense of agents that are deployed but no longer delivering meaningful results. Both tools produce shareable outputs, so you can bring data-backed projections to budget conversations instead of guesswork. ### Hundreds of Enterprise Use Cases, Sorted by What Matters The [use case library](https://futureofagentic.com/use-cases) catalogs hundreds of enterprise applications of agentic AI, each with architecture context and complexity ratings. What makes this different from a typical “top 10 use cases” listicle is the filtering: sort by department, by implementation complexity, or by business function to find the applications that match your organization’s maturity and priorities. Whether you’re a head of customer success exploring automated escalation workflows or a CISO evaluating security operations agents, the library narrows the field to what’s relevant. ### Governance Frameworks for the Enterprise, Not the Lab The [Deloitte State of AI 2026 report](https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html) found that only 21% of organizations have mature AI governance models in place—even as 38% are actively piloting AI agents. That governance gap is a ticking clock. The [governance section](https://futureofagentic.com/governance) on Future of Agentic provides risk assessment frameworks, compliance checklists, and decision-making guides built for enterprise reality. These aren’t theoretical policy templates — they complement our own [CISO governance checklist](/blog/ciso-governance-checklist/) and are structured around the actual decisions leaders face: What level of autonomy should this agent have? What happens when it fails? Who’s accountable? How do we audit it? ### An AI Readiness Quiz (30 Seconds to Your Roadmap) Sometimes the most valuable tool is the simplest. The AI readiness assessment takes about 30 seconds, asks targeted questions about your organization’s current AI maturity, and produces a customized roadmap with recommended next steps. It’s not a lead-gen funnel—it runs entirely in the browser and gives you immediate, actionable output. We’ve seen leaders use it to align executive teams on where they actually stand versus where they think they stand, which often turns out to be a more productive conversation than any strategy offsite. ### The Enterprise AI Unlocked Podcast Research and frameworks are essential, but there’s no substitute for hearing how other leaders are navigating these challenges in practice. [Enterprise AI Unlocked](/podcast/) features in-depth conversations with enterprise leaders and practitioners—from Fortune 500 AI playbooks to the real economics of voice AI deployments. Six episodes are live, with new conversations publishing regularly. Each episode is enriched with chapters and participant context so you can jump directly to the topics that matter most to you. ## Who This Is For We built Future of Agentic for the people making decisions about AI in their organizations: CIOs evaluating agent architectures, CISOs building governance frameworks, CFOs modeling [AI agent ROI](/blog/ai-agent-roi-lessons/), and Heads of AI or Data leading implementation. But it’s equally valuable for the product managers, directors, and team leads who need to build informed business cases and present them upward. Everything on the site is free and ungated—because we believe better-informed leaders make better decisions, regardless of whether they ever become Olakai customers. ## Where Olakai Fits Future of Agentic is the research and planning phase—understanding what’s possible, modeling the economics, and building a governance framework before you deploy. [Olakai](/platform/) is the execution and measurement phase—tracking ROI, governing risk, controlling costs, and securing AI usage once agents are live in production. The two are complementary by design: plan with Future of Agentic, then measure and govern with Olakai. ## Start Exploring If your team is navigating agentic AI decisions right now—or preparing to—[explore Future of Agentic](https://futureofagentic.com/). Start with the KPI library if you need measurement frameworks, the use case library if you’re evaluating where agents fit, or the readiness quiz if you want a quick pulse on organizational maturity. And when you’re ready to move from planning to production, [schedule a demo of Olakai](/schedule-a-demo/) to see how measurement and governance work in practice. [AI Metrics That Matter: What CFOs Actually Want to See](https://olakai.ai/blog/ai-metrics-that-matter/) [The 30-Day AI Pilot That Actually Proves Value](https://olakai.ai/blog/30-day-ai-pilot/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Gartner Ai Roi 28 Percent Source: /blog/gartner-ai-roi-28-percent [← Back to Olakai's Blog](/blog/) # Gartner: Only 28% of AI Projects Deliver ROI. Here’s Why the Rest Don’t. ![Abstract visualization of AI investment converging into measured ROI versus scattering into unmeasured fragments](https://olakai.ai/wp-content/uploads/2026/07/gartner-ai-roi-28-percent.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. July 12, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Gartner surveyed 782 infrastructure and operations leaders. Only 28% said their AI projects were fully meeting return-on-investment expectations. One in five — 20% — reported their AI initiatives had failed outright. The remaining majority sat somewhere in between: technically live, technically “in production,” and still unable to show the business a return anyone would call a win. That finding, published April 7, 2026, is one of the more sobering data points to come out of enterprise AI research this year — not because it’s shocking, but because it’s precise. [Gartner’s research](https://www.gartner.com/en/newsroom/press-releases/2026-04-07-gartner-says-artificial-intelligence-projects-in-infrastructure-and-operations-stall-ahead-of-meaningful-roi-returns) isn’t describing a handful of failed pilots. It’s describing the median enterprise AI program: deployed, adopted, budgeted for — and still unmeasured against the outcomes it was funded to deliver. ## A second Gartner study just took away the easiest excuse A month later, on May 5, 2026, Gartner published a companion release built on a separate survey — 350 executives at companies with more than $1 billion in revenue. It found that roughly 80% of organizations piloting or deploying autonomous AI report some form of workforce reduction. On its own, that stat fuels the standard board-level story: AI is cutting costs, headcount is coming down, the investment is paying for itself. Gartner’s data says otherwise. The rate of workforce reduction was [nearly identical between companies reporting high AI ROI and companies reporting flat or negative ROI](https://www.gartner.com/en/newsroom/press-releases/2026-05-05-gartner-says-autonomous-business-and-artificial-intelligence-layoffs-may-create-budget-room-but-do-not-deliver-returns). Layoffs happened either way. They just weren’t correlated with whether the AI actually worked. That single finding dismantles a narrative a lot of executive teams have been quietly leaning on. Cutting headcount around an AI rollout isn’t evidence of AI value — it’s a budget action that companies take whether or not the underlying technology is delivering. If your board is citing reduced headcount as proof your AI investment is working, Gartner’s own data says that proof doesn’t hold. The 28% of companies fully realizing ROI aren’t the ones who cut the most people. They’re the ones who can actually show what changed. ## The pattern isn’t unique to Gartner’s sample PwC’s 29th Global CEO Survey, published in January 2026, surveyed 4,454 CEOs across 95 countries and landed on a strikingly similar shape of problem. Fifty-six percent of CEOs report zero revenue or cost benefit from their AI investments to date. Only 12% report benefiting on both fronts — revenue and cost — at once. Two different research firms, two different survey populations, and the same structural story: a small minority of enterprises can point to AI value with confidence, and a majority cannot, despite comparable or larger spend. We’ve written before about a related but distinct data point — [the enterprise AI revenue gap](/blog/enterprise-ai-roi-gap-2026/) that Deloitte’s own research surfaced — and this Gartner/PwC pairing confirms it’s not an anomaly specific to one vendor’s survey methodology. It’s the default outcome when AI adoption outpaces AI measurement. What separates the 28% from everyone else is not a better model, a bigger budget, or a more ambitious use case. Gartner’s research points to something less exciting and far more fixable. Among the I&O leaders who reported failure, the dominant root cause was misaligned expectations — leadership assumed AI would immediately automate complex tasks or produce cost reductions on a timeline the technology was never going to meet. Among those who reported success, the top two factors were integrating AI into existing workflows rather than bolting it on as a parallel process, and securing full executive support before and during the rollout, not just at launch. Neither of those factors requires a different AI vendor. Both require a measurement layer that tells leadership, in real time, whether expectations and reality are converging or diverging. ## Why “run another pilot” isn’t the fix The instinctive response to a disappointing AI rollout is usually to relaunch it — a new pilot, a new vendor, a new proof of concept scoped more carefully this time. That instinct is understandable and, per Gartner’s own root-cause data, largely misdirected. The 72% of organizations not seeing full ROI don’t have a pilot problem; they have a visibility problem. They can’t see, in any unified way, which teams are using AI productively, which usage is idle license spend, where the workflow integration succeeded, and where it quietly reverted to the old process the day nobody was watching. Our own research on structured, time-boxed pilots — see [the 30-day AI pilot framework](/blog/30-day-ai-pilot/) — makes a version of this same point: the pilot itself isn’t usually the failure point. The failure point is what happens after the pilot, when nobody is instrumenting the rollout against a defined success bar. This is precisely the gap Olakai was built to close. Olakai is a vendor-neutral Enterprise AI Intelligence Platform — a measurement layer that sits above whatever AI tools, agents, and copilots an organization already has, rather than replacing any of them. It doesn’t require betting on a different model or ripping out an existing rollout. It requires instrumenting the AI that’s already live: which teams are using it, what outcomes it’s producing against the KPIs that actually matter to the business, and where the gap between expectation and reality is widening instead of closing. That’s the system of record enterprises are missing — and it’s exactly the layer that turns Gartner’s root-cause findings into an operating discipline instead of a postmortem. ## What this means for the CFO’s office For a CFO, these two Gartner releases together are close to a mandate. The first says most AI spend under your purview is not clearing the ROI bar the business case promised. The second closes off the one metric finance teams have been quietly using as a proxy for success — headcount reduction — because Gartner’s data shows that number moves the same way whether or not the AI is actually working. That leaves finance with a harder but more honest question: not “did we cut costs somewhere near the AI rollout,” but “can we show, tool by tool and team by team, what this specific AI investment returned.” Answering that question requires the same instrumentation an engineering leader would want for infrastructure spend — usage data, adoption data, outcome data, tied to the specific KPIs the board actually cares about, not vanity metrics like prompt volume or seat counts. We built [Olakai’s CFO use case](/use-cases/cfo/) around exactly that requirement, because “we reduced headcount” is not a board-defensible ROI answer anymore, and after May 5, 2026, most CFOs know it. None of this is really an argument against AI investment. Gartner’s own root-cause data says the fix is inexpensive relative to the AI spend itself: align expectations up front, integrate into existing workflows instead of running parallel processes, and keep executive sponsorship active past the launch date. The organizations getting this right aren’t spending dramatically more than the ones getting it wrong — they’re measuring more precisely. We saw a similar pattern in our review of [100+ AI agent deployments](/blog/ai-agent-roi-lessons/): the deployments that scaled were rarely the ones with the most sophisticated technology. They were the ones with a clear, agreed-upon definition of what success looked like before the rollout started, and a way to check that definition against reality every month, not just at the annual budget review. ## The choice in front of most enterprises right now Gartner’s numbers describe where most enterprises already are: 72% short of full ROI, 20% at outright failure, and a workforce-reduction number that no longer means what boards have been telling themselves it means. None of that is a verdict on AI technology. It’s a verdict on the absence of a measurement layer sitting between the AI tools an enterprise buys and the outcomes it’s actually able to prove. Our own [AI ROI page](/ai-roi/) lays out what that measurement discipline looks like in practice — the KPIs, the adoption tracking, the governance tie-in — because the fix Gartner’s research points to isn’t a new pilot or a headcount announcement. It’s visibility into the AI you already have. Enterprises now have a clear choice, and Gartner just made it a quantified one. Build the measurement layer now, while the gap between the 28% and everyone else is still closeable with better instrumentation rather than a strategy reversal — or keep operating on faith, keep citing headcount numbers the board can no longer treat as proof, and end up counted among the 72% a year from now when the next survey runs. Build this with Olakai, or explain to your board next year why your AI program is still one of the 72%. [Talk to an Expert](/schedule-a-demo/) [The CFO Just Walked Into the Coding Room](https://olakai.ai/blog/cursor-cfo-council/) [The AI Bill Is Eating Everything Else](https://olakai.ai/blog/ai-bill-eating-everything-else/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Google Cant Ship Best Ai Source: /blog/google-cant-ship-best-ai [← Back to Olakai's Blog](/blog/) # Even Google Can’t Ship Its Best AI ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. July 17, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) [TSMC](https://www.tsmc.com) bet another $100 billion this week. Google can’t ship its best model. And the evaluations that actually decide enterprise AI purchases just went private. Here is what the week is telling anyone managing an AI budget. What a week to read the room. A few AI stories underneath the market’s rough run tell you exactly where this is heading. The fast version first, then the one thing worth teaching. ## The boom is very real Earlier this month, IBM had its worst single-day stock drop since 1987 after warning that AI-infrastructure spending was diverting client budgets away from its own software and services lines — the AI bill eating into every other line of the budget, a pattern we track closely on [Olakai’s analytics and custom KPI](/analytics-kpis/) pages. That story kept going, and two new ones landed on top of it. First, TSMC, the company that actually makes the chips, had a monster quarter: profit up 77%, and it committed another $100 billion to factories in Arizona on top of what it had already pledged, taking its total US commitment to around $265 billion. Chairman C.C. Wei’s line: “AI-related demand continues to be extremely robust.” Nobody puts a quarter-trillion dollars into fabs unless they are very sure the demand is coming. But here is the part any AI budget owner should notice. TSMC also flagged that the memory shortage is now squeezing parts of the market that have nothing to do with AI. The same force that hit IBM. Chips up, memory up, and eventually the cost of everything running on top of them, up. The AI buildout has a downstream tax, and it is coming for procurement lines that have never touched an AI vendor. ## The story worth sitting on: Google [Google](https://www.google.com) delayed its flagship model again. Gemini 3.5 Pro slipped, the stock dropped, and Alphabet shed serious market value this week. Forget the model horse race for a second and look at why. The reported reasons were token efficiency problems and long-horizon task performance. Sit with that. One of the most capable AI labs on the planet is holding back its best model, in part, because it uses too many tokens. Even Google is fighting the efficiency battle, which is the exact thing we have been teaching for months in pieces like [how model routing turns token efficiency into a budget lever](/blog/model-routing-explained/). Token efficiency is becoming the frontier metric. Not just how smart a model is, but how much it burns to be smart. ## The lesson buried in the delay There is a second lesson in the Google story worth pulling out. Flagship launches now wait on something, and it is not a leaderboard. It is private buyer evaluations. Enterprises do not sign contracts on a public benchmark score, which is fascinating on its own — they run the model against their own data, in their own harness, and that harness never gets published. What that means for any AI budget owner: a leaderboard win that does not clear procurement is a press release. The score that matters is the one run against a company’s own workload. We made this same point about coding models in [the Cursor CFO Council breakdown](/blog/cursor-cfo-council/), where cost per request lies and cost per accepted line tells the truth. Same principle, bigger stage. The public number is marketing. A company’s own number is the truth, and the only way to have that number is to measure it — to get the visibility and the control over tokenomics that a vendor’s press release will never hand over. If proof that efficiency is where the game is now is needed, look back at the Cursor data broken down that same week: once caching is counted, output tokens were 0.6% of total usage, and without caching the bill would have run roughly ten times higher. Google is fighting the same fight at the model level that every enterprise is fighting at the usage level. Everybody, all the way up to Google, is learning that spending wisely beats spending big. ## What is coming next One more item, because it is the shape of what is next. [Nvidia](https://www.nvidia.com) launched Cosmos 3 Edge this week, a model for robots and physical AI, and Jensen Huang is calling physical AI the next frontier: factories, warehouses, healthcare robots. Here is why that is worth flagging now, not later. Every one of those is a brand-new category of AI consumption, tokens, compute, inference, running in the physical world, that nobody has a budget line for yet. A year ago, most finance teams were not thinking about tokens as a quarterly CFO line item. Now they are, per the CFO-facing shift covered in [what CFOs need from AI ROI reporting](/use-cases/cfo/). Physical AI is next. If tokenomics feels like a headache now, wait until the warehouse forklift has an inference bill. ## The read The boom is loud. TSMC just bet another hundred billion on it. But proof got quiet. Even Google cannot show its next model is efficient enough to ship, and the evaluations that actually decide purchases have gone private, inside each buyer’s own harness. Loud spending, quiet proof. The winners from here will be the enterprises that can measure their own AI, on their own workload, across every vendor in one place, and show what it returned — while everyone else is still reading someone else’s leaderboard. That gap between public marketing numbers and a company’s own verified return is precisely the space Olakai’s vendor-neutral measurement layer is built to close, whether the tool in question is a chatbot, a coding agent, or the next physical-AI deployment nobody has budgeted for yet. One question for the week: is your organization still trusting the leaderboard, or does it run its own evals? [**Talk to an Expert →**](/schedule-a-demo/) [The EU AI Act Is Now Enforceable. Is Your AI Governance Ready?](https://olakai.ai/blog/eu-ai-act-enforcement-august-2026/) [Your AI Got Cheaper. Your Bill Didn’t.](https://olakai.ai/blog/your-ai-got-cheaper-your-bill-didnt/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## How Olakai Detects Ai Usage Source: /blog/how-olakai-detects-ai-usage [← Back to Olakai's Blog](/blog/) # How Olakai Detects AI Coding Tool Usage Without Installing a Single Agent ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. April 29, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) The first objection a [CISO](/use-cases/ciso/) raises to almost any AI analytics pitch is some version of the same question: “so now I need to put another agent on every developer’s laptop?” It’s a fair question — security teams have spent years fighting endpoint sprawl, and a new mandatory install is a real cost even when the tool behind it is useful. For Olakai’s AI coding tool detection, the answer is no. Nothing runs on a developer’s machine. The entire signal comes from pull request data that already exists in GitHub, Bitbucket, or GitLab, read through the same organization-level credential used to power the [AI Impact Dashboard](/coding-iq/). That’s a meaningfully different trust posture than most AI monitoring tools, and it’s worth walking through exactly how the detection actually works — not as a black box, but as a stack of specific, checkable signals, with an honest fallback for the cases none of them catch. ## Three signals, applied in order Every merged pull request is checked against three detection methods. First, bot author detection: was the PR opened by a known bot account — dependabot, renovate, devin-ai, copilot-swe-agent, claude-code, sweep-ai, snyk-bot, and others recognized by GitHub’s own \`user.type === “Bot”\` flag? Second, commit co-author trailers: do any commits in the PR carry a `Co-authored-by:` line matching a known AI tool pattern, covering GitHub Copilot, Cursor, Claude Code, Devin, Amazon Q, and Gemini? Third, PR title and body markers: does the PR’s own text contain a recognizable phrase — “Generated by Cursor,” “claude-code,” “Copilot Workspace,” “Created by Devin,” and similar strings that AI tools leave behind by convention? If any one of those three signals fires, the PR is classified AI-assisted — and a single PR can be attributed to more than one tool if different commits carry different signals, which happens more than you’d expect on PRs where a human picks up and finishes AI-started work. A PR earns the stronger label of fully agentic specifically when a bot account opened it: the AI created the branch, wrote the code, and opened the PR itself, without a human author in the loop at all. That’s a real distinction, not a marketing one — a fully agentic PR is a different governance conversation than one where a human developer used AI as a fast collaborator. ## What happens when none of the three signals fire Not every AI-assisted PR leaves a clean marker. A developer might paste AI-generated code into a normal commit with no trailer, no bot account, and no mention in the PR body. For that gray area, Olakai runs an LLM classifier — Claude Haiku 4.5 — that reads the PR title, body, and a sample of the diff, and assigns a confidence score. Critically, the PR is only marked AI-assisted through this path at 60% confidence or higher; anything below that threshold is treated as “not detected,” specifically so ambiguous cases don’t get counted and inflate the AI-assisted total. That’s a deliberate design decision worth sitting with for a second: the system is built to under-count in ambiguous cases rather than over-claim. Most vendors pitching AI-coding-tool ROI have every incentive to inflate the “AI is helping” number — a bigger adoption percentage is a better story. Olakai’s classifier does the opposite by default, which is exactly the kind of choice that should show up in an [honest AI coding tool ROI metric](/blog/ai-coding-tool-roi-metrics/) instead of a vanity one. ## Multi-provider integrity, and where the fidelity differs Three source-control providers feed the same underlying pull-request table: GitHub, the production integration with the most mature detection signal set; Bitbucket Cloud, currently in beta; and GitLab, covering both SaaS and self-managed instances, using the same signal types plus GitLab-specific Duo markers. Every row is uniquely keyed by account, provider, repository, and PR number, so an organization running both GitHub and Bitbucket at once never gets a collided or double-counted PR — the two providers’ data sits cleanly side by side in the same analytics. Fidelity isn’t identical across providers, and Olakai says so rather than presenting every provider as equivalent. Bitbucket and GitLab have no first-class “review” object the way GitHub does, so review rounds and approvals are derived from each platform’s activity stream instead of native review submissions — directionally correct, but lower-fidelity than GitHub’s numbers. Any cross-provider cycle-time comparison should carry that caveat rather than treating a GitHub PR and a Bitbucket PR as measured on perfectly identical instruments. ## Whose PR is this, exactly? Detecting AI usage is only half the problem; attributing it to the right developer is the other half, and it’s less glamorous but just as important. Olakai resolves author identity through a four-step priority order: first, the commit email from a commit whose author matches the PR opener, excluding GitHub’s own noreply addresses; if that’s unavailable, the email from the PR’s first commit; failing that, the email on the GitHub user profile; and as a last resort, a constructed noreply fallback built from the GitHub login. That chain exists because real organizations have messy Git configuration — different emails on different machines, corporate SSO aliases, personal accounts used for a first commit — and per-developer [ROI attribution](/blog/ai-coding-tools-generating-value/) is worthless if it silently drops or misattributes a chunk of activity because someone’s \`git config\` didn’t match their Olakai account exactly. The same pull-request pipeline also captures signals beyond raw detection — PR size classification from XS to XL by lines added (used to flag AI PRs that are suspiciously always large, which the documentation itself calls a potential sign of rubber-stamping), issue linkage via `Fixes #N` or `Closes #N` references, first-pass approval rate, and a test-file ratio that the product is explicit about not being perfect: it’s based on file count, not line count, so a PR with one large test file and many small source files reads as low-coverage even when it isn’t. Publishing that limitation alongside the metric is the same pattern as the LLM classifier’s 60% confidence floor — under-claim rather than over-claim. Put together, the detection layer is the foundation everything else in [Olakai’s AI monitoring](/complete-ai-monitoring/) is built on — the PR mix, the cycle-time comparisons, the productivity score, all of it depends on getting “was this PR actually AI-assisted, and by which tool” right at the source, without asking a single developer to install anything. For a CISO evaluating the tool, that’s the actual pitch: the analytics run on data your organization already generates and already controls access to, not on a new agent asking for a new set of permissions. Want to see what this detection stack finds in your own repositories before rolling it out further? [Talk to an Expert](/schedule-a-demo/). [Meta’s $53B AI Capex Bet vs. 14,000 Layoffs: When the Market Stops Cheering](https://olakai.ai/blog/meta-layoffs-ai-capex-math/) [Anthropic’s Mythos Crisis: What a $900B Raise Tells Enterprise AI Buyers](https://olakai.ai/blog/anthropic-mythos-compute-trap/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Inside Ai Spend Governance Source: /blog/inside-ai-spend-governance [← Back to Olakai's Blog](/blog/) # Inside AI Spend Governance: Budgets and the Alerts That Fire Before You Blow Through Them ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. June 3, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) A [CFO](/use-cases/cfo/) rarely discovers an AI spend problem from a dashboard. They discover it from an invoice, weeks after the spending already happened, with no window left to do anything but ask engineering what happened. Olakai’s Budgets feature inside [AI Spend Governance](/coding-iq/) exists specifically to close that gap — not by promising a smarter forecast, but by being honest about what a budget actually is and firing an alert while there’s still time to act on it. ## Budgets are lenses, not a partition The single most important thing to understand about Olakai’s budgets is also the thing most competing tools obscure: budgets do not partition spend. They are independent, overlapping lenses over the same dollars. The same charge can count toward a developer’s budget, their department’s budget, the provider budget, and the program budget, all at once. If you add up every individual budget on the page expecting the total to match your program spend, it won’t — and that’s by design, not a bug to file a ticket about. Budgets are organized into four groups. Program is the master ceiling — every developer, every provider, every project rolled into one account-wide cap. Provider lets you cap a single vendor, like Anthropic or Cursor, without touching anyone else’s spend. Employee-centric budgets attribute spend to people — an individual developer, the persona they belong to, or their department — with the same overlap rule: a shared engineer’s spend counts fully against every relevant lens. Project groups shared service keys into named cost centers, with developers who belong to multiple projects contributing their full spend to each one rather than having it split proportionally. ## What budgets don’t track, and why that’s deliberate Budgets only track per-token, cost-bearing providers — Anthropic, Cursor, and OpenAI. GitHub Copilot is excluded outright, because its pricing is seat-based rather than usage-metered, and a per-token budget mechanism has nothing to measure against a flat subscription fee. Spend that can’t be attributed to a specific person still counts toward the program and provider totals, but drops out of the developer, persona, and department lenses entirely — which means per-entity totals can legitimately be lower than the program total, and that’s worth knowing before a CFO tries to reconcile the two and assumes something’s broken. The same per-provider precision shows up in how Olakai handles [Google Vertex AI cost data](/blog/google-vertex-ai-cost-reconciliation/), which lags behind usage by design rather than pretending to be real-time when it isn’t. Budgets extend naturally into Projects, Olakai’s term for a cost center: a named bucket that groups shared service API keys, member developers, and owned repositories under one monthly limit. Total project cost is service-key spend plus member-developer token spend, and — consistent with the overlapping-lens rule everywhere else — a developer who belongs to more than one project contributes their full token spend to each project they’re in, not a proportional split. Archiving a project unassigns its keys and removes its budget and alert rules, but never deletes the underlying spend history; only the grouping goes away. ## The forecast that’s already live, and the one that isn’t yet Every budget carries a Projected month-end figure built the same way: the recent daily spend rate, extended across the remaining days of the month, added to spend so far, with a confidence signal that reflects how steady daily spend has actually been. It’s a run-rate projection, not a model of growth or seasonality — a distinction Olakai is upfront about, the same way it’s upfront about the 30-day spend projection on the main [AI Impact Dashboard](/coding-iq/) being a straight-line extrapolation rather than a real forecast. Budgets are evaluated once daily, right after the cost-import job pulls fresh provider spend, and saving a budget automatically provisions the alert rules behind it — nothing extra to configure. Two kinds of alerts come out of that evaluation. The first is a threshold alert: actual month-to-date spend crosses a configurable percentage of the budget — 50%, 80%, or 100%. The second, and the more useful one, is a forecast alert: the run-rate projection is on track to exceed the limit by month end, even if the account isn’t over budget yet today. That second alert is the actual point of the feature — catching a trajectory early enough to still change it, rather than confirming after the fact that the month already went over. Worth being precise about scope here: Olakai also has a separate, standalone Forecasts tab planned for what-if scenario modeling across budget dimensions — a different, more ambitious feature for testing hypothetical spend trajectories before committing to them. As of this writing, that tab isn’t live yet. What’s covered above — the run-rate projection and the two alert types built directly into the Budgets page — is shipped and running today; the scenario-modeling tool is a separate thing worth revisiting once it ships. ## Why the overlap is the right design, not a shortcut It would be simpler to build budgets that partition spend cleanly — every dollar assigned to exactly one bucket, everything adding up neatly on a summary slide. It would also be wrong for how AI spend actually happens inside a real engineering org, where the same developer’s usage genuinely belongs to their department’s headcount planning, their manager’s persona-level benchmarking, the vendor contract renewal conversation, and the specific project that consumed it — four legitimate, simultaneous questions about the same dollar. Building four separate, non-overlapping ledgers to answer four different questions would mean picking one authoritative answer and getting the other three wrong. Overlapping lenses let all four questions get an honest answer from the same underlying spend data, at the cost of a program total that doesn’t equal the sum of its parts — which is exactly the tradeoff a CFO should want once it’s explained, rather than discovered while trying to make the numbers reconcile. It’s also worth knowing that budgets and projects aren’t limited to point-and-click configuration — [Kai can create, edit, and archive them conversationally](/blog/ask-kai-conversational-control-plane/), gated by admin permission and a confirmation step before anything actually changes, which is a useful shortcut when there’s a long backlog of unassigned service keys to triage. This isn’t a hypothetical risk — [Uber blew through an entire year of AI budget in four months](/blog/uber-ai-budget-blowout/) before building a reactive cap of its own. If your AI coding spend has outgrown a spreadsheet and a monthly Slack message from finance, [Talk to an Expert](/schedule-a-demo/) about setting up budgets against your own provider and project data. [AI’s $725B Capex Reckoning: Prove ROI or Get Cut](https://olakai.ai/blog/ai-capex-reckoning/) [Power, Casual, New, Idle: How Olakai’s Adoption Cohorts Find Your Wasted AI Licenses](https://olakai.ai/blog/adoption-cohorts-wasted-ai-licenses/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Jpmorgan Agentic Ai Rollout Source: /blog/jpmorgan-agentic-ai-rollout [← Back to Olakai's Blog](/blog/) # What JP Morgan’s Agentic AI Rollout to 250,000 Employees Tells Us ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. February 11, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) JP Morgan Chase just gave 250,000 employees access to AI — and then announced it was moving to agentic AI, where those systems won’t just answer questions but [execute complex multistep tasks autonomously](https://www.cnbc.com/2025/09/30/jpmorgan-chase-fully-ai-connected-megabank.html). The bank’s vision: “Every employee will have their own personalized AI assistant; every process is powered by AI agents.” It’s the largest enterprise AI deployment publicly disclosed. And it raises a question every enterprise will face as AI scales: can governance keep pace with adoption? ## The Numbers Behind the Rollout The scale is worth pausing on. JP Morgan’s LLM Suite — an internal AI platform built on models from OpenAI and Anthropic — is available to the bank’s entire workforce except branch and call center staff. Of the 250,000 employees with access, 150,000 use it weekly. Half of those use it daily. The platform is updated every eight weeks with new enterprise data, creating a continuously evolving tool that gets more capable and more embedded in daily work with each cycle. Behind the platform sit 2,000 AI and machine learning specialists — 900 data scientists, 600 machine learning engineers, and 200 AI researchers — managing 600-plus production use cases. That’s a specialist-to-user ratio of roughly 1:125. Even with one of the largest enterprise AI teams in the world, JP Morgan has far more AI usage than any team can manually oversee. And that’s before [agentic AI](/blog/what-is-agentic-ai/) enters the picture. ## From Assistive to Agentic: A Governance Leap There’s a fundamental difference between assistive AI and agentic AI from a governance perspective. Assistive AI — the copilots, chatbots, and summarization tools that make up most enterprise AI today — operates in a request-response pattern. An employee asks a question, the AI responds, the employee decides what to do with the response. The human is in the loop for every decision. Agentic AI operates differently. An AI agent receives a goal, decomposes it into subtasks, executes those subtasks autonomously (potentially across multiple systems), and delivers a result. The human may not be in the loop for intermediate decisions. The agent might query a database, analyze the results, draft a recommendation, and send it to a stakeholder — all without human review of each step. JP Morgan described itself as “early in the next phase” of deploying agentic AI in late 2025. The use cases in banking are obvious: automated fraud investigation that pulls transaction records, cross-references patterns, and generates suspicious activity reports. Loan underwriting that collects applicant data, runs credit analysis, and produces risk assessments. Compliance monitoring that scans communications, identifies potential violations, and initiates review workflows. Each of these use cases involves an AI system making decisions — or heavily influencing decisions — about sensitive data, regulated activities, and consequential outcomes. Governing these systems requires fundamentally different controls than governing a chatbot that summarizes meeting notes. ## The Governance Challenge at 250,000 Users Scale compounds every governance challenge. Consider the numbers: 150,000 weekly active AI users generating millions of interactions. Each interaction potentially involves customer data, financial records, proprietary strategies, or regulated communications. The average organization already experiences [223 data policy violations involving generative AI per month](https://www.kiteworks.com/cybersecurity-risk-management/ai-data-security-crisis-shadow-ai-governance-strategies-2026/). At JP Morgan’s scale, that number could be orders of magnitude higher. Then there’s the [shadow AI problem](/blog/shadow-ai-enterprise-risk/). Research shows that 9% of employees now switch between personal and enterprise AI accounts — up from 4% in the previous period. At 250,000 employees, 9% represents 22,500 potential data leakage vectors. These are employees whose company-approved tools don’t meet their needs for convenience or functionality, driving them to seek alternatives that lack enterprise security and privacy controls. And the challenge deepens as AI moves from assistive to agentic. With assistive AI, governance can focus on what employees share with AI tools — input monitoring. With agentic AI, governance must also cover what AI systems do autonomously — output monitoring, decision accountability, and cascading action controls. An agentic AI system that autonomously initiates a trade, approves a credit application, or files a regulatory report introduces governance requirements that most enterprises haven’t even begun to design. ## How JP Morgan Is Approaching Governance JP Morgan’s governance structure provides a template — though it’s one built with resources few organizations can match. **Platform centralization.** Rather than allowing AI tool sprawl, JP Morgan channeled adoption through a single platform (LLM Suite). This centralization creates a single point of governance: access controls, data policies, usage monitoring, and audit trails all flow through one system. The alternative — employees using dozens of independent AI tools — makes governance exponentially harder because each tool has different data handling practices, different security models, and different audit capabilities. **Model governance.** The LLM Suite uses models from multiple vendors (OpenAI and Anthropic), updated every eight weeks. This multi-model strategy diversifies vendor risk while the regular update cycle ensures the platform stays current. But it also means continuous governance validation — every update cycle requires re-evaluation of model behavior, security posture, and compliance alignment. **Use case-level oversight.** Each of JP Morgan’s 600-plus production use cases is individually tracked and governed. This granularity matters because the governance requirements for a document summarization tool are fundamentally different from the governance requirements for a fraud detection agent. Use case-level governance allows risk-proportional controls — more oversight for high-stakes, high-sensitivity applications and lighter controls for low-risk productivity tools. Our [AI risk heatmap approach](/blog/ai-risk-heatmap/) follows the same principle. **Dedicated governance functions.** JP Morgan maintains a Model Risk Governance function that assesses each AI use case and a Firmwide Chief Data Officer responsible for data governance, quality, and access control. These aren’t part-time responsibilities added to existing roles. They’re dedicated functions with the organizational authority to block or modify AI deployments that don’t meet governance standards. ## The Workforce Reality Jamie Dimon has been unusually candid about AI’s impact on employment. In February 2026, he acknowledged that [AI is already displacing workers](https://www.cnbc.com/2026/02/24/jpm-ceo-jamie-dimon-ai-reshaping-workforce-redeployment.html) at JP Morgan, stating the bank has “huge redeployment plans” to move affected employees to other roles. He’d previously called AI “possibly as transformational as the printing press, steam engine, computing, and the Internet.” JP Morgan isn’t alone. Bank of America, Citigroup, and Wells Fargo all projected lower headcounts for 2026, with AI-driven efficiency gains cited as a key factor. The financial services industry, with its high proportion of knowledge work and data-intensive processes, is one of the sectors most exposed to AI-driven workforce changes. This workforce dimension adds another governance requirement that most frameworks ignore: the responsibility to plan for displacement before it happens. JP Morgan’s approach — retrain and redeploy — is more thoughtful than many enterprises, but it still requires knowing which roles AI will affect, on what timeline, and at what scale. That knowledge comes from the same measurement infrastructure that proves AI ROI: if you can’t measure what AI is doing, you can’t project what it will change. ## What This Means for Other Enterprises Most organizations won’t deploy AI to 250,000 users overnight. But every organization scaling AI faces the same governance challenges JP Morgan faces — just at a different magnitude. The principles translate: **Centralize AI access before you lose control.** Once employees adopt dozens of independent AI tools, governing the ecosystem becomes nearly impossible. A platform strategy — even a modest one — creates the governance foundation that fragmented tool adoption destroys. **Design agentic governance before deploying agentic AI.** The gap between assistive AI governance (monitoring inputs) and agentic AI governance (monitoring autonomous decisions) is substantial. Organizations that deploy agentic AI with assistive-era governance will face accountability gaps when agents make consequential decisions without human oversight. **Treat shadow AI as inevitable, not preventable.** At any scale, some employees will use unauthorized AI tools. The governance response should be detection and redirection (identifying unauthorized tools and channeling users to approved alternatives), not just prohibition. Prohibition fails at scale because it creates friction that drives adoption underground. **Start workforce planning now.** If AI is going to change roles at your organization — and it will — the time to plan for redeployment is before displacement occurs, not after. That planning requires visibility into where AI is being used and how it’s changing work patterns — data that only comes from measurement infrastructure. JP Morgan’s rollout is a preview of what every enterprise AI program will eventually face: the governance challenge of AI operating at scale, across an entire workforce, with increasing autonomy. The question isn’t whether your organization will get there. It’s whether your governance infrastructure will be ready when it does. For a deeper look at the frameworks and readiness assessments for agentic AI deployment, [Future of Agentic’s governance hub](https://futureofagentic.com/governance) provides comprehensive resources. And [our CISO governance checklist](/blog/ciso-governance-checklist/) offers a practical starting point for security leaders. **Scaling AI and need governance that scales with it?** [Talk to an expert](/schedule-a-demo/) to see how Olakai provides [unified AI governance](/ai-governance/) across assistive and agentic AI — before risk scales with adoption. [JP Morgan Spent $2B on AI. Here’s What They Measured.](https://olakai.ai/blog/jpmorgan-ai-measurement/) [Voice AI in the Enterprise: From Call Centers to Revenue Impact](https://olakai.ai/blog/enterprise-voice-ai/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Jpmorgan Ai Measurement Source: /blog/jpmorgan-ai-measurement [← Back to Olakai's Blog](/blog/) # JP Morgan Spent $2B on AI. Here’s What They Measured. ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. February 11, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) In February 2026, Jamie Dimon made a claim that most enterprise leaders can only dream of: JP Morgan Chase’s $2 billion AI investment had [“paid for itself.”](https://finance.yahoo.com/news/jamie-dimon-declares-jpmorgan-chases-[redacted].html) Not “shows promise.” Not “is on track.” Paid for itself — $2 billion in measured benefits offsetting $2 billion in costs. Break-even isn’t a moonshot return. But in a landscape where only 20% of enterprises can prove AI drives any revenue at all, the fact that JP Morgan can put a dollar figure on AI’s contribution — and defend it to shareholders — puts them in rare company. The question worth studying isn’t how much they spent. It’s what they measured, and how. ## The Scale of the Bet JP Morgan’s $2 billion annual AI investment sits within a $17 billion technology budget that grew to $19.8 billion in 2026 — a 10% increase year over year. The bank employs more than 2,000 AI and machine learning specialists, including 900 data scientists, 600 machine learning engineers, and 200 AI researchers. This isn’t a skunkworks team running experiments. It’s a division-level commitment that treats AI as core infrastructure alongside payment systems and risk controls. The centerpiece is the LLM Suite, an internal platform built on models from OpenAI and Anthropic that 150,000 employees use weekly. The platform is updated every eight weeks with new enterprise data, and by late 2025, roughly half of all LLM Suite users were using it daily. At that adoption rate, AI interactions at JP Morgan generate measurement data at a scale most enterprises can’t match. But scale alone doesn’t prove value. What makes JP Morgan’s approach worth studying is that they measure AI at the use case level — not just at the platform level. Every one of their 600-plus production AI use cases has specific metrics tied to specific business outcomes. ## What They Actually Measured JP Morgan’s AI measurement spans four categories, each connecting AI activity to a different type of business outcome: **Time recovery.** The most widely cited metric: LLM Suite users report saving an average of four hours per day. At 150,000 weekly users, that’s potentially 600,000 hours per week of recovered employee time — time that gets redirected to higher-value work, client engagement, and analysis that was previously crowded out by routine tasks. Time recovery is the most accessible AI metric because it’s easy to measure and easy to understand, but it’s also the most dangerous if not connected to downstream outcomes. Four hours “saved” only creates value if those hours are deployed productively. **Cost reduction.** The COiN (Contract Intelligence) platform provides the clearest cost reduction case study. Before AI, JP Morgan’s legal team manually reviewed commercial loan agreements — a process that consumed approximately 360,000 hours annually. COiN now reviews 12,000 documents in seconds rather than weeks, reducing legal operations costs by 30% and cutting compliance errors by 80%. The cost reduction is measured against a known baseline (manual review hours and error rates), making the calculation straightforward and defensible. **Revenue impact.** JP Morgan’s AI trading algorithms illustrate revenue-side measurement. The bank reported that AI-driven trading systems improved win rates from 52% to 63% and saved $25 million in slippage costs. Revenue impact is harder to measure than cost reduction because attribution is more complex — markets move for many reasons, and isolating AI’s contribution requires careful methodology. But JP Morgan’s approach of measuring specific trading performance metrics (win rate, slippage) rather than aggregate revenue provides a more defensible attribution model. **Value creation mapping.** Across customer personalization, trading, fraud detection, and credit decisioning, JP Morgan identified $1 to $1.5 billion in value creation from AI. This portfolio-level view is what enables the “$2 billion investment paid for itself” claim — it aggregates use case-level measurements into an enterprise-wide picture that can be presented to shareholders. ## Lessons for the Rest of Us Most enterprises aren’t JP Morgan. They don’t have 2,000 AI specialists or a $17 billion technology budget. But the measurement principles that underpin JP Morgan’s ability to claim ROI are applicable at any scale. **Measure at the use case level, not the platform level.** JP Morgan doesn’t report a single “AI ROI” number derived from aggregate spending and aggregate benefits. They track 600-plus individual use cases, each with defined metrics. This granularity is what makes the portfolio-level claim credible — it’s built bottom-up from measured outcomes, not estimated top-down from spending. Even an enterprise with five AI use cases can apply this discipline: define the success metric for each use case, measure it against a baseline, and report results individually before aggregating. **Track multiple metric categories.** Time recovery alone doesn’t prove ROI. Cost reduction alone doesn’t capture the full picture. Revenue impact alone is too hard to attribute without supporting data. JP Morgan tracks all four categories (time, cost, revenue, value creation) and presents them together. This multi-dimensional view is more credible to boards and CFOs than any single metric, because it demonstrates that the organization has instrumented AI measurement comprehensively. **Build governance alongside measurement.** JP Morgan’s Model Risk Governance function and Firmwide Chief Data Officer aren’t separate from AI measurement — they’re integral to it. Governance forces the organization to define what each AI system does, which creates the accountability structure that measurement requires. As we’ve seen across [100-plus AI agent deployments](/blog/ai-agent-roi-lessons/), the enterprises with the strongest ROI data are the ones with the most rigorous governance frameworks. **Treat AI as infrastructure, not R&D.** JP Morgan reclassified AI from an innovation investment to core infrastructure — the same category as payment processing and risk management. This shift has measurement implications: infrastructure has uptime, performance, and cost-efficiency metrics that are reviewed continuously, not evaluated in quarterly innovation reviews. When AI becomes infrastructure, measurement becomes operational rather than experimental. ## The ServiceNow Parallel JP Morgan isn’t the only enterprise betting big on AI measurement. [ServiceNow’s AI business](/blog/servicenow-ai-acquisitions-governance/) reached $600 million in annual contract value in 2025 and expects to exceed $1 billion by the end of 2026. Like JP Morgan, ServiceNow measures AI at the product level — tracking adoption, usage patterns, and customer value creation for each AI capability rather than reporting a single aggregate number. The pattern is consistent across enterprises that prove AI ROI: measurement happens at the individual use case or product level, governance provides the accountability structure, and results are aggregated into a portfolio view for executive and board reporting. The enterprises stuck in pilot purgatory do the opposite — they measure at the platform level, lack governance infrastructure, and can’t connect aggregate spending to specific outcomes. ## What This Means for 2026 McKinsey projects that AI could unlock $200 to $340 billion annually in value for [financial services](/industries/financial-services/) alone, and the industry is responding — more than 70% of financial institutions were using AI at scale by late 2025, up from 30% in 2023. But the gap between “using AI” and “proving AI ROI” remains wide. JP Morgan is one of the few financial institutions that can put specific dollar figures on specific AI outcomes. The lesson isn’t that enterprises need to spend $2 billion. It’s that the measurement infrastructure JP Morgan built — use case-level tracking, baseline metrics, multi-category measurement, governance integration — is what enables the ROI claim. That infrastructure can be built at any scale, for any number of AI initiatives. The cost of building it is a fraction of the cost of running AI without it. If your organization is investing in AI but can’t answer “what’s the return?” with specific numbers, the problem isn’t your AI. It’s your measurement. [Our AI ROI framework](/blog/ai-roi-framework/) provides the methodology, and [Olakai’s platform](/ai-roi/) provides the instrumentation to track AI value the way JP Morgan does — at the use case level, against baselines, across time, cost, revenue, and risk. **Ready to measure your AI like JP Morgan?** [Talk to an expert](/schedule-a-demo/) and we’ll show you how enterprises track AI ROI across every initiative — without needing a $17 billion technology budget. [What 100+ AI Agent Deployments Taught Us About Proving ROI](https://olakai.ai/blog/ai-agent-roi-lessons/) [What JP Morgan’s Agentic AI Rollout to 250,000 Employees Tells Us](https://olakai.ai/blog/jpmorgan-agentic-ai-rollout/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Measure Ai 2026 Resolution Source: /blog/measure-ai-2026-resolution [← Back to Olakai's Blog](/blog/) # Your Most Important 2026 Resolution: Measure Your AI ![Diverse team of business executives reviewing AI metrics on display - AI ROI measurement for 2026](https://olakai.ai/wp-content/uploads/2026/01/featured-post-1292.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. January 7, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Forget the gym membership. Here’s the 2026 resolution that will actually transform your organization. Every January, leadership teams gather to set priorities for the year ahead. They review budgets, realign strategies, and make bold promises about what they’ll accomplish. But if your organization launched AI initiatives in 2024 or 2025, there’s one resolution that matters more than all the others: this year, you’re going to measure what your AI is actually doing. It sounds simple. It isn’t. According to [Gartner](https://www.gartner.com/en/newsroom/press-releases/2024-07-29-gartner-predicts-30-percent-of-generative-ai-projects-will-be-abandoned-after-proof-of-concept-by-end-of-2025), at least 30% of generative AI projects were abandoned after proof of concept by the end of 2025—not because they failed, but because teams couldn’t demonstrate clear business value. The AI worked. The measurement didn’t. ## The Pilot Purgatory Problem If you’ve been in enterprise technology for any length of time, you’ve seen this movie before. A promising technology emerges. Teams rush to experiment. Pilots launch across departments. And then… nothing. The pilots keep running, but they never scale. They become permanent experiments, consuming budget and attention without ever delivering the transformation they promised. AI has accelerated this pattern dramatically. The barrier to launching an AI pilot is lower than ever—a team can spin up a chatbot or copilot integration in days. But the barrier to proving that pilot’s value remains stubbornly high. When the CFO asks “What’s the ROI on our AI investment?”, most teams can only offer anecdotes and assumptions. This is pilot purgatory, and it’s where AI initiatives go to languish. A recent [industry analysis](https://www.informatica.com/blogs/the-surprising-reason-most-ai-projects-fail-and-how-to-avoid-it-at-your-enterprise.html) found that on average, only 48% of AI projects make it into production, and it takes 8 months to go from prototype to production. The problem isn’t the technology. It’s the inability to answer the fundamental question: is this working? ## Why 2026 Is Different The pressure to prove AI value has never been higher. After two years of experimentation, boards and executive teams are demanding results. They’ve seen the hype. They’ve approved the budgets. Now they want to know what they got for their investment. Meanwhile, AI capabilities are advancing rapidly. Agentic AI—systems that can autonomously plan and execute complex tasks—is moving from research labs to production environments. Organizations that can’t [measure the value of their current AI deployments](/ai-roi/) will struggle to make informed decisions about these more sophisticated (and more expensive) capabilities. The teams that figure out measurement in 2026 will scale their AI programs. The teams that don’t will watch their pilots slowly fade away, replaced by the next wave of experiments that also never prove their worth. ## Five Measurement Commitments for 2026 Making “measure AI” a meaningful resolution requires specific commitments. Here’s what the teams that escape pilot purgatory actually do differently. First, they track outcomes, not just usage. Knowing that 500 employees used your AI assistant last month tells you almost nothing. Knowing that those employees resolved customer issues 23% faster, or processed invoices with 15% fewer errors—that’s actionable intelligence. The shift from counting interactions to measuring business impact is the single most important change most organizations need to make. Second, they tie AI to existing [business KPIs](/analytics-kpis/). Your organization already measures what matters: revenue, costs, customer satisfaction, employee productivity, error rates, cycle times. [Effective AI measurement](/blog/ai-roi-framework/) connects AI usage to these existing metrics rather than creating a parallel universe of AI-specific vanity metrics. When you can show that teams using AI tools have 18% higher customer satisfaction scores, you’ve made the business case. Third, they monitor costs proactively. AI costs can spiral quickly—API calls, compute resources, vendor subscriptions, integration maintenance. Teams that measure well know their cost per outcome, not just their total spend. They can answer questions like “How much does it cost us to resolve a customer issue with AI assistance versus without?” This kind of granular cost visibility is essential for making scaling decisions. Fourth, they document what’s working and what isn’t. The value of AI measurement isn’t just in proving ROI—it’s in learning. Which use cases deliver the highest value? Which teams have figured out how to get the most from AI tools? Which integrations consistently underperform? Organizations that systematically capture these insights can make smarter decisions about where to invest next. Fifth, they build the case for scaling incrementally. The path from pilot to production isn’t a single leap—it’s a series of gates, each requiring evidence that the AI is delivering value. Teams that measure well can show steady improvement over time, building confidence with stakeholders and earning the resources needed to expand. ## How to Actually Keep This Resolution Unlike most New Year’s resolutions, measuring AI doesn’t require willpower—it requires infrastructure. You need systems that capture AI usage data, connect it to business outcomes, and present it in ways that executives and finance teams can act on. This is where many organizations stumble. They try to build measurement capabilities from scratch, cobbling together logging tools, custom dashboards, and manual reporting processes. The result is fragile, incomplete, and almost never maintained once the initial enthusiasm fades. The more sustainable approach is to implement purpose-built AI intelligence platforms that handle measurement automatically. These platforms integrate with your existing AI tools—chatbots, copilots, agent frameworks, AI-enabled SaaS—and provide unified visibility into usage, outcomes, and costs across all of them. Olakai, for example, was built specifically to solve this problem: giving enterprises the data they need to prove AI value and make confident scaling decisions. ## The Payoff Teams that measure scale. Teams that don’t stay stuck in pilot purgatory indefinitely. It’s that simple. When you can show the CFO exactly how much value your AI initiatives are delivering—in terms they understand, tied to metrics they already care about—you transform the conversation. You move from defending your AI budget to advocating for expansion. You shift from “we think this is working” to “here’s the data proving it works.” More importantly, you give your organization the information it needs to make smart decisions about AI. Not every pilot should scale. Not every use case delivers value. Measurement lets you distinguish the winners from the losers and concentrate resources where they’ll have the greatest impact. 2026 will be the year that separates the organizations that figured out AI from the ones still experimenting. The difference won’t be which AI tools they chose or how sophisticated their implementations were. It will be whether they could prove their AI was working—and use that proof to build something lasting. That’s a resolution worth keeping. Ready to start 2026 with visibility into your AI investments? [Talk to an expert](/schedule-a-demo/) to see how Olakai measures AI ROI across your entire organization. [AI Predictions for 2026: What Enterprise Leaders Need to Know](https://olakai.ai/blog/ai-predictions-2026/) [Shadow AI: The Enterprise Risk Hiding in Plain Sight](https://olakai.ai/blog/shadow-ai-enterprise-risk/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Meta Layoffs Ai Capex Math Source: /blog/meta-layoffs-ai-capex-math [← Back to Olakai's Blog](/blog/) # Meta’s $53B AI Capex Bet vs. 14,000 Layoffs: When the Market Stops Cheering ![Massive teal data center campus dwarfing a small dim grid of office buildings — AI capex absorbing scale](https://olakai.ai/wp-content/uploads/2026/05/meta-layoffs-ai-capex-math.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. April 28, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) I closed the layoff trilogy last week with Disney. The plan for the next installment was to step back from headline reactions and get back to measurement frameworks that actually move enterprise decisions, because that is where Show Me The Math belongs. Then [Meta](https://about.meta.com/) dropped a memo on Thursday afternoon, and the math demanded one more episode. ## The announcement Meta will lay off approximately 8,000 employees on May 20, 2026, and close another 6,000 open roles, for a total of 14,000 careers affected by a single Thursday afternoon memo from Chief People Officer Janelle Gale. The memo does not mention AI once and explains the decision as an effort to run the company more efficiently and to offset other investments Meta is making. The other investments are not subtle. ## The capex picture In its January 2026 earnings report, Meta guided 2026 capital expenditures to a range of $115 billion to $135 billion. In 2025, the actual figure was $72.2 billion. Taking the midpoint of the 2026 guidance at $125 billion, the year-over-year increase is approximately $53 billion, with the spending going toward AI infrastructure, data centers, custom chips, and the company’s superintelligence research lab, which has been writing widely-reported nine-figure compensation packages for top researchers. For scale, Meta also disclosed total 2026 expense guidance of $162 billion to $169 billion, which means the capex line alone is now nearly the size of the entire operating expense base. ## The savings picture Meta has not publicly disclosed the average fully-loaded cost of an employee, so we have to work with estimates. Using a generous figure of $400,000 per head for a workforce concentrated in the Bay Area and dominated by engineers, the 8,000 layoffs translate to approximately $3.2 billion in annual payroll savings, and including the 6,000 closed open roles at the same blended rate brings the total avoided cost to roughly $5.5 billion per year. That figure represents about 10 to 11 percent of the year-over-year capex increase. In other words, even if every dollar saved from headcount reductions and unfilled roles were redirected to capex, it would not cover one-tenth of the new AI spend. The remainder has to come from somewhere, and in Meta’s case that somewhere is the advertising business, which generated $59.89 billion in revenue in Q4 2025 alone, up 24 percent year over year. So when the memo says efficiency, what it actually means is that the AI bill is bigger than the savings, someone still has to pay the difference, and the advertising business is paying it. The buffer in that equation is human beings. ## The market reaction is the real story This is the part I did not expect, and it is the part that motivated me to write at all. Meta shares were down approximately 2 percent in afternoon trading on Thursday, broadly tracking the market, and by Friday the stock had recovered most of the move. Effectively flat. Two or three years ago, a layoff memo of this scale paired with an efficiency narrative would likely have driven a multi-billion-dollar bump in market capitalization by the closing bell, and the 2022 Year of Efficiency framing added meaningful value to Meta’s stock at the time. It worked then. Today, the market shrugged. That muted response is arguably the most important signal in the entire story. I noticed the same thing with Disney earlier in the week. Investors have now watched the same playbook executed by Block in February (40 percent), Snap earlier this month (16 percent), Oracle in waves through last quarter, [Amazon](https://www.aboutamazon.com/)‘s 16,000 cuts in January, and on the same Thursday afternoon as Meta, [Microsoft](https://www.microsoft.com/) offered voluntary buyouts to roughly 8,750 US employees. The layoff-funds-AI memo is no longer news, it is a quarterly ritual, and when the market stops rewarding the action, the action stops being a strategy. It becomes a tax. This is the signal [Olakai](/platform/) was built for. When the market stops accepting “we built it” and starts demanding “show me what it returned,” the gap between AI investment and measurable AI outcome becomes the most important number in your finance stack. The companies that close that gap before the next earnings call will not need a layoff memo to balance the AI capex line. The companies that do not close it will keep funding AI by subtracting people, and the market has now told them, in the most polite way possible, that the trick has stopped working. ## What this means for your AI ROI math Show Me The Math is a financial discipline at its core, and the discipline only works if it includes the full picture. For enterprise leaders watching this play out at the trillion-dollar scale, the buyer-side translation is direct: the largest, most well-capitalized AI spenders on earth cannot make their own AI capex math work without dipping into headcount and ad revenue, which means the assumption that AI investment self-funds through measurable productivity gains is being stress-tested in public, and the results are not yet conclusive. That is exactly the gap Olakai exists to close. We are the vendor-neutral [Enterprise AI Intelligence Platform](/platform/) — the system of record that sits across every AI agent, copilot, and embedded tool in your stack, telling you what each one costs, what it returns, and where the unit economics actually break even. The thesis at trillion-dollar scale is the same as the thesis at enterprise scale: AI does not pay for itself by default, it pays for itself when you can measure it. Without that measurement layer, every CIO and CFO is running the same script Meta just ran in public, except with smaller numbers and less margin to absorb the miss. The four-step playbook keeps applying. [See](/blog/ai-visibility-audit/) what your AI is actually doing across the stack, including the shadow AI you do not yet know is running. [Measure](/blog/ai-metrics-that-matter/) the metrics that matter to a CFO — cost per task, completion rate, revenue impact, cycle-time reduction — not the activity metrics that look good on an internal dashboard. [Decide](/blog/30-day-ai-pilot/) within 30 to 60 days whether a pilot is generating the unit economics it promised, because every quarter you spend funding an unverified deployment is a quarter you cannot redirect to one that works. [Act](/blog/enterprise-ai-roi-playbook/) on what the data tells you, including killing pilots that are not delivering. That is the entire [AI ROI playbook](/ai-roi/), and it scales from a single agent in a single department to the $125 billion capex line at Meta. ## The human factor When 14,000 careers at one company are called off in a single afternoon, the line items on the income statement do not capture what is actually moving. There are mortgages, school enrollments, visa statuses on different and more urgent timelines than the headlines suggest, and partners with their own careers in the same compressed labor market. That is a lot of real lives compressed into a 27-day countdown to May 20. The severance package is comparatively generous and worth saying so honestly: 16 weeks of base pay, two additional weeks per year of service, and 18 months of healthcare coverage for US employees. That cushion matters and is better than most. But severance is a parachute, not a destination, and severance is not a strategy. The cost of living is at multi-year highs, the tech hiring market has been compressed for two years, and the carry cost of being between roles in 2026 is materially higher than it was during the 2022 wave. The people receiving an email on May 20 are entering a labor market where the same pattern is being repeated by the very companies they would naturally apply to next. This is the part the math does not capture, and it is the part that matters most. ## What to do with this If you are a CFO, the Meta memo is your future-state preview. AI capex is going to eat budget you did not know was edible, and the answer is not to wait for Q3 surprises but to audit your AI spend against measurable outcomes now, ideally in the same quarter you read this. Olakai’s [CFO use case](/use-cases/cfo/) walks through the specific framing — what to measure, what to ignore, and what a board-ready AI ROI scorecard actually looks like. If you are a sales or operations leader, the question is not whether AI replaces your team. The question is whether the AI you are already paying for is actually moving unit economics, or just adding another seat license to your stack. Map every AI tool to a measurable outcome before the next renewal cycle, because the measurement gap is what makes the layoff-funds-AI playbook so easy to default to. Olakai’s job is to surface that mapping automatically, so the renewal conversation starts with data rather than vibes. If you are an individual contributor watching this, the response is not panic, it is leverage. Innovate. Use AI to make your own work better. Become the person on the team who shows up with measurable output the rest of the team cannot match. The era in which headcount equaled value is closing, and the era in which measurable, accountable AI value defines organizational worth is opening. Both eras are tough, and the second one is at least one we can prepare for. ## The next episode and the bigger picture I will stress-test the Meta capex bet directly in a future installment, once there is more public information to work with. Q1 2026 earnings drop on April 29, and that call should give us something concrete to model: at what level of AI-driven revenue growth or operational savings does the $53 billion year-over-year increase actually break even, and what does Meta need to show to justify the cost the workforce is being asked to absorb? The short preview is that the spreadsheet does not yet justify the memo, and whether it will is the question Meta has to answer to investors next week, and to the 14,000 people whose lives have already been answered for them. The bigger picture is the one Olakai keeps pushing on every guest who comes on the podcast and every CFO we talk to: AI investment without an intelligence layer underneath it is a bet on faith, and faith is the most expensive form of capex on the books. Foundation first, measurement before scaling, governance that extends to your vendor chain, and an honest scorecard that survives a board review. Build that, and the next AI capex decision is grounded in data your CFO can defend. Skip it, and the only lever left is the one Meta just pulled. If you want help building that measurement and governance foundation before your own capex math forces a memo of its own, [talk to an Expert](/schedule-a-demo/). And if you want the longer-form conversations behind the analysis, the [Enterprise AI Unlocked podcast](/podcast/) goes deeper than the weekly Show Me The Math notes. [The Return of the Desktop App: And the AI Measurement Gap It Creates](https://olakai.ai/blog/desktop-renaissance-ai-measurement-gap/) [How Olakai Detects AI Coding Tool Usage Without Installing a Single Agent](https://olakai.ai/blog/how-olakai-detects-ai-usage/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Model Routing Explained Source: /blog/model-routing-explained [← Back to Olakai's Blog](/blog/) # How to Be a Smarter Token Manager: Model Routing, Explained ![Abstract visualization of AI model routing across layered network tiers](https://olakai.ai/wp-content/uploads/2026/07/model-routing-hero.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. July 2, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Two weeks of writing about AI token economics kept leading to the same corner: you cannot control what the vendors charge, only how wisely you spend it. That is the entire case for model routing, and this week it got a perfect teaching example. On Tuesday, [Anthropic](https://www.anthropic.com/news/claude-sonnet-5) launched Claude Sonnet 5, and the whole pitch fit in one sentence: near-Opus performance, at a fraction of the price. Sit with that for a second, because that sentence is the entire case for routing, stated by a frontier lab about its own model lineup. ## How the pricing actually works You pay per token, split into input (what you send) and output (what the model writes back), and output is the expensive side, usually about five times the input rate. Here is the current Claude ladder, per million tokens. Model Input Output Notes Haiku 4.5 $1 $5 Fastest, cheapest current tier Sonnet 5 (intro) $2 $10 Through Aug 31, 2026 Sonnet 5 (standard) $3 $15 After Aug 31 Opus 4.8 $5 $25 Premium, the common default Fable 5 $10 $50 Top tier, twice Opus Two more levers sit on top of that ladder: batch processing takes 50% off, and prompt caching takes up to 90% off the input you reuse. Keep both in your back pocket. Now, Sonnet 5 specifically. Anthropic’s own benchmark numbers put it close to Opus 4.8, roughly 63 versus 69 on agentic coding and basically tied on knowledge work, at about 40% of the price. That is the headline, and it is real. Here are the two things worth checking before anyone lets a vendor’s pricing banner do the talking. Sonnet 5’s new tokenizer turns the same input into up to 35% more tokens, so a slice of that discount comes right back. And the two-dollar rate is an introductory price that reverts to three and fifteen at the end of August. Real savings are real, you just calculate them on tokens, at the price you will actually pay in September, not the launch banner. ## The move: match the model to the task Here is the whole idea, and it is almost embarrassingly simple. Most AI calls never needed the top model in the first place. Across the deployments we see at Olakai, somewhere between 60 and 80% of the work, the summarizing, the extracting, the routine code, gets handled just as well by a model that costs five or ten times less. This is not a hunch. Researchers at UC Berkeley, Anyscale, and Canva published [peer-reviewed routing work](https://research.ibm.com/blog/LLM-routers) (RouteLLM, presented at ICLR 2025) showing roughly 85% cost savings while holding 95% of frontier-model quality, and in practice a well-chosen model pair lands around half the cost at about 98% of the quality. The reason it works is simple: most teams were overpaying on the easy stuff the whole time. Being a smarter token manager is just this: send each task to the cheapest model that can actually do it, and save the expensive model for the work that truly needs it. ## How it plays out with AI coding agents Coding is where the token bill actually lives for most engineering orgs, so it is worth making this concrete with three scenarios that show up constantly in [AI coding tool](/coding-iq/) deployments. **The planner and the executors.** A coding agent is not one thing. It is a planner that decides the approach, and a swarm of executors that do the grunt work: writing boilerplate, generating tests, fixing lint, editing files. The judgment lives in the planner, so give it the best model available. The executors are mostly routine, and they run over and over across a long session, which is exactly where tokens pile up. Point the executors at Haiku or Sonnet 5 instead of Opus, and the build gets dramatically cheaper with no drop in the quality that matters. One measured example: a 14-million-token build came in 57% cheaper with the executor on Haiku 4.5 instead of Opus 4.8, and the planner never changed. **Route by difficulty.** Not every ticket is hard. Renaming variables, scaffolding a test, a simple endpoint, a formatting pass, that is easy work, and it should go to Haiku. A feature or a mid-size refactor is Sonnet 5 territory. The gnarly stuff, tricky architecture, a subtle concurrency bug, security-sensitive code, is where it makes sense to spend on Opus. On most engineering teams the easy and medium buckets make up the vast majority of tickets, which means most traffic should never touch the frontier model at all. **Watch the loops.** Agentic coding burns tokens in a way chat never did, because agents retry, re-prompt, and loop, and every wasted token in turn one gets paid for again on every turn after it. A single long, unoptimized Opus session can run twenty dollars or more; the same session, routed and cleaned up, can be two or three. Multiply that across a twenty-developer team running dozens of sessions a day, and the difference is a five-figure monthly bill that is mostly avoidable. Route the routine sub-steps down, and cap the loops so one stuck agent cannot run up the tab. The same logic holds outside of code. Summarizing a long document costs about eleven cents on Haiku, fifty-five cents on Opus, and a dollar-ten on Fable, for the same summary. Run a million of those a month and that is a hundred and ten thousand dollars against well over a million. Burning the most capable model on a routine document summary is paying ten times over for an answer nobody can tell apart from the cheaper one. ## The catch, and it is the important one Routing is not free money, and it is not fire-and-forget. The classic way it bites: a team builds a router, cuts the bill 40%, finance is thrilled. Then the provider quietly tweaks the cheap model, a quality check starts failing, and the router silently sends everything back to the most expensive model. The next bill triples. Nothing errored. Nothing alerted. Savings only ever count net of quality, and a weaker answer that triggers retries and manual cleanup can quietly eat the very savings it created. Routing without measurement saves money right up until it costs more than it saved. Doing it properly takes three things running underneath the router: cost per outcome for each model, not just the total bill, so a route can be proven to actually pay off; a live watch for silent escalation and quality drift; and enforceable limits around the whole system, so a misrouted or runaway job cannot eat the quarter’s budget before anyone notices. This is precisely the layer we built [Agent IQ](/agent-iq/) to sit on top of, and it is why [cost-per-outcome tracking](/analytics-kpis/), not just total spend, is the metric that matters. Visibility tells a team it happened. A limit stops it before it does. ## What this means for the CFO conversation For a finance leader watching AI coding spend climb, routing is the single highest-leverage lever available before the next budget review, but only if someone can show the receipt. “We switched to a cheaper model” is not a number. “We cut cost per accepted line by 40% while holding acceptance rate flat” is. That distinction is the difference between a CFO who trusts the next AI budget request and one who starts asking for a moratorium, a pattern we have written about in [why acceptance rate is the wrong metric on its own](/blog/ai-coding-tool-roi-metrics/) for judging coding-tool ROI. ## The playbook This is what leading an AI transformation actually looks like in 2026. Not chasing the biggest model. Matching the model to the task, measuring that the swap held quality, and proving the savings with a number a CFO can defend in a board meeting. That is the vendor-neutral measurement layer Olakai exists to provide: one place to see cost per outcome across every model and every vendor, not a router’s word for it. That is how a team ships more, spends like it is its own money, and walks into the next budget review with the receipt instead of an excuse. One question worth taking into the next architecture review: what share of your AI calls hit your most expensive model by default, and do you actually know whether they needed to? If the honest answer is “we’re not sure,” that is the gap [talking to an Olakai expert](/schedule-a-demo/) is built to close. [**Talk to an Expert →**](/schedule-a-demo/) [Ask Kai: Inside Olakai’s Conversational Control Plane](https://olakai.ai/blog/ask-kai-conversational-control-plane/) [Companies Are Cutting Jobs to Pay for AI. Can They Prove It’s Working?](https://olakai.ai/blog/companies-cutting-jobs-to-pay-for-ai/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Agent Iq Data Sheet Source: /blog/resources/agent-iq-data-sheet [← Back to Olakai's Blog](/blog/) # Agent IQ Data Sheet ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. June 29, 2026 Agent IQ gives organizations cost and performance visibility into autonomous AI workflows — so you can measure what each agent run costs, what it produces, and whether it is worth scaling. This three-page data sheet covers capabilities, key metrics, and integration overview for Olakai Agent IQ. **What’s inside:** Agent execution analytics, cost-per-workflow measurement, custom KPI tracking, agent comparison and benchmarking, governance and policy rails, and Kai synthesis for CFO and VP Engineering reporting. [Download Data Sheet (PDF)](https://olakai.ai/wp-content/uploads/2026/06/2026-06-agent-iq-datasheet.pdf) [Assistive IQ Data Sheet](https://olakai.ai/blog/resources/assistive-iq-data-sheet/) [Kai Data Sheet](https://olakai.ai/blog/resources/kai-data-sheet/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Assistive Iq Data Sheet Source: /blog/resources/assistive-iq-data-sheet [← Back to Olakai's Blog](/blog/) # Assistive IQ Data Sheet ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. June 29, 2026 Assistive IQ gives organizations complete visibility into how employees use AI assistants — approved and unauthorized — so they can measure ROI, detect shadow AI exposure, and enforce data policy at scale. This three-page data sheet covers capabilities, key metrics, and integration overview for Olakai Assistive IQ. **What’s inside:** Shadow AI detection and classification, DLP and data policy enforcement, productivity measurement across the assistive AI portfolio, license optimization, and Kai synthesis for CISO and Head of AI reporting. [Download Data Sheet (PDF)](https://olakai.ai/wp-content/uploads/2026/06/2026-06-assistive-iq-datasheet.pdf) [Coding IQ Data Sheet](https://olakai.ai/blog/resources/coding-iq-data-sheet/) [Agent IQ Data Sheet](https://olakai.ai/blog/resources/agent-iq-data-sheet/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Coding Iq Data Sheet Source: /blog/resources/coding-iq-data-sheet [← Back to Olakai's Blog](/blog/) # Coding IQ Data Sheet ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. June 29, 2026 Coding IQ gives engineering leaders the visibility to measure what AI coding tools actually return — not just acceptance rate, but cycle time improvement, cost per pull request, and team-level ROI. This three-page data sheet covers capabilities, key metrics, and integration overview for Olakai Coding IQ. **What’s inside:** Developer productivity measurement, AI spend attribution by team and project, license utilization analysis, cycle time and code quality tracking, and Kai synthesis for board-ready reporting. [Download Data Sheet (PDF)](https://olakai.ai/wp-content/uploads/2026/06/2026-06-coding-iq-datasheet.pdf) [Assistive IQ Data Sheet](https://olakai.ai/blog/resources/assistive-iq-data-sheet/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Kai Data Sheet Source: /blog/resources/kai-data-sheet [← Back to Olakai's Blog](/blog/) # Kai Data Sheet ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. June 29, 2026 Kai is the conversational intelligence layer that synthesizes insights across Coding IQ, Assistive IQ, and Agent IQ — answering board-level questions in seconds, not hours. This three-page data sheet covers Kai capabilities, example queries, and how it connects to your AI measurement data. **What’s inside:** Cross-pillar synthesis and natural language queries, transparent reasoning and scenario modeling, board-ready output generation, proactive cost and risk alerts, and integration with all three Olakai pillars. [Download Data Sheet (PDF)](https://olakai.ai/wp-content/uploads/2026/06/2026-06-kai-datasheet.pdf) [Agent IQ Data Sheet](https://olakai.ai/blog/resources/agent-iq-data-sheet/) [Proving AI ROI: The Enterprise Measurement Playbook](https://olakai.ai/blog/resources/proving-ai-roi-the-enterprise-measurement-playbook/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Shadow Ai The Hidden Risk And The Opportunity Source: /blog/resources/shadow-ai-the-hidden-risk-and-the-opportunity [← Back to Olakai's Blog](/blog/) # Shadow AI: The Hidden Risk and the Opportunity ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. June 29, 2026 78% of knowledge workers use AI tools not approved by their employer. Shadow AI is not just a security problem — it is also your clearest signal for where AI creates real workforce value. This nine-page white paper gives CISOs, CFOs, and boards a framework to address the risk and capture the signal simultaneously. **What’s inside:** What shadow AI actually is and why it is growing, the data exposure and compliance risk vectors, the opportunity side (shadow AI as adoption signal), a three-phase detection and response framework, and role-specific takeaways for CISOs, CFOs, and board members. [Download White Paper (PDF)](https://olakai.ai/wp-content/uploads/2026/06/2026-06-shadow-ai-risk-and-opportunity.pdf) [Proving AI ROI: The Enterprise Measurement Playbook](https://olakai.ai/blog/resources/proving-ai-roi-the-enterprise-measurement-playbook/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Revenue Forecast Ai Budget Source: /blog/revenue-forecast-ai-budget [← Back to Olakai's Blog](/blog/) # Their Revenue Forecast Is Your AI Budget ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. August 25, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) From the AI ROI Series, recorded 25 August 2026. [Anthropic](https://www.anthropic.com) is going public, and it is shaping up to be the largest listing in history, bigger than SpaceX, which raised $75 billion back in June. The Financial Times reports that investors are targeting $2 trillion for the company. Almost all of the coverage stops at that number, and the arithmetic underneath it points straight at your own budget, so that is where I want to go. ## A headline valuation is a function of how much stock changes hands When a company lists, it does not sell itself. It sells a small piece of itself, and that piece sets the price of everything else. SpaceX sold 4.2% to raise its $75 billion. Anthropic wants to beat that raise and clear $2 trillion. So put $100 billion into $2 trillion and you get about 5% changing hands, which means 95% of the company is being priced by the 5% that sells. That is the Wall Street half, and I am done with it, because we are buyers here rather than investors. What I care about is what has to be true for $2 trillion to hold, since the answer to that question arrives on your invoice. Two things have to hold, and they pull against each other. ## First, the revenue Bankers expect Anthropic past $100 billion annualised by year end. They started the year at around $9 billion. That is roughly an elevenfold move inside twelve months, and the thing to be clear about is where it comes from. That revenue is enterprises buying tokens. Their revenue forecast is your AI budget, and the growth has to come out of somebody’s line item, which means the plan you are writing for 2027 is on the other side of the same equation. It is worth being precise about what that implies, because it is easy to read as rhetoric. A vendor growing from $9 billion to $100 billion in a year is not doing it on new logos alone at that scale. A large share has to come from existing customers spending more, which is the same creeping invoice I described in [the agent portfolio piece](/blog/four-agents-70-percent-of-the-return/): adoption spreads, usage climbs, and the bill climbs with it, always with a good reason attached. From the vendor’s side that curve is the growth story underwriting the listing. From your side it is next year’s budget variance. ## Second, the margin, and this is the fragile one Gross margins run around 44%. The valuation is priced on 40% to 50%, sustained for years. Anything under 35% and the analysis says most of the valuation goes with it. So the whole structure rests on a band of about ten points, in a business whose input costs are being set by the compute market I have been complaining about all year. Gross margin What it implies 40% to 50%, sustained The band the $2 trillion valuation is priced on \~44% Where margins actually run today Below 35% Most of the valuation goes with it *Directional, from reported figures and analyst work, recorded 25 August 2026. The arithmetic is mine, so check my math.* ## Now hold that next to the capability chart Ten frontier models, 16 points of capability between them, priced 12 times apart. The top two, the most capable and the most expensive, are Anthropic’s. Sixteen points of capability across a twelve-fold price range means the premium buys real capability, and buys it at a rate that gets harder to defend the further you get from the tasks that genuinely need it. So the rational move for any buyer is to push work down the curve wherever the job allows it, and I am seeing a great deal more of that, seriously, among our own customers. Here is the part I think is genuinely underappreciated. Every enterprise that pushes work down the curve takes a point of somebody’s margin. The buyer behaviour that is rational for you individually is the same behaviour that presses on the one variable the valuation cannot afford to lose. That is my thesis, and it is why enterprise buyers are the variable in this arithmetic rather than the audience for it. [Routing work to the cheapest model that finishes the job](/blog/model-routing-explained/) stopped being purely a cost tactic somewhere in the last year. ## You can only make that call if you can see it Pushing work down the curve sounds like a procurement decision and is actually a measurement one, because the phrase “wherever the job allows it” is doing all the work in that sentence. Deciding which jobs allow it means knowing, per task type, what the cheaper model finishes and what it does not, which is a question about your own workload rather than about any leaderboard. Get that wrong in the cautious direction and you pay the premium on everything forever. Get it wrong in the aggressive direction and you cut the bill while quietly degrading the output, which I have [written about at some length](/blog/ai-bill-eating-everything-else/) after getting the unit wrong myself. None of the above is actionable without visibility across all three of the places AI now runs: your coding AI, your assistive AI, and the autonomous agents most organisations are piloting for next year. One record, rather than three vendor consoles and a cloud bill you reconcile by hand in January. Capture everything, attribute it to a team, an agent, and a model, and then it can answer a question you had not thought of when you started collecting. Not a dashboard, though. A dashboard is just a view, and a view only shows what somebody already collected, which in practice means vanilla metrics chosen before anyone knew what would matter. That distinction is the same one behind [a falling rate card and a rising bill](/blog/your-ai-got-cheaper-your-bill-didnt/), and behind [an agent portfolio nobody had split by agent](/blog/four-agents-70-percent-of-the-return/). In both cases the number that mattered existed only after somebody kept the record that could produce it. ## 2027 budgets are being written right now That is the window, and it closes. As always, check my math and tell me if I have this wrong, because I welcome that all day long. But the questions I would want answered before you sign next year’s number are these. Do you have one source of truth, or several? Are you capturing every AI interaction across coding, assistive, and agentic use, or only the ones a vendor happens to report to you? Can you make sense of it at scale without a project to do so? And are you collecting continuously, so that [the AI ROI question](/ai-roi/) can be answered with evidence rather than reconstructed under deadline? I’m Paul, co-founder of Olakai. Olakai is the system of record for enterprise AI: one record across every tool, every agent, and every token, in your own environment. [Your AI is an investment, so let’s measure it like one](/schedule-a-demo/). [The Cache Tax: Where DeepSeek’s Price Increase Is Concentrated](https://olakai.ai/blog/the-cache-tax/) [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Servicenow Ai Acquisitions Governance Source: /blog/servicenow-ai-acquisitions-governance [← Back to Olakai's Blog](/blog/) # What ServiceNow’s $8B AI Acquisition Spree Tells Us About the Future of Enterprise AI ![Executive boardroom discussing AI governance strategy](https://olakai.ai/wp-content/uploads/2026/01/featured-post-1296.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. January 28, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) ServiceNow just spent $7.75 billion to solve a problem most enterprises don’t know they have yet. In January 2026, ServiceNow announced its largest acquisition ever: Armis, a cyber exposure management platform, for $7.75 billion in cash. But this wasn’t an isolated move. It was the culmination of an acquisition strategy that signals a fundamental shift in how the enterprise software market views AI governance. When a $200 billion platform company makes its largest purchase in history, it’s worth paying attention to what they’re buying—and why. ## The Acquisition Timeline ServiceNow’s 2025 spending spree tells a coherent story. In January 2025, they acquired [Cuein](https://newsroom.servicenow.com/press-releases/details/2025/ServiceNow-accelerates-agentic-AI-roadmap-with-acquisition-of-AI-native-conversation-data-analysis-platform-Cuein-01-17-2025-traffic/default.aspx), an AI-native conversation data analysis platform. In April, they announced the acquisition of Logik.ai, an AI-powered configure-price-quote solution. Then came Moveworks for $2.85 billion, Data.World for data governance, and Veza for identity security. The Armis deal dwarfs them all. At $7.75 billion in cash—more than twice the Moveworks price—it represents a massive bet on the convergence of AI, security, and operational technology. Combined with the earlier acquisitions, ServiceNow is assembling capabilities that span AI conversation analysis, data governance, identity management, and now comprehensive exposure management across IT, OT, and IoT environments. This isn’t a collection of opportunistic purchases. It’s a deliberate construction of an AI governance stack. ## The AI Control Tower Vision ServiceNow has been explicit about their strategic direction. They’re positioning themselves not just as an AI platform, but as what they call an “AI Control Tower”—a unified system that governs and manages AI across the enterprise. In the [Armis announcement](https://newsroom.servicenow.com/press-releases/details/2025/ServiceNow-to-acquire-Armis-to-expand-cyber-exposure-and-security-across-the-full-attack-surface-in-IT-OT-and-medical-devices-for-companies-governments-and-critical-infrastructure-worldwide/default.aspx), ServiceNow President Amit Zavery stated it directly: “In the agentic AI era, intelligent trust and governance that span any cloud, any asset, any AI system, and any device are non-negotiable if companies want to scale AI for the long-term.” That framing matters. ServiceNow isn’t just saying AI governance is important. They’re saying it’s non-negotiable for scaling AI—and they’re willing to spend nearly $8 billion to prove the point. The Armis acquisition specifically addresses a visibility gap that most organizations haven’t fully reckoned with. Without knowing what’s connected across IT, operational technology, IoT, and physical environments, ServiceNow argues that “workflow automation, AI governance, and risk prioritization all collapse into theatre.” You can write policies all day, but if you can’t see what’s actually happening across your technology footprint, those policies are aspirational at best. ## Why This Matters for Every Enterprise ServiceNow’s acquisition strategy validates a market reality that’s been emerging for the past two years. AI governance isn’t a nice-to-have feature for compliance teams to worry about later. It’s becoming a core enterprise capability—one that established platform companies are racing to own. Consider what this signals. A company with ServiceNow’s market intelligence—they see how their 8,100+ enterprise customers are actually deploying technology—has concluded that AI governance is worth a multi-billion dollar bet. This aligns with the broader trajectory we traced in [The Evolution of Enterprise AI](/blog/enterprise-ai-evolution/), where each era demands more robust governance. They’re not experimenting. They’re going all-in. This has several implications for enterprise leaders. First, the governance problem is real and urgent. If you’ve been treating [agentic AI governance](/blog/what-is-agentic-ai/) as a future concern, the market is moving faster than that timeline allows. ServiceNow, Microsoft, Salesforce, and other major platforms are all investing heavily in AI governance capabilities. They’re building for a future where governance is expected, not optional. Second, visibility is the foundation. Every acquisition ServiceNow made connects to visibility in some way—seeing AI conversations, understanding data flows, tracking identities, monitoring connected devices. You can’t govern what you can’t see, and the platform leaders are racing to be the ones who provide that visibility layer. Third, the vendor landscape is consolidating. When large platforms acquire specialized governance capabilities, they’re signaling an intent to own that layer of the stack. Organizations that wait too long may find themselves choosing between platform lock-in and building custom solutions from scratch. ## The Broader Pattern ServiceNow isn’t alone in this recognition. Microsoft has been embedding governance capabilities across its Copilot ecosystem. Salesforce is building AI controls into its platform. AWS, Google Cloud, and Azure are all developing AI governance tooling. The pattern is clear: every major platform company has concluded that AI governance will be a battleground for enterprise relationships. They’re not just selling AI capabilities—they’re selling the ability to control, secure, and measure those capabilities. This creates both opportunity and risk for enterprises. The opportunity is that governance capabilities will become more accessible as platform providers compete to offer them. The risk is that governance becomes another vector for platform lock-in, with organizations finding themselves dependent on a single vendor not just for AI capabilities but for their ability to manage and measure those capabilities. ## What This Means for Your AI Strategy The ServiceNow acquisitions should prompt several strategic questions for enterprise leaders. If you’re still waiting for AI governance, the market isn’t. The leading platform companies are spending billions to build governance capabilities. They’re doing this because they see demand from their largest customers—the enterprises that are furthest along in AI deployment. If you’re behind the curve on AI governance, you’re increasingly in the minority. Enterprise-grade governance is becoming table stakes. Two years ago, AI governance was a differentiator. Organizations that had it were ahead. Today, it’s moving toward baseline expectation. The question is shifting from “Do you have AI governance?” to “How mature is your AI governance?” Organizations without any [governance infrastructure](/ai-governance/) will increasingly struggle to pass security reviews, satisfy regulators, and win enterprise deals. You don’t need $8 billion to get started. ServiceNow is building for a world where they’re the governance layer for their entire customer base. Your organization has different needs. You need visibility into what AI is doing, measurement of what value it’s delivering, and controls that scale with your risk profile. That doesn’t require a platform acquisition strategy—it requires the right tools applied to your specific environment. ## The Vendor-Neutral Alternative Olakai was built on the same insight that’s driving ServiceNow’s acquisition strategy: enterprises need unified visibility, governance, and ROI measurement across their AI deployments. The difference is in how we deliver it. Rather than locking customers into a single platform, Olakai provides a vendor-neutral control plane that works across AI tools, models, and infrastructure. We integrate with whatever AI systems you’re using—whether that’s chatbots from one vendor, copilots from another, and agent frameworks from a third. The goal is the same governance visibility and ROI measurement that ServiceNow is assembling through acquisitions, without requiring you to commit to their ecosystem. This matters because most enterprises don’t have a single-vendor AI environment, and they’re unlikely to in the foreseeable future. Different teams have different needs. Different use cases have different requirements. A governance layer that only works within one platform leaves gaps that shadow AI will fill. ## Looking Ahead The ServiceNow acquisition spree marks a turning point. AI governance has moved from emerging concern to validated market category, with billions of dollars of M&A activity confirming its importance. This shift is playing out across every [industry vertical](/industries/). For enterprise leaders, the message is clear. The organizations that figure out AI governance in 2026 will have a significant advantage over those that don’t — a theme we explore across all eight trends in our [AI Predictions for 2026](/blog/ai-predictions-2026/). They’ll scale AI programs faster because they can prove value and manage risk. They’ll win more enterprise deals because they can satisfy security and compliance requirements. They’ll retain talent because they can offer AI tools with appropriate guardrails rather than blanket prohibitions. ServiceNow is betting that AI governance will be non-negotiable for enterprises that want to scale AI. Based on what we’re seeing in the market, that bet looks correct. The only question is whether you’ll build that governance capability before your competitors do. The market has validated AI governance. [Talk to an expert](/schedule-a-demo/) to see how Olakai delivers it without platform lock-in. [Shadow AI: The Enterprise Risk Hiding in Plain Sight](https://olakai.ai/blog/shadow-ai-enterprise-risk/) [What 100+ AI Agent Deployments Taught Us About Proving ROI](https://olakai.ai/blog/ai-agent-roi-lessons/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Shadow Ai 76 Percent Problem Source: /blog/shadow-ai-76-percent-problem [← Back to Olakai's Blog](/blog/) # The 76% Problem: Shadow AI Is Getting Worse, Not Better ![The 76% Problem: Shadow AI Is Getting Worse, Not Better](https://olakai.ai/wp-content/uploads/2026/03/featured-post-1712.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. March 19, 2026 · [AI Governance](https://olakai.ai/blog/category/ai-governance/) Three out of four organizations say shadow AI is a problem. One in three doesn’t know whether they’ve already been breached because of it. Those numbers come from the [HiddenLayer 2026 AI Threat Landscape Report](https://www.prnewswire.com/news-releases/hiddenlayer-releases-the-2026-ai-threat-landscape-report-spotlighting-the-rise-of-agentic-ai-and-the-expanding-attack-surface-of-autonomous-systems-[redacted].html), released this month, which surveyed 250 IT and security leaders. The headline finding: 76% of organizations now cite shadow AI as a definite or probable problem. That’s up from 61% last year — a 15-point jump that represents one of the largest shifts in the dataset. And 31% of organizations don’t know whether they experienced an AI security breach in the past 12 months. Not “haven’t been breached.” Don’t know. Shadow AI isn’t stabilizing. It’s accelerating. ## What Changed in Twelve Months The 15-point jump from 61% to 76% didn’t happen because security teams got worse at their jobs. It happened because AI tools proliferated faster than governance could follow. In 2025, the AI coding tool market exploded. Cursor crossed $2 billion in annualized revenue. Claude Code hit $2.5 billion. GitHub Copilot remained embedded across enterprise engineering teams. Beyond development, ChatGPT, Gemini, Claude, and dozens of specialized AI tools became standard productivity enhancers for marketing teams, sales organizations, customer success groups, and finance departments. Each one represents a potential vector for unauthorized data flow. The math is simple. More AI tools available means more tools employees will adopt without waiting for IT to evaluate, approve, and provision them. According to [the 2025 State of Shadow AI Report](https://www.reco.ai/state-of-shadow-ai-report), the average enterprise hosts 1,200 unauthorized applications. Nearly half of employees using generative AI platforms do so through personal accounts that companies can’t see or govern. And the data exposure isn’t hypothetical. IBM’s 2025 Cost of a Data Breach Report found that one in five organizations reported a breach due to shadow AI, with those breaches costing $4.63 million on average — $670,000 more than standard breaches. Only 37% of organizations have policies to manage AI or detect shadow AI usage. The other 63% are flying blind. ## Why Banning Doesn’t Work — and What Does Every CISO who has tried to block AI tool access has learned the same lesson that IT leaders learned with Dropbox and Slack a decade ago: prohibition doesn’t eliminate usage. It eliminates visibility. Employees route around restrictions because AI tools make them measurably more productive. The sales rep who uses AI to draft responses closes more deals. The developer who uses an AI coding assistant ships features faster. The analyst who uses AI for research produces better work in less time. Telling them to stop isn’t a governance strategy — it’s a talent retention risk. The data backs this up. According to the [2026 CISO AI Risk Report from Saviynt](https://saviynt.com/ciso-ai-risk-report-2026), which surveyed 235 CISOs at large enterprises, 75% have already discovered unsanctioned AI tools running in production environments — and another 16% aren’t sure. Shadow AI ranked as CISOs’ number one risk concern, ahead of traditional threats like phishing and ransomware. Yet only 37% of organizations have policies to manage AI usage or detect shadow AI, according to IBM. The answer isn’t better firewalls. It’s better alternatives. When organizations provide governed AI tools that meet the same needs employees are solving with shadow tools, unauthorized usage becomes unnecessary — not just prohibited. The problem isn’t employees wanting to use AI. The problem is organizations not giving them a governed way to do it. ## The Costs You Can’t See The financial cost of shadow AI breaches is quantifiable — $4.63 million per incident, according to IBM. But the costs that accumulate before a breach are harder to measure and potentially larger. **Data leakage at scale.** When employees paste company data into ungoverned AI tools, that data flows to third-party servers that may retain it for model training. Customer records, financial projections, product roadmaps, and source code are all flowing through systems your security team can’t monitor. The Reco Shadow AI Report found that 86% of organizations are blind to AI data flows, and the average company experiences 223 incidents per month of users sending sensitive data to AI applications — double the rate from a year ago. **Compliance exposure that compounds.** The EU AI Act’s provisions for high-risk AI systems take effect on August 2, 2026. Organizations using AI in hiring, credit decisions, healthcare, or safety-critical systems will need to demonstrate compliance with transparency, human oversight, and risk management requirements. If those AI systems include tools that were never formally evaluated or approved — shadow AI, in other words — demonstrating compliance becomes impossible. You can’t document controls for tools you don’t know about. **Licensing and IP liability.** When AI tools generate outputs based on copyrighted training data, the liability for using those outputs falls on your organization. If a developer uses an ungoverned coding assistant that was trained on GPL-licensed code, and that code pattern ends up in your proprietary software, the legal exposure belongs to you — not the AI vendor and not the developer who used it. **Audit trail gaps.** When regulators, auditors, or legal teams ask how AI is being used in your organization, you need a comprehensive answer. Shadow AI makes that impossible. IBM found that 97% of organizations that reported AI breaches lacked proper AI access controls. The breach isn’t the only problem — it’s the inability to explain what happened and why. ## The Governance Playbook That Works Effective shadow AI governance follows a sequence: Detect, Assess, Redirect, Monitor. Not “block everything” — but “see everything, govern what matters.” **Detect.** You can’t govern what you can’t see. The first step is comprehensive discovery of AI tool usage across your organization — not just the tools IT provisioned, but the ones employees adopted on their own. This means monitoring network traffic patterns, analyzing browser extensions and desktop applications, reviewing SaaS procurement and expense reports, and surveying teams about their actual workflows. The goal is a complete inventory, not a witch hunt. **Assess.** Not all shadow AI carries equal risk. An employee using ChatGPT to brainstorm blog post ideas is not the same as a developer pasting proprietary algorithms into an ungoverned coding assistant. Prioritize by data sensitivity, regulatory exposure, and business criticality. Our [AI risk heatmap framework](/blog/ai-risk-heatmap/) provides a structured methodology for matching governance intensity to actual risk levels. **Redirect.** For high-value use cases discovered in the shadow, provision approved alternatives that meet the same need with appropriate controls. Enterprise versions of popular AI tools typically include data handling agreements, SSO integration, audit logging, and content filtering that their consumer counterparts lack. When approved tools are easy to access and meet employee needs, the incentive to use unauthorized alternatives disappears. **Monitor.** Shadow AI isn’t a one-time problem to solve — it’s an ongoing condition to manage. New AI tools launch weekly. Employee needs evolve. Governance policies need continuous enforcement. Build continuous monitoring into your AI governance framework, with alerts for new unauthorized tools, data flow anomalies, and policy violations. ## The Market Is Signaling The venture capital and analyst community has noticed what enterprise security teams are experiencing firsthand. The AI governance market reached $492 million in 2026 and is heading toward $1 billion by 2030, according to Gartner estimates. That growth reflects a simple reality: organizations are realizing they can’t scale AI adoption without governance infrastructure to match. A 2025 Gartner survey of 360 organizations found that enterprises using dedicated AI governance platforms are 3.4 times more likely to achieve high governance effectiveness than those relying on manual processes. Manual approaches — spreadsheet inventories, annual surveys, policy documents that nobody reads — worked when AI usage was limited to a few approved tools. They break down completely when every department in the organization is adopting AI tools at its own pace. The organizations that figure out shadow AI governance first won’t just avoid breaches. They’ll move faster than their competitors because they’ll be able to say yes to AI adoption with confidence rather than defaulting to no out of fear. They’ll know which tools are delivering value, which ones create risk, and where to invest next. Visibility isn’t just a security capability — it’s a strategic advantage. ## The 76% Is a Leading Indicator When three-quarters of organizations acknowledge a problem and a third don’t know whether they’ve already been breached, we’re past the awareness phase. The question is no longer whether shadow AI is a risk. It’s whether your organization will address it before a breach forces your hand. The path forward isn’t restriction. It’s visibility. See what’s happening. Assess the risk. Redirect usage to governed channels. Monitor continuously. The organizations that do this will turn shadow AI from a hidden liability into a governed advantage. The organizations that don’t will keep finding out the hard way — through breach reports, compliance failures, and audit findings that could have been prevented. *How much AI is running in your organization that you don’t know about? See how Olakai’s [shadow AI detection](/shadow-ai/) gives you complete visibility across every AI tool your employees use. [Talk to an expert](/schedule-a-demo/) to find out.* [Your AI Coding Tools Are Generating Code. Are They Generating Value?](https://olakai.ai/blog/ai-coding-tools-generating-value/) [Is Your $500K AI Coding Tool Investment Paying Off? What the Data Shows](https://olakai.ai/blog/ai-coding-tool-roi/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Shadow Ai Enterprise Risk Source: /blog/shadow-ai-enterprise-risk [← Back to Olakai's Blog](/blog/) # Shadow AI: The Enterprise Risk Hiding in Plain Sight ![Employee using unauthorized AI tools alone in dark office after hours — shadow AI risk](https://olakai.ai/wp-content/uploads/2026/01/shadow-ai-enterprise-risk-featured.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. January 23, 2026 · [AI Governance](https://olakai.ai/blog/category/ai-governance/) Right now, someone in your organization is using an AI tool you don’t know about. They’re pasting customer data into it. This isn’t a hypothetical scenario. According to [recent research from BlackFog](https://www.blackfog.com/blackfog-research-shadow-ai-threat-grows/), 86% of employees now use AI tools at least weekly for work—and 49% of them are using AI tools not sanctioned by their employer. That’s nearly half your workforce operating outside your visibility. The term for this is shadow AI, and it represents one of the fastest-growing enterprise risks of 2026. Unlike traditional shadow IT, which took a decade to become a crisis, shadow AI is accelerating on a timeline measured in months. The tools are too accessible, too useful, and too easy to hide. ## What Shadow AI Actually Looks Like Shadow AI isn’t malicious. That’s what makes it so difficult to address. Your employees aren’t trying to harm the company—they’re trying to do their jobs better. A sales rep pastes customer objections into ChatGPT to draft responses. A marketing manager uploads competitive research to Claude for analysis. A developer uses an AI coding assistant their team hasn’t officially adopted. A customer success manager feeds support tickets into an AI tool to identify patterns. Every one of these use cases is reasonable. Every one of them is also invisible to your security, compliance, and IT teams. And every one of them creates risk you can’t quantify because you don’t even know it exists. The explosion of shadow AI is driven by simple economics. Consumer-grade AI tools are free or nearly free. They require no procurement process, no IT approval, no integration work. An employee can start using ChatGPT, Claude, Gemini, or dozens of other AI tools in minutes, from any browser, on any device. The friction to adopt is essentially zero. ## The Risks Nobody’s Tracking When AI usage happens outside your visibility, risks accumulate in ways that are difficult to detect until something goes wrong. Data leakage is the most immediate concern. Employees pasting sensitive information into AI prompts are essentially sharing that data with third-party services. Customer records, financial projections, product roadmaps, legal documents, personnel information—all of it can flow into AI tools that may retain, train on, or inadvertently expose that data. According to a [survey cited by IBM](https://www.ibm.com/think/insights/rising-ai-adoption-creating-shadow-risks), over 38% of employees share sensitive information with AI tools without permission from their employer. Compliance violations compound the problem. If customer data from EU residents enters an AI system that doesn’t meet GDPR requirements, your organization bears the liability—not the AI vendor. The same applies to HIPAA-protected health information, SOC 2 data handling requirements, and industry-specific regulations. Your compliance posture is only as strong as your visibility into how data moves through your organization, and shadow AI creates massive blind spots. Output consistency becomes another hidden risk. When employees use unauthorized AI tools to generate customer-facing content, legal advice, financial analysis, or technical documentation, you have no quality control over what AI is producing on your behalf. Hallucinations—confident but incorrect AI outputs—can propagate through your organization without anyone recognizing their source. Cost sprawl may be the slowest-burning problem, but it’s significant. When every team and individual subscribes to their own AI tools, you lose purchasing leverage and budget visibility. Organizations often discover they’re paying for dozens of overlapping AI subscriptions, with no consolidation and no way to measure comparative value. Perhaps most critically, shadow AI leaves you without an audit trail. When regulators, auditors, or legal teams ask how AI is being used in your organization, you can’t answer. You can only hope nothing material happened in the systems you can’t see. ## Why Blocking Doesn’t Work The instinctive response to [shadow AI risk](/blog/shadow-ai-risk/) is to block it. Add ChatGPT to the firewall. Prohibit AI tool usage in the acceptable use policy. Send a memo. This approach fails for the same reasons it failed with shadow IT twenty years ago. Employees route around restrictions because they need these tools to compete. The sales rep who uses AI to respond to customers faster wins more deals. The developer who uses AI coding assistance ships features more quickly. The marketing manager who uses AI for research produces better analysis. When you block AI without providing alternatives, you don’t eliminate usage—you just push it further underground. Employees use personal devices, personal accounts, and personal networks. The shadow gets darker, and your visibility gets worse. There’s also a talent dimension. The most effective knowledge workers have already integrated AI into how they operate. Telling them they can’t use these tools at your organization is effectively telling them to work less effectively—or to work somewhere else. ## The Governance Gap The fundamental challenge with shadow AI is simple: you can’t govern what you can’t see. Most organizations have some form of AI policy. They may have approved certain tools, defined acceptable use cases, and established data handling requirements. But policies only work when they’re applied to known activity. Shadow AI, by definition, exists outside that framework. It’s the AI usage that happens in the gaps between your policies and your enforcement capabilities. For a structured approach to closing those gaps, see our [AI governance checklist for CISOs](/blog/ciso-governance-checklist/). Closing that gap requires visibility before control—you need to discover what’s actually happening before you can decide what should be allowed. This is where traditional security tools fall short. They’re designed to detect known threats and block prohibited applications. Shadow AI is neither. It’s legitimate tools being used for legitimate purposes in ways that happen to bypass your governance framework. Detecting it requires understanding the full landscape of AI usage across your organization, including tools you haven’t explicitly approved. ## How Olakai Addresses Shadow AI Olakai takes a visibility-first approach to [shadow AI detection and control](/shadow-ai/). Rather than starting with blocking, we start with discovery. What AI tools are actually being used in your organization? Who is using them? What data is flowing through them? What outcomes are they producing? This visibility layer creates the foundation for informed governance. Once you understand the full picture of AI usage, you can make intelligent decisions about what to allow, what to restrict, and what to redirect to approved alternatives. Our [AI risk heatmap framework](/blog/ai-risk-heatmap/) provides a methodology for matching governance intensity to the actual risk of each tool. You can identify high-risk usage patterns before they become incidents. You can consolidate redundant subscriptions and negotiate enterprise agreements with vendors you’re already using at scale. Importantly, visibility enables a partnership approach with employees rather than an adversarial one. When you can see which AI tools are delivering real value, you can fast-track their official adoption. When you can identify risky usage patterns, you can work with teams to provide safer alternatives. You shift from “you can’t use that” to “let me help you use this more effectively.” ## Getting Started The first step isn’t blocking. It’s understanding. Before you can govern AI usage effectively, you need to know what’s actually happening. Many organizations are surprised by what they discover—both the scope of AI usage and the specific tools that have gained traction without official approval. That discovery process should answer several key questions. What AI tools are employees using, both sanctioned and unsanctioned? What types of data are flowing through these tools? Which use cases are delivering measurable value? Where are the highest-risk concentrations of activity? With those answers in hand, you can build governance that’s proportional to risk and responsive to value. High-risk, low-value AI usage gets restricted. High-value, controllable AI usage gets accelerated. The gray areas in between get managed through policy, training, and approved alternatives. Shadow AI will continue growing in 2026. The question isn’t whether your employees will use AI tools you don’t control—it’s whether you’ll build the visibility to govern that usage intelligently. The organizations that figure this out will turn shadow AI from a hidden risk into a competitive advantage. The organizations that don’t will remain in the dark, hoping that nothing goes wrong in the systems they can’t see. How much AI is running in your organization that you don’t know about? [Talk to an expert](/schedule-a-demo/) to find out. [Your Most Important 2026 Resolution: Measure Your AI](https://olakai.ai/blog/measure-ai-2026-resolution/) [What ServiceNow’s $8B AI Acquisition Spree Tells Us About the Future of Enterprise AI](https://olakai.ai/blog/servicenow-ai-acquisitions-governance/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Shadow Ai Opportunity Source: /blog/shadow-ai-opportunity [← Back to Olakai's Blog](/blog/) # What Your Employees Are Actually Using: The Shadow AI Opportunity ![Shadow AI demand signals becoming visible across an enterprise data field with hidden patterns illuminated by detection beam, abstract visualization](https://olakai.ai/wp-content/uploads/2026/04/shadow-ai-opportunity-featured.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. April 9, 2026 · [AI Governance](https://olakai.ai/blog/category/ai-governance/) Here’s a scenario we’ve watched play out more than once. A CIO asks his team a simple question: how many AI tools are we paying for right now? He gets a clean answer, a few dozen on the procurement list, with a combined spend that’s easy to defend in a board meeting. Then his company deploys a browser extension across the organization. Within 48 hours, the real answer arrives. Not dozens. Hundreds. Most of them free, most of them nowhere near the procurement list, and almost none of them visible to the security team. That gap between what leadership knows about and what employees actually use is the [shadow AI problem](/blog/shadow-ai-enterprise-risk/). Most conversations about it focus on the threat, and the threat is real. But there’s another side of shadow AI that almost nobody talks about, and it’s the reason the CIO in that story spent the next month rethinking his entire AI procurement strategy. Shadow AI is the most honest data you have about what your teams actually want from AI. Used right, it’s a procurement gift wrapped in a security wrapper. ## The number that should worry you (and also excite you) Across the enterprise deployments we’ve analyzed, roughly 60% of AI usage happens outside IT-sanctioned channels. Employees paste customer data into free ChatGPT tabs, upload financial models to AI summarizers they found on Product Hunt, and run code through coding assistants the CAIO has never heard of. The security risk is obvious. What gets less airtime is what that 60% is telling you about your workforce. Six out of every ten AI interactions happening in your company right now represent a demand signal, an employee who needed a tool badly enough to go find one, bypass procurement, and keep using it until it became habit. That isn’t only a risk. It’s research. ## Prohibition has never worked Every security team’s first instinct, when they see the shadow AI scale for the first time, is to ban it. Block the URLs. Rewrite the [acceptable use policy](/blog/ciso-governance-checklist/). Send a firmwide email. We’ve watched this happen a dozen times, and the result is always the same. Employees route around the block. They switch to mobile devices. They email documents to personal accounts and run them through free tools at home. They find three alternatives for every one you block, because the tools genuinely help them work faster, and no policy has ever beaten a productivity gain you can feel within an hour. The answer is not prohibition. It’s [visibility](/blog/ai-visibility-audit/) and governance. You need to know what is being used, by whom, with what data, and whether it complies with your policies. Then you need to decide, tool by tool, whether to sanction it, route it through an approved version, or block it with a replacement in hand. That’s governance. Prohibition is policy theater. ## What shadow AI is actually telling you Here’s the part of the conversation nobody has. Your sanctioned AI stack tells you what your leadership team decided. Shadow AI tells you what your people reached for when nothing was stopping them. That second data set is almost always more useful than the first. ![Olakai Assistive IQ overview dashboard showing AI value created, total time saved, licensing cost, license utilization, data risk exposure, ROI trend, and interaction count across the organization](https://olakai.ai/wp-content/uploads/2026/04/assistive-iq-overview-scaled.png) The view most CIOs see for the first time: AI value, cost, license utilization, and data-risk exposure for every AI tool in the organization, side by side. When you can see every AI tool in use across the organization, ranked by department, frequency, and data sensitivity, you start noticing patterns that rewrite your procurement plan. The $40,000-a-year tool legal bought in Q2 has less weekly usage than a free alternative two analysts discovered on their own. Marketing adopted a writing assistant that nobody approved, and campaign velocity has quietly doubled. Finance is using three different AI tools that overlap almost completely, a consolidation opportunity that pays for itself in a month. Shadow AI isn’t a failure of policy. It’s a heat map of real demand. This is the reframe that changes the budget conversation. [McKinsey’s State of AI](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) has tracked enterprise AI adoption climbing faster than any other software category in a decade, but almost every organization we talk to can’t tell you [where the value inside that adoption is actually landing](/blog/ai-metrics-that-matter/). Shadow AI is where a lot of that value is landing, and it’s where most of the buying signals you’re missing already live. ## What Olakai does about it We built Olakai’s [shadow AI capability](/shadow-ai/) around three steps: discover, govern, and report. Discovery starts with a lightweight browser extension that identifies over 600 AI tools the moment an employee starts using one. It doesn’t require SSO integration, it doesn’t require expense report reconciliation, and it catches the tools that would never show up in either. Within days, you have a complete inventory of every AI tool your organization is actually using, mapped to departments, users, and usage patterns. ![Olakai Assistive IQ shadow AI detail view for Fireflies.ai showing an Olakai Composite Risk Assessment of 54 percent High with breakdown across AI training, content license, data retention, third-party sharing, and IP ownership, plus interaction count and IT governance status flagged as shadow AI](https://olakai.ai/wp-content/uploads/2026/04/assistive-iq-shadow-ai-scaled.png) Every shadow AI tool gets a composite risk score based on its terms of service, privacy policy, and EULA — broken down across the five dimensions a security team actually cares about. Governance is where the threat and the opportunity both get resolved. Olakai ranks each tool by risk surface: which ones are processing PII, which departments are most exposed, which specific prompts contain sensitive data. You set policies once, and they enforce automatically. Real-time data loss prevention catches regulated data before it leaves the building. Approved tools keep running. High-risk ones get blocked with a replacement suggested in the same breath. Your security team stops playing whack-a-mole and starts making decisions backed by real usage data. Reporting closes the loop. Every interaction, every tool, every policy decision becomes an audit trail you can export for compliance reviews. When an auditor asks what AI tools your finance team used in Q3, you don’t send an IT admin to pull logs from five different admin consoles. You export one report. ## The part your CFO will actually care about This is the piece of the shadow AI conversation that usually gets left out, and it’s the piece that pays for the platform in the first quarter. Every AI tool you’re sanctioning charges by the seat. Every enterprise over-provisions. [Assistive IQ](/assistive-iq/) shows you exactly which licenses are sitting idle, by tool, by team, by user, and gives you a precise reclaim list you can hand to procurement before the next renewal cycle. ![Olakai Assistive IQ Cost and Utilization view showing total monthly AI licensing spend of $8,892, wasted spend of $5,962 on unused seats, 41 percent average utilization rate, and seat-by-seat utilization for six licensed AI applications including Anthropic at 185 percent, Figma AI at 104 percent, GitHub Copilot at 60 percent, Notion AI at 47 percent, Grammarly at 41 percent, and Jasper at 30 percent](https://olakai.ai/wp-content/uploads/2026/04/assitive-iq-licences-scaled.png) Cost and utilization, continuously updated. On this one screen: $5,962 of monthly spend sitting on unused seats, two apps running over capacity, and four apps with a precise reclaim list attached. Look at the screenshot above for a minute. It’s a real view from an Assistive IQ deployment. Total monthly licensed AI spend: $8,892. Wasted spend on unused seats: $5,962. That’s 67% of the AI budget earning nothing, and it’s not an outlier. Some tools are running over capacity (Anthropic at 185% utilization, Figma AI at 104%, both strong signals to buy more seats or renegotiate). Others are drastically under (Jasper at 30%, Grammarly at 41%, both candidates for reclaim or consolidation). The same single view tells procurement where to buy more, where to cut, and where to do neither. Now overlay that with the shadow AI data. The tools your people are reaching for on their own, that you aren’t paying for, are frequently the ones quietly delivering the most value. Meanwhile the licensed tools sitting at 30% utilization are the ones you’re defending in a budget review. That isn’t a failure of the employees. It’s a failure of visibility. When you can see both sides at once, the licensed spend that isn’t earning its keep and the shadow usage that represents real demand, procurement stops guessing. You reallocate budget from the dead seats to the tools your team is actually using, and you do it with [numbers your CFO will accept](/use-cases/cfo/). ## Why this exercise matters right now Every enterprise we talk to is sitting at some version of the same moment. AI spending has ramped faster than any other software category in a decade. Boards are asking for ROI numbers with a patience that’s running out. Regulators are writing compliance frameworks that will require audit-ready AI usage logs. Meanwhile, the people actually using AI in your company are miles ahead of the procurement process that’s supposed to govern them. The gap between those two things is growing by the week, and it’s becoming the single biggest source of hidden risk and hidden opportunity in enterprise IT. Companies that close the gap this year will have a cleaner compliance story, a leaner AI budget, and a better read on which AI tools are actually working than their competitors. Companies that don’t will spend 2026 explaining what they don’t know about AI to boards that assumed they already knew it. The exercise of actually surfacing shadow AI, governing it, and feeding the data back into procurement is one of the highest-leverage things a CIO, CISO, or CFO can do in the next 90 days. ## Your next step Shadow AI isn’t going away. It’s the shape enterprise AI adoption takes before governance catches up. You can treat it as a threat and spend the next year trying to suppress it, or you can treat it as the most honest data you’ve ever had about how your people actually work, make procurement and governance decisions from that data, and turn the whole thing into an advantage. If you want to see what shadow AI looks like inside your own organization, [talk to an expert](/schedule-a-demo/). We’ll show you what 48 hours of browser-level visibility reveals, and we’ll walk you through how the threat and the opportunity get resolved at the same time. [5 Tools Enterprises Actually Use to Measure AI ROI — And What None of Them Get Right](https://olakai.ai/blog/ai-roi-measurement-tools/) [AI Can Do Math After All: Finance Is the \#2 AI ROI Function and Nobody’s Talking About It](https://olakai.ai/blog/ai-roi-finance-cfo/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Shadow Ai Risk Source: /blog/shadow-ai-risk [← Back to Olakai's Blog](/blog/) # Shadow AI: The Hidden Risk in Your Enterprise ![Shadow AI risk - hidden unauthorized AI usage in enterprise](https://olakai.ai/wp-content/uploads/2025/10/shadow-ai-risk-featured.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. October 29, 2025 · [AI Governance](https://olakai.ai/blog/category/ai-governance/) Your employees are using AI tools you don’t know about. Right now. They’re pasting customer data into ChatGPT to draft emails. They’re uploading financial documents to AI summarizers. They’re using unapproved coding assistants that send your source code to third-party servers. And the numbers are staggering. According to a [Gartner survey of cybersecurity leaders](https://www.gartner.com/en/newsroom/press-releases/2025-11-19-gartner-identifies-critical-genai-blind-spots-that-cios-must-urgently-address0) conducted in 2025, 69% of organizations suspect or have evidence that employees are using prohibited public GenAI tools. Microsoft’s research found that 71% of UK employees admitted to using unapproved AI tools at work—with 51% doing so at least once a week. This isn’t occasional experimentation; it’s a systematic shadow operation running parallel to your official technology stack. This is **shadow AI**—the enterprise AI equivalent of shadow IT—and it represents one of the most significant and underestimated risks facing organizations today. ## What is Shadow AI? Shadow AI refers to AI tools and services that exist outside your organization’s visibility and governance. They’re not approved by IT, security, or compliance teams. They’re not visible in your technology inventory. They’re not governed by your data protection policies. And they’re not monitored for security, compliance, or cost implications. Just as shadow IT emerged when employees started using Dropbox, Slack, and other cloud tools without IT approval, shadow AI is spreading as employees discover that AI makes their jobs easier—regardless of whether it’s sanctioned. According to [the 2025 State of Shadow AI Report](https://www.reco.ai/state-of-shadow-ai-report), the average enterprise hosts 1,200 unauthorized applications, and 86% of organizations are blind to AI data flows. Nearly half (47%) of people using generative AI platforms do so through personal accounts that companies aren’t overseeing. ## Why Shadow AI is Different from Shadow IT Shadow AI carries risks that go beyond traditional shadow IT in fundamental ways. **Data goes out, not just in.** When an employee uses unauthorized Dropbox, they might store company files externally—a risk, but a bounded one. When they use unauthorized AI, they actively send sensitive data to third-party models. That customer complaint they pasted into ChatGPT? It might be used to train the model, potentially surfacing in responses to competitors. According to Cisco’s 2025 study, 46% of organizations reported internal data leaks through generative AI—data that flowed out through employee prompts rather than traditional exfiltration. **Prompts reveal more than files.** The questions employees ask AI reveal context that raw data doesn’t. “Summarize this contract and identify terms unfavorable to us” tells the AI (and its operator) not just the contract contents, but your negotiating strategy and concerns. The prompt itself is intelligence. **Answers drive decisions.** When AI provides analysis or recommendations, employees act on them. An unauthorized AI tool giving bad financial advice, incorrect legal interpretation, or flawed technical guidance can lead to costly mistakes with no audit trail. And there’s no recourse when things go wrong. **The attack surface is enormous.** Prompt injection, jailbreaking, and other AI-specific attacks create new vectors. An employee who pastes customer data into a compromised AI tool might unknowingly expose that data to attackers who’ve manipulated the model. ## The Scope of the Problem If you think shadow AI isn’t happening in your organization, the statistics suggest otherwise. Gartner predicts that by 2030, more than 40% of enterprises will experience security or compliance incidents linked to unauthorized shadow AI. That’s not a distant future risk—it’s the trajectory we’re already on. The financial impact is real and immediate. According to [IBM’s 2025 Cost of Data Breach Report](https://www.kiteworks.com/cybersecurity-risk-management/ibm-2025-data-breach-report-ai-risks/), shadow AI incidents now account for 20% of all breaches and carry a cost premium: $4.63 million versus $3.96 million for standard breaches. AI-associated cases caused organizations more than $650,000 extra per breach. The gap between AI adoption and AI governance is where shadow AI thrives—and where the costs accumulate. Perhaps most concerning: 83% of organizations operate without basic controls to prevent data exposure to AI tools. The average company experiences 223 incidents per month of users sending sensitive data to AI applications—double the rate from a year ago. And 27% of organizations report that over 30% of their AI-processed data contains private information, including customer records, financial data, and trade secrets. ![Shadow AI by the Numbers — $4.63M breach cost, 69% suspect prohibited use, 223 incidents per month, 90% of security leaders use unapproved tools](https://olakai.ai/wp-content/uploads/2026/02/shadow-ai-by-numbers-inline.webp) ## Common Shadow AI Scenarios These aren’t hypothetical risks. They’re happening in organizations like yours, every day. **The helpful marketer** uses an AI writing tool to draft blog posts. She pastes competitor analysis, product roadmaps, and customer testimonials as context. The tool’s terms of service allow training on user inputs. Your competitive intelligence is now potentially in someone else’s model—or in their training data, waiting to surface in responses to your competitors. **The efficient developer** uses an unapproved coding assistant to speed up development. He pastes internal API documentation and proprietary algorithms for context. The code generated might include those patterns in ways that constitute IP leakage, and the original code may be used for model training. **The overwhelmed HR manager** uses an AI tool to help screen resumes and draft interview questions. She pastes candidate information, salary data, and performance review excerpts. She’s now exposed PII to an unapproved processor, potentially violating GDPR and internal policies—with no documentation of consent or processing basis. **The pressured analyst** uses an AI tool to summarize earnings calls and model scenarios. He pastes material non-public information into prompts. If that information surfaces elsewhere—or even if someone later discovers it was processed through an unauthorized channel—it could trigger SEC scrutiny. ## Why Traditional Controls Don’t Work The approaches that worked for shadow IT often fail for shadow AI. **Blocking doesn’t scale.** You can’t block every AI tool—new ones appear daily. Employees use personal devices. VPNs and proxies circumvent network controls. Small businesses face the highest risk, with 27% of employees in companies with 11-50 workers using unsanctioned tools. These organizations average 269 shadow AI tools per 1,000 employees while lacking the security resources to monitor them. **Policies aren’t enough.** Acceptable use policies help, but they rely on employees reading, understanding, and following them. When AI makes someone dramatically more productive, policy compliance becomes an afterthought. According to research, 90% of security leaders themselves report using unapproved AI tools at work—with 69% of CISOs incorporating them into daily workflows. If the people writing the policies aren’t following them, you have a systemic problem. **Training has limits.** Security awareness training can highlight risks, but it can’t prevent every incident. Employees under deadline pressure make expedient choices. ## A Better Approach: Discovery, Governance, and Alternatives Effective shadow AI management requires a multi-pronged approach that acknowledges human nature while protecting organizational interests. **Discovery: See what’s happening.** You can’t govern what you can’t see. Modern [shadow AI discovery](/shadow-ai/) involves monitoring network traffic for AI tool usage patterns, analyzing browser extensions and desktop applications, surveying employees about tools they’re using, and reviewing expense reports and credit card statements for AI subscriptions. The goal isn’t surveillance—it’s visibility. You need to know what’s being used so you can make informed governance decisions. **Risk assessment: Prioritize what matters.** Not all shadow AI carries equal risk. Assess each discovered tool against data sensitivity (what data types are being processed?), regulatory exposure (does usage implicate GDPR, CCPA, HIPAA, or SOX?), vendor risk (what are the tool’s data handling practices?), and business impact (how critical is this tool to the workflow?). For a framework on matching governance to risk levels, see our [AI risk heatmap approach](/blog/ai-risk-heatmap/). **Provide sanctioned alternatives.** Heavy-handed blocking drives shadow AI underground. Instead, provide approved alternatives that meet employee needs: deploy enterprise AI tools with proper data protection, negotiate data processing agreements with AI vendors, configure guardrails like PII detection and content filtering, and communicate what’s available and how to access it. When approved tools are easy to use and meet employee needs, shadow AI becomes less attractive. **Continuous monitoring.** Shadow AI isn’t a one-time problem to solve—it’s an ongoing challenge to manage. Establish regular discovery scans to identify new tools, usage monitoring for sanctioned tools, incident response procedures for policy violations, and feedback loops to understand why employees seek alternatives. According to Delinea’s 2025 report, 44% of organizations with AI usage struggle with business units deploying AI solutions without involving IT and security teams. That gap needs ongoing attention. ## The Role of Governance Ultimately, shadow AI is a symptom of governance gaps. Organizations that struggle with shadow AI often lack visibility (no central inventory of AI tools and usage), policy (no clear guidelines on acceptable AI use), process (no fast-track approval for low-risk AI tools), alternatives (no sanctioned tools that meet employee needs), and culture (no psychological safety to ask “Can I use this?”). Building AI governance isn’t about creating barriers—it’s about creating clarity. Employees want to do the right thing. They just need to know what the right thing is. Our [CISO governance checklist](/blog/ciso-governance-checklist/) provides a comprehensive framework for building these foundations. ## Getting Started If you’re concerned about shadow AI in your organization—and if you’re paying attention to the statistics, you should be—start with a discovery exercise. Survey employees, analyze network traffic, review expense reports. Understand your current exposure before trying to solve it. Assess risks by prioritizing discovered tools by data sensitivity and regulatory exposure. Focus governance efforts on highest-risk usage first—you can’t solve everything at once. Provide alternatives by deploying sanctioned AI tools that meet legitimate employee needs. Make approved tools easier to use than shadow alternatives. If the official path is harder than the unofficial one, you’ll keep losing. Build governance foundations through policies, processes, and monitoring. But start lightweight—you can add sophistication over time. Gartner also predicts that by 2030, 50% of enterprises will face delayed AI upgrades and rising maintenance costs due to unmanaged GenAI technical debt. Building governance now prevents that debt from accumulating. Communicate clearly. Tell employees what’s approved, what’s not, and why. Create a safe channel to ask questions. The [Future of Agentic use case library](https://futureofagentic.com/use-cases/) can help illustrate what good AI governance looks like in practice. ## The Bottom Line Shadow AI is already in your organization. The question isn’t whether it exists, but how you’ll respond. A heavy-handed approach drives usage underground. A permissive approach exposes you to risk. The right approach—discovery, governance, and alternatives—lets you get the benefits of AI while managing the downsides. *Want to understand your shadow AI exposure? [Talk to an expert](/schedule-a-demo/) to see how Olakai helps enterprises discover, assess, and govern AI usage across the organization.* [5 AI Use Cases Every Sales Team Should Know](https://olakai.ai/blog/ai-sales-use-cases/) [How to Measure AI ROI: A Framework for Enterprise Leaders](https://olakai.ai/blog/ai-roi-framework/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Shadow Ai Statistics 2026 Source: /blog/shadow-ai-statistics-2026 [← Back to Olakai's Blog](/blog/) # Shadow AI Statistics 2026: The Governance Crisis Is Already Here ![Shadow AI nodes hidden in an enterprise network revealed by teal detection beams](https://olakai.ai/wp-content/uploads/2026/06/shadow-ai-statistics-2026-featured.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. May 24, 2026 · [AI Governance](https://olakai.ai/blog/category/ai-governance/) Last year, one in five organizations was breached because of shadow AI. The average tab came to $4.63 million, roughly $670,000 more than a conventional incident. The detail that should keep security leaders up at night is not the dollar figure, though. It is that [97% of the breached organizations lacked basic AI access controls](https://www.ibm.com/reports/data-breach), according to IBM’s 2025 Cost of a Data Breach Report. Not because their security teams were careless, but because you cannot put a control around a tool you do not know exists. That is the real shape of the shadow AI problem in 2026. It is not a story about a few reckless employees pasting customer data into a chatbot. It is a story about visibility, and the uncomfortable truth that most enterprises have almost none of it over the AI their own people use every day. The statistics that landed this spring make the scale hard to wave away, and harder still to govern with the policy-first playbooks most companies are reaching for. ## The scale is bigger than most security teams know Start with adoption, because that is where the gap begins. UpGuard’s late-2025 research found that 81% of employees and 88% of security leaders admit to using unapproved AI tools, which means the people writing the policies are breaking them too. Netskope’s data puts a sharper edge on it: 47% of enterprise generative-AI usage now flows through personal, unmanaged accounts, entirely outside corporate data controls, and the average organization logs 223 AI-related data-policy violations every month, a figure that doubled in a year. Layer those findings on top of one another and shadow AI stops looking like an edge case and starts looking like the default operating state of the modern enterprise. Employees adopted AI faster than IT could catalog it, faster than security could vet it, and far faster than legal could write rules for it. The result is a sprawling, invisible layer of AI activity that no single dashboard captures, which is exactly the visibility gap we examined in [our deeper look at shadow AI as an enterprise risk](/blog/shadow-ai-enterprise-risk/). The first step toward governing any of it is simply seeing it, and most organizations cannot. ## It is not just chatbots anymore If unmanaged chatbots were the whole problem, it would be serious but bounded. The 2026 escalation is that shadow AI has grown agents. A [Cloud Security Alliance survey released in April](https://www.businesswire.com/news/home/[redacted]/en/New-Cloud-Security-Alliance-Survey-Reveals-82-of-Enterprises-Have-Unknown-AI-Agents-in-Their-Environments) found that 82% of enterprises had discovered previously unknown AI agents running inside their environments in the past year, and 41% had found them more than once. These are not browser tabs. They are autonomous workflows holding API credentials, reading from production systems, and taking actions without a human approving each step. The risk class is fundamentally different. A rogue ChatGPT session is recoverable and largely contained to whatever a person chose to paste into it. An unknown agent with standing access to your finance or HR systems is a far larger exposure, and most governance tooling built for the assistive era simply cannot see it. Watching the browser layer was never going to be enough once AI started acting on its own behalf, which is why [monitoring autonomous agents](/agent-iq/) has to be part of the same system of record as everything else, not a separate tool bolted on after the first incident. You cannot govern an agent you have never met. ## Why policies always lag the tools Faced with these numbers, the instinct is to write a policy. Most companies have, and most policies are not working. Gartner found that 69% of cybersecurity leaders either suspect or have confirmed that employees are using prohibited AI tools, and IBM’s data shows only 37% of organizations have any approval process or oversight mechanism for AI in the first place. Protiviti’s 2026 survey put the share of companies with a formal AI governance framework at just 41%. The policies exist on paper at a fraction of companies, and even where they exist, they describe a world the organization cannot actually observe. This is the core failure, and it is a sequencing failure more than a willpower one. Policy-first governance assumes you already know your inventory, that you can name the tools, the users, and the data flows you intend to regulate. Shadow AI breaks that assumption at the foundation. Until you can answer what AI is running, where, by whom, and against which data, every rule you write is aspirational. Olakai was built on the opposite sequence: [complete visibility across the AI stack](/complete-ai-monitoring/) first, governance second, because measurement is not a precondition for governing AI usage so much as it is the substance of it. The companies treating [shadow AI as a demand signal](/blog/shadow-ai-opportunity/) rather than only a threat are the ones discovering which tools their people actually find valuable, then bringing them into the light instead of driving them further underground. ## The market is already responding None of this is lost on the analysts who advise enterprise budgets. [Gartner forecasts that enterprise spending on AI governance platforms will reach $492 million in 2026](https://www.gartner.com/en/newsroom/press-releases/2026-02-17-gartner-global-ai-regulations-fuel-billion-dollar-market-for-ai-governance-platforms) and surpass a billion dollars by 2030, driven by AI regulation expanding to cover three quarters of the world’s economies. Gartner also predicts that 40% of enterprises will suffer a shadow AI security incident by the end of the decade. The market has effectively priced in the problem. The open question is whether your organization gets ahead of it or becomes one of the case studies that justifies everyone else’s budget. For the CISO, that reframes the conversation from defensive to strategic. The point of investing now is not to bolt locks onto a problem after the breach, but to build the visibility that makes every later decision, every policy, every control, enforceable rather than theoretical. That is the case we lay out in detail for security leaders in our [guide to AI governance for CISOs](/use-cases/ciso/), and it starts from the same premise as everything else here: see first, then govern. ## Start with what you can see The shadow AI statistics of 2026 are alarming, but they point to a clear and unglamorous first move. Before the next policy memo, before the next vendor demo, before the next acceptable-use training that 81% of people will ignore anyway, build a complete inventory of the AI your organization is actually running. Assistive tools, coding tools, and autonomous agents all belong in the same picture, because the gaps between them are precisely where the breaches happen. A governance program that begins with measurement is durable. One that begins with policy, against an inventory you cannot see, is theater. The 30-page [governance checklist](/blog/ciso-governance-checklist/) only works once you know what you are governing. **You can’t govern what you can’t see.** [Talk to an expert](/schedule-a-demo/) to see how Olakai gives you a complete, vendor-neutral inventory of every AI tool and agent in your enterprise, so your governance starts from evidence instead of guesswork. [AI Coding Tool ROI: Why Acceptance Rate Is the Wrong Metric](https://olakai.ai/blog/ai-coding-tool-roi-metrics/) [AI’s $725B Capex Reckoning: Prove ROI or Get Cut](https://olakai.ai/blog/ai-capex-reckoning/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## The Cache Tax Source: /blog/the-cache-tax [← Back to Olakai's Blog](/blog/) # The Cache Tax: Where DeepSeek’s Price Increase Is Concentrated ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. August 14, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) From Enterprise AI Weekly, recorded 14 August 2026. On Tuesday I said compute gets more expensive from here and that the falling price per token was the wrong number to watch. I got a couple of messages telling me I was being dramatic, which is fair, and I probably was a little. Then three companies repriced in the same week. ## DeepSeek raised prices for the first time I want to be fair to [DeepSeek](https://www.deepseek.com) here, because they are a large part of the reason any of us have cheap inference at all. They started the price war, and they are the company everybody cites when they tell you AI is getting cheaper. On Wednesday they announced their first ever price increase: peak and off-peak pricing, live from Sunday, with Chinese business hours costing double the rest of the day. Against the flat rate that preceded it, the peak output price is up more than four times. The wording was that this would allocate resources more reasonably, which is a polite way of saying they are short of compute and have started rationing it with price. ## The cache tax There is a second layer to that announcement I have not seen covered anywhere, and honestly I only found it because I went looking at the cache line specifically. This is a little geeky, so bear with me. The headline model prices went up three to four times. The cached input price went up about twelve. Cached input is what agents run on. Long system prompts, the same context re-read every turn, retrieval, tool loops, which is precisely the workload everyone is scaling right now, me included. So the increase is concentrated in the one line item that agentic workloads consume most of, and if you built an agent budget on a cheap cache hit, it changed on Sunday. That is the part worth carrying out of this week, and it is the reason I would go back through any 2027 business case that assumes cost per token keeps falling. ## And it is not only them [Google](https://ai.google) shipped Gemini 3.7 Flash on Thursday at $0.75 per million in and $3.75 out, which is a good price. It is also an introductory price that doubles on 1 January, and to their credit they said so in the launch post, so you can plan around a date. Grok 4.6 shipped at $2 in and $6 out, also good, until you cross 200,000 tokens of context, at which point the whole request reprices at double. Not the overflow, the entire request. That is the one that would catch me out, because nobody sets a context length on purpose. It just grows. ## What that costs in practice Take one realistic agent task at 10 million tokens in and 1 million out. The cheapest model on the market runs you about $1.70. The most expensive runs about $150. That is 89 times, for the same job. I ran it twice because I did not believe it the first time. And the cheapest number on that chart expired that weekend, which is worth remembering the next time you read a post explaining to people who do not buy compute that compute is getting cheaper. An 89x spread on an identical task is a routing decision before it is a procurement decision, and it is the same argument I made about [matching the model to the task](/blog/model-routing-explained/), only with a wider gap and a deadline attached. It is also why [a falling rate card and a rising bill](/blog/your-ai-got-cheaper-your-bill-didnt/) keep coexisting: the menu got cheaper at one end while the workload moved to the other. ## The quote I keep coming back to OpenAI’s enterprise lead told TechCrunch that six months ago every customer conversation was about what the model can do and whether it is good enough. Then he said this: > Our conversations are never about that now. Now the conversations are about we are spending so much. What visibility do you have? What auditability do you have? What token controls do you have? > > OpenAI’s enterprise lead, to TechCrunch That is the company selling you the tokens, describing what its own customers now ask for. They sit on the other side of the invoice from you, and that is what they hear all day. I do not know about you, but I found it more convincing than anything I could have written this week. Customers stopped asking whether it works and started asking what it costs, and in the space of five days three vendors made it cost more. Visibility, auditability, and token controls is a fair description of [what a measurement layer has to do](/complete-ai-monitoring/), and it is notable that the list came from a vendor rather than from me. Those three words are also, roughly, the order in which enterprises acquire the capability: you see the spend, then you can explain it, then you can bound it. Most of the organisations I speak to are somewhere in the first stage and budgeting as though they were in the third. So the check this week is narrow enough to run on Monday. Do you know what share of your token spend is cached input, and would you notice if its price moved under you? Do you know which of [your agents](/agent-iq/) sit above a 200,000 token context on a normal day, given that the threshold reprices the whole request rather than the excess? And is anything in your stack watching the vendors’ own pricing pages, given that one of this week’s three increases was published with a date on it months in advance? None of that requires a project. It requires a record of what you are already spending, broken down far enough to answer a question somebody else set, which is the same reason [an agent portfolio needs splitting by agent](/blog/four-agents-70-percent-of-the-return/) before anyone can say which parts of it earned their keep. If you think I have this wrong, tell me, I welcome that all day long. And if you would rather see your own number than argue about mine, [that is the work](/ai-roi/). [Four Agents Carried 70% of the Return](https://olakai.ai/blog/four-agents-70-percent-of-the-return/) [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Token Cost Metrics Cfo Source: /blog/token-cost-metrics-cfo [← Back to Olakai's Blog](/blog/) # 3 Token Cost Metrics Every CFO Should Be Watching ![CFO analyzing rising AI token costs on laptop in boardroom at night](https://olakai.ai/wp-content/uploads/2026/06/token-cost-metrics-cfo-featured.webp) ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. June 17, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) In May 2026, Uber’s COO Andrew Macdonald said something that should make every CFO uncomfortable. Uber had burned through its entire 2026 AI budget in four months — deploying [Anthropic’s Claude Code to roughly 5,000 engineers](https://fortune.com/2026/05/26/uber-coo-ai-spending-tokens-claude-code/), watching per-engineer token costs hit $500 to $2,000 per month, and reaching April before anyone noticed the year was over. When pressed on the return, Macdonald said: “That link is not there yet.” Meaning Uber — a $140B technology company with sophisticated financial infrastructure — cannot draw a line between its AI spend and any consumer feature shipped to customers. This isn’t a story about Uber being careless. It’s a story about a structural gap that no CFO team was built for. SaaS budgets were predictable: seat count × price, invoiced monthly, trivial to reconcile. Token-based AI consumption is none of those things. It scales with usage, multiplies with agentic workflows, and generates costs that engineering teams incur invisibly throughout the month. By the time finance sees the number, the spending is already done. Uber found out in April. Microsoft found out around the same time and [revoked Claude Code licenses](https://cybernews.com/ai-news/microsoft-claude-code-burn-yearly-ai-budget/) for an entire division effective June 30. These aren’t outliers. According to [Ramp’s April 2026 AI Index](https://ramp.com/leading-indicators/april-2026-ai-index), monthly AI token spend across enterprise customers grew 1,001% from January 2025 to April 2026. The median company now dedicates nearly 15% of its software budget to AI tools. The finance operating model hasn’t caught up. Most AI monitoring tools give CFOs a token dashboard — a view of how many tokens were consumed, by which provider, at what cost. That’s a start. But it’s not a CFO metric. It’s an engineering metric dressed up for the finance team. What CFOs actually need are three different measurements, each one capturing something a token dashboard deliberately ignores. ## Why This Is Different From Every SaaS Budget You’ve Managed Before The shift from seat-based to token-based pricing is more disruptive to financial planning than it looks. Seat costs are a fixed overhead — you know the number on the first of the month. Token costs are a variable that compounds with behavior. The more your engineers use AI, the more capable and dependent they become, and the more tokens they consume. [EY estimates that a standard chatbot interaction costs roughly $0.04](https://www.ey.com/en_us/insights/ai/agentic-ai-token-costs). An orchestrated agentic workflow — where AI models call tools, spawn sub-agents, and iterate across multiple reasoning steps — costs approximately $1.20 per interaction. That’s a 30x multiplier, and it’s built into the architecture of where AI is going. [Goldman Sachs projects that agentic AI adoption will drive a 24x increase in global token demand by 2030.](https://www.goldmansachs.com/insights/articles/ai-agents-forecast-to-boost-tech-cash-flow-as-usage-soars) Meanwhile, per-developer token consumption is growing at a pace that defies normal budget forecasting. [TechCrunch reported in June 2026](https://techcrunch.com/2026/06/05/the-token-bill-comes-due-inside-the-industry-scramble-to-manage-ais-runaway-costs/) that per-developer token consumption has grown approximately 18.6x in nine months across enterprise organizations. A Priceline engineer burned $40,000 in tokens in a single month. An unnamed enterprise accumulated a $500M Claude bill. The Linux Foundation has responded by standing up a formal Tokenomics Foundation to create standards for AI token tracking — which is itself a signal that the industry now acknowledges cost runaway as a structural problem, not an edge case. If you don’t have the right instruments in place, you’re flying without gauges in an environment where the turbulence is increasing. Here are the three metrics that change that. ## Metric 1: Cost-Per-Outcome, Not Cost-Per-Token Andrew Macdonald’s admission — “that link is not there yet” — describes exactly what’s missing from every token dashboard on the market. They tell you what you spent. They don’t tell you what you got. And the gap between those two questions is where CFOs get into trouble. A team burning twice the tokens of the team next to them isn’t necessarily wasteful. They might be twice as productive. Or they might be prompting in circles. You cannot tell from a spend number alone, which is why [cost-per-token is the wrong unit of analysis for a CFO](https://olakai.ai/blog/ai-metrics-that-matter/). The metric that matters is cost-per-outcome: the fully-loaded dollar cost of each unit of value produced. For engineering teams, that’s cost per merged pull request, cost per deployed feature, cost per lines of production code shipped. When you measure at this level, the teams consuming the most tokens often look very different than you’d expect. Jellyfish’s research found that heavy AI users were twice as productive as their peers but consumed ten times more tokens. At the token level, they look expensive. At the outcome level, they’re your most cost-efficient engineers. Only 14% of CFOs report they’ve seen clear, measurable AI ROI (RGP, 200 US finance chiefs) — the primary reason is that they’re measuring inputs, not outputs. Cost-per-outcome is what [CFOs actually need from AI measurement](https://olakai.ai/use-cases/cfo/) to make budget decisions that hold up to board scrutiny. ## Metric 2: Spend Run-Rate Forecast, Not Month-to-Date Total Month-to-date spend is a rearview mirror. By the time April’s actuals landed in Uber’s financial system, the year was already gone. What every CFO needs — and almost none have — is a forward-looking signal: at the current trajectory, when do we exhaust this budget? This is the difference between a smoke alarm and a fire report. MTD is the fire report. Run-rate forecast is the smoke alarm. The reason this matters so urgently right now is the 18.6x nine-month consumption growth rate. Token spend doesn’t grow linearly. It grows exponentially as more engineers adopt AI tools, as those engineers use them for more complex tasks, and as agentic workflows multiply the token cost of each interaction. A budget that looked fine in January can be 40% consumed by February if adoption accelerates faster than the plan assumed. The answer is a rolling run-rate alert — a projection based on trailing consumption that fires when the month-end trajectory crosses a threshold, not when the limit is already breached. In the Uber scenario, a 7-day trailing average run-rate alert in late January or early February would have changed the conversation months before the budget was gone. Budget alerts that fire after the fact aren’t governance — they’re retrospectives. The signal you need fires while there’s still time to adjust. This is the [complete AI monitoring](https://olakai.ai/complete-ai-monitoring/) posture that separates reactive from proactive finance teams. ## Metric 3: Value Leak Rate The Priceline engineer who spent $40,000 in tokens in one month is an interesting problem. Maybe those tokens produced something extraordinary — a complex system design, a breakthrough on a hard architecture problem, intensive research that unblocked the whole team. Or maybe that engineer was prompting in circles, getting low-quality outputs, and abandoning sessions without shipping anything. From a token dashboard, both scenarios look identical. Both show high spend. Neither reveals whether the spend connected to anything the business actually values. Value leak rate measures the share of AI spend that doesn’t connect to a committed output: a merged PR, a deployed commit, a shipped feature. High-spend sessions that end without a commit are the signal. Not because exploration is bad — sometimes the right answer from a session is “don’t build this” — but because a high value leak rate at the account level tells you that a meaningful fraction of your AI spend is disappearing without evidence of production. The nuance matters here. Flagging every high-spend session as waste would punish your most ambitious engineers. The right instrument identifies the pattern: sessions with consistently high spend and no output, compared against a team-median baseline, tracked over time. That’s the difference between an [AI visibility audit](https://olakai.ai/blog/ai-visibility-audit/) and a surveillance tool. One helps CFOs understand where the budget is going. The other just creates resentment. Jellyfish’s data — 2x productivity, 10x token cost for heavy users — makes the case for why you need this ratio, not the raw number. The ratio tells you whether the premium is justified. And if you want [custom AI cost KPIs](https://olakai.ai/analytics-kpis/) that reflect your team’s specific cost structure, the baseline needs to come from your own data, not industry benchmarks. ## What Proactive Finance Teams Are Doing Now The companies that have gotten ahead of this aren’t waiting for the annual budget reconciliation to discover they have a token runaway problem. AT&T achieved 90% cost savings in AI infrastructure after building visibility into where tokens were actually going — not by cutting investment, but by identifying the optimization opportunities that were invisible before. Kumo AI now treats per-engineer token consumption as a tracked R&D expense line, the same way they track compute or software licensing. This framing shifts the conversation from “are we spending too much?” to “are we getting R&D-quality returns on this R&D-level expense?” — which is the right question for a CFO to be asking. Gartner projects that by 2029, CFOs who implement strategic AI deployment will add 10 margin points of growth, and over 40% of agentic AI projects will be canceled before that due to escalating costs and unclear business value. The companies that add those margin points will be the ones that built the measurement infrastructure before the costs compounded. The others will be telling the Uber story about themselves in 2027. The AI P&L is becoming a real thing inside enterprise finance. Token spend, cost-per-outcome, run-rate forecasting, and value leak rate are the line items. The CFOs who define those metrics now, build the instrumentation to track them, and establish the governance to act on them will be in a fundamentally different position than those who wait for the token dashboards to catch up. The gap between tracking spend and understanding value is the gap between a cost center and a competitive advantage. If you’re not tracking these three numbers across your entire AI stack today, [talk to an expert](https://olakai.ai/schedule-a-demo/) about what it takes to get there. [Power, Casual, New, Idle: How Olakai’s Adoption Cohorts Find Your Wasted AI Licenses](https://olakai.ai/blog/adoption-cohorts-wasted-ai-licenses/) [Ask Kai: Inside Olakai’s Conversational Control Plane](https://olakai.ai/blog/ask-kai-conversational-control-plane/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Tokenmaxxing Claudeonomics Source: /blog/tokenmaxxing-claudeonomics [← Back to Olakai's Blog](/blog/) # Tokenmaxxing Is the New Lines of Code: Why Token Leaderboards Won’t Prove AI Value ![Enterprise team at competing workstations in an open-plan office, racing to use AI chat tools — illustrating the tokenmaxxing productivity race](https://olakai.ai/wp-content/uploads/2026/04/tokenmaxxing-competition-featured-1.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. April 15, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Someone at Meta built a leaderboard called *Claudeonomics*. It ranked employees by the number of tokens their AI models processed and generated. Top spenders got rewards. Then it leaked to the press. Then Meta quietly shut it down. That was earlier this month. This week, Reid Hoffman [came out in measured defense](https://techcrunch.com/2026/04/15/reid-hoffman-weighs-in-on-the-tokenmaxxing-debate/) of the practice at Semafor’s World Economy Summit. On the same day, an inference-infrastructure startup called Parasail [raised $32 million](https://techcrunch.com/2026/04/15/parasail-raises-32m-to-feed-tokenmaxxing-ai-developers/) on the thesis that “tokenmaxxing” will create the next compute giant. The company already generates 500 billion tokens a day. If you run an AI program and you haven’t yet been asked by your CEO or your board why your engineers aren’t in the top quartile of token consumption, you will be soon. And when that conversation arrives, you need a better answer than a bigger number. ## What tokenmaxxing actually measures A token is a small chunk of text an AI model processes. Every prompt consumed, every response generated, every line of code auto-completed — they all add up to a token count. “Maxxing” is Gen Z slang for optimizing something to the extreme. Put them together and you get the idea: rank employees by how many tokens they burn, and call the top of the list your best AI adopters. Meta built the internal dashboard. Shopify folded AI usage into performance reviews. Venture capital is now funding the picks and shovels. Hoffman’s defense, notable because he’s one of the more careful voices in the debate, was a cautious endorsement: “You should be getting people at all different kinds of functions actually engaging and experimenting \[with AI\].” He then immediately added that token tracking “doesn’t mean it’s a perfect example of productivity.” Read that second sentence again. The strongest public defender of tokenmaxxing concedes, in the same breath, that it doesn’t measure productivity. Which raises the question everyone at Meta was too polite to ask before the leaderboard leaked: what exactly are we measuring, and why? ## The new lines of code If this pattern feels familiar, it should. For decades, engineering organizations tried measuring developer productivity by lines of code written. The metric was easy to count, easy to rank, and spectacularly broken. Engineers who wrote terse, elegant code scored poorly. Engineers who produced verbose, repetitive code scored well. Every competent engineering leader learned the lesson the hard way: when you turn an input metric into a target, people optimize for the metric, not the work. Economists call this Goodhart’s Law. When a measure becomes a target, it ceases to be a good measure. Token consumption is lines of code with a fresh coat of paint. It’s an input. It’s easy to count. And it tells you almost nothing about whether the work the AI produced was useful, correct, or worth the compute bill that came with it. The cynical version of tokenmaxxing plays out predictably. Employees pad their AI usage with throwaway prompts. Managers celebrate the chart going up. Finance sees the OpenAI and Anthropic invoices climbing and asks what changed. Nobody can tell them, because the leaderboard only measures spend. We covered this exact anti-pattern in [AI Metrics That Matter](/blog/ai-metrics-that-matter/) — the gap between what’s easy to count and what a CFO actually wants to see. ## Why it’s seductive anyway Tokenmaxxing isn’t popular because executives are naive. It’s popular because real AI measurement is hard and token counts are sitting right there in the API billing dashboard. When a CEO asks the head of AI whether the organization is actually using its new tools, “we processed 4.2 billion tokens last quarter, up 340%” is a satisfying answer to give. It’s specific. It’s directional. It trends up and to the right. It’s also, as NVIDIA’s recent survey of 3,200 enterprise leaders revealed, roughly the level of measurement most organizations have settled for. As we covered in our analysis of the [NVIDIA State of AI report](/blog/nvidia-ai-report-roi-measurement/), 30% of enterprises still cannot measure the ROI of their AI investments at all. Token counts are what you reach for when you’ve given up on measuring the thing you actually care about. The other reason tokenmaxxing spreads is that it pushes a real problem — AI adoption — through an easy pipe. In most enterprises, the gap between AI tool licenses purchased and AI tools actually used by employees is enormous. Licenses go unclaimed. Copilots go idle. Shadow AI proliferates in the gap. Counting tokens at least tells you who’s trying something. But “trying something” is a foundation for measurement, not its destination. ## What outcome-based measurement looks like The measurement you want isn’t on the API invoice. It’s in the business system the AI was supposed to change. If your developers are using AI coding tools, the question isn’t how many tokens they generated — it’s whether cycle time dropped, whether pull request quality held, whether production incidents stayed flat. If your sales team is using an AI assistant, the question is whether deal velocity improved, not whether reps sent more prompts. This is the measurement layer missing from almost every tokenmaxxing dashboard we’ve seen. It’s also the layer that [Coding IQ](/coding-iq/) and the rest of the Olakai platform exist to provide. The question we ask our customers to answer isn’t “how much AI did you use?” It’s “what did your AI produce, for whom, and at what business outcome?” Those three questions are the ones a CFO will ask when the bill arrives, and the ones a CISO will ask when governance gets challenged. We built an entire framework around this. We call it [SEE → MEASURE → DECIDE → ACT](/blog/enterprise-ai-roi-playbook/). SEE surfaces every AI tool in use, not just the sanctioned ones. MEASURE ties usage to business KPIs the executive team already cares about. DECIDE gives you the evidence to scale, fix, or kill each pilot. ACT turns the answers into an operating rhythm instead of a once-a-quarter scramble. None of those steps begin with token counts. All of them produce numbers your board will actually recognize as value. ## The governance blind spot There’s a second problem with tokenmaxxing that rarely gets discussed. A leaderboard that rewards token spend creates an incentive to bypass governance controls to get more of it. Employees who find a sanctioned tool too slow, too throttled, or too narrow in capability will reach for something unsanctioned. Shadow AI already grew fast in the absence of measurement. Adding a scoreboard that rewards consumption accelerates it. This is the worry that haunts every CISO we talk to, and it’s why [the CFO view](/use-cases/cfo/) and the CISO view of AI can’t live in separate dashboards. You cannot measure AI ROI without measuring AI risk, because the risk is the other half of the cost. Tokenmaxxing, by design, only counts one side. ## Getting started: audit what your AI produces, not what it consumes If your organization is under pressure to show AI adoption and you’re being nudged toward tokenmaxxing, there’s a better first step. Pick the three most visible AI deployments in your organization — coding assistants, a customer support copilot, a sales enablement tool — and, for each one, write down the business outcome it was supposed to change. Cycle time. First-contact resolution. Win rate. Whatever it is, write it down. Then measure whether the outcome moved. Our [AI ROI framework](/blog/ai-roi-framework/) walks through this end to end. Do that for three deployments and you’ll know more about the real state of AI in your organization than any token leaderboard will tell you. You’ll also have the beginnings of a measurement system that survives the next wave of AI hype, whatever it gets called. Lines of code didn’t survive the last one. Tokenmaxxing won’t survive this one. Outcomes always do. Olakai helps enterprises measure what their AI is actually producing — across every tool, every user, every workflow — and tie it back to the business KPIs executives already track. If tokenmaxxing is the conversation your board is having, we can help you lead a better one. [Talk to an expert](/schedule-a-demo/). [Inside the AI Impact Dashboard: How Olakai Turns PR Data Into Proof of AI Value](https://olakai.ai/blog/ai-impact-dashboard-explained/) [The Return of the Desktop App: And the AI Measurement Gap It Creates](https://olakai.ai/blog/desktop-renaissance-ai-measurement-gap/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Uber Ai Budget Blowout Source: /blog/uber-ai-budget-blowout [← Back to Olakai's Blog](/blog/) # Uber Blew Through a Year of AI Budget in Four Months. The Guardrail It Built Next Already Existed. ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. July 31, 2026 · [Industry Analysis](https://olakai.ai/blog/category/industry-analysis/) Before [Uber](https://www.uber.com) capped anything, it did the opposite. The company encouraged employees to use AI coding tools “as much as possible” and put usage on internal leaderboards — a competitive, public ranking of who was generating the most AI activity, with no ceiling attached. Four months later, Uber’s CTO told the company it had blown through its entire annual AI budget. Not a quarter’s worth. Not a soft warning at 80%. The whole year, gone in a third of it, discovered only after the money was already spent. The fix Uber built next is real infrastructure, not a memo. According to [TechCrunch’s reporting](https://techcrunch.com/2026/06/02/uber-caps-employee-ai-spending-after-blowing-through-budget-in-four-months/), Uber now caps AI spending at $1,500 per employee, per month, per agentic coding tool — a separate limit for each of Claude Code and Cursor, so maxing out one doesn’t touch the budget for the other. Employees can track their own usage against the cap through an internal dashboard, and the limit can be exceeded with permission when the work genuinely calls for it. That’s a legitimate governance mechanism, built under real pressure, by a company that clearly has engineers capable of shipping it fast. It’s also the second half of a story worth sitting with: the guardrail arrived after the crash, not before it. ## A flat cap catches the symptom, not the trajectory A per-employee, per-tool monthly ceiling is a real control, and it’s a meaningfully better position than the leaderboard-driven free-for-all that came before it. But it’s also a blunt instrument compared to what a purpose-built [AI spend governance](/coding-iq/) system actually does. A flat cap tells an employee “no” at $1,500 with no earlier signal along the way. It doesn’t distinguish between a team burning through budget in the first ten days of the month versus one pacing evenly across thirty. It has no program-level ceiling sitting above the per-tool one, and no way to see a provider-wide spend trend across every developer at once — just individual caps, checked individually, after the spend has already happened. Compare that to what a real budget mechanism looks like when it’s built to catch the problem before month-end rather than cap it after the fact. [Olakai’s own AI Spend Governance](/blog/inside-ai-spend-governance/) runs a daily run-rate forecast against every budget — program-wide, per-provider, per-developer — and fires two different kinds of alerts: one when actual spend crosses a threshold (50%, 80%, 100% of the limit), and a second, more useful one, when the trajectory alone is on pace to blow through the budget by month-end, even while spend is technically still under the line today. That second alert is the exact warning Uber didn’t have in month three, when the company was still four to six weeks from finding out the hard way. ## The other half of the problem: nobody could say what the spend bought Spend control is only half of what went wrong at Uber, and arguably the easier half to fix. The harder admission came from COO Andrew Macdonald, who said on a podcast that “it’s very hard to draw a line” between the company’s AI usage and the new consumer features it had shipped. That’s a striking thing for a public company’s COO to say on the record — not “AI isn’t working,” but “we genuinely can’t tell.” A company that spent a full year’s AI budget in four months, driven by a leaderboard that rewarded activity over outcome, and then couldn’t connect that activity to a specific shipped result, wasn’t missing a budget cap. It was missing a way to measure value at all. That’s the pattern that should worry a [CFO](/use-cases/cfo/) more than the overspend itself. A budget overrun is a one-time, fixable embarrassment. A COO unable to say whether a year of AI spend produced anything measurable is a standing problem that a hard cap doesn’t touch — Uber can enforce $1,500 per employee per tool forever and still not know if that $1,500 is buying real developer output or just more prompts on a leaderboard. Spend control and value measurement are usually treated as two separate initiatives, owned by two different teams, on two different timelines. They’re actually the same problem: you can’t govern spend you can’t see clearly, and you can’t prove ROI on spend you’re not governing. ## What the next version of this story should look like Uber isn’t unusual for having gone through this — it’s unusual for having said so publicly. Most companies encouraging “use AI as much as possible” this year, with no program-wide budget ceiling and no forecast catching the trajectory early, are somewhere on the same four-month timeline Uber already lived through; they just haven’t hit the wall yet, or haven’t been reported on when they did. The version of this story worth aiming for isn’t “we built a cap after the crisis.” It’s a [unified view](/coding-iq/) of AI coding spend and AI coding value from day one — budgets that alert before the month closes, not after, and a productivity signal sitting right next to the cost one, so nobody’s COO has to say “it’s very hard to draw a line” on a podcast eighteen months in. If your organization is somewhere on that same trajectory — [multiple AI coding tools](/blog/ai-coding-tool-sprawl/), growing spend, and no clear answer yet to “what did this actually buy us” — the better time to build the guardrail is before the invoice, not after. [Talk to an Expert](/schedule-a-demo/) about what proactive AI spend governance looks like against your own provider data. [Build or Buy: The Arithmetic on Running Your Own Model](https://olakai.ai/blog/build-or-buy-self-hosting-ai-cost/) [Four Agents Carried 70% of the Return](https://olakai.ai/blog/four-agents-70-percent-of-the-return/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## What Is Agentic Ai Source: /blog/what-is-agentic-ai [← Back to Olakai's Blog](/blog/) # What is Agentic AI? A Guide for Enterprise Leaders ![Enterprise leader exploring agentic AI concepts](https://olakai.ai/wp-content/uploads/2025/10/what-is-agentic-ai-photo.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. October 8, 2025 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) If you’re an enterprise leader trying to make sense of AI, you’ve likely noticed a shift in the conversation. ChatGPT and copilots were impressive—but now there’s talk of **agentic AI**: systems that don’t just answer questions, but take action to achieve goals. What does this mean for your organization? The numbers suggest this isn’t hype. According to [Gartner](https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025), 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. That’s an 8x increase in a single year. [McKinsey’s 2025 State of AI report](https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai) found that 62% of organizations are already experimenting with AI agents, and 79% say they’ve adopted agents to some extent. This guide cuts through the hype to explain what makes AI “agentic,” how it differs from the chatbots and copilots you’re already using, and what enterprise leaders need to know as autonomous agents become a reality. ## The Evolution of Enterprise AI To understand agentic AI, it helps to see where we’ve been. **Traditional AI (2020-2022)** consisted of machine learning models that predict outcomes based on patterns. Think fraud detection scoring, demand forecasting, or customer churn prediction. These systems were powerful but passive—they required humans to interpret results and take action on the insights they provided. **Chat AI (2023)** brought large language models that respond to prompts with natural language. ChatGPT made AI accessible to everyone, enabling research assistance, content drafting, and customer service chatbots. But these systems had no ability to take action—they could only provide information and leave the execution to humans. **Copilots (2024)** represented AI assistants that augment human work with suggestions and completions. GitHub Copilot, Microsoft 365 Copilot, and Salesforce Einstein GPT define this generation. They’re context-aware and integrated into workflows, but humans remain in control of every decision. The AI suggests; the human decides and executes. **Agentic AI (2025-2026)** introduces autonomous systems that take action to achieve goals with minimal human intervention. These agents don’t wait for prompts—they plan multi-step workflows, use tools and APIs, and execute [end-to-end processes](https://futureofagentic.com/use-cases). For a deeper exploration of how this evolution is unfolding, see our analysis of [enterprise AI’s evolution from prediction to action](/blog/enterprise-ai-evolution/). ## Six Core Characteristics of Agentic AI What makes an AI system truly “agentic”? According to Gartner, autonomous agents are combined systems that achieve defined goals without repeated human intervention, using a variety of AI techniques to make decisions and generate outputs. They have the potential to learn from their environment and improve over time. Look for these six characteristics. **Autonomy** means the system takes action without constant human input. It operates independently within defined boundaries and escalates only when necessary. Think of it like a trusted personal assistant who knows to book your recurring monthly flight without asking each time, but will check with you if prices exceed your usual budget. Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention. **Planning** enables the system to break down complex tasks into actionable steps. It creates execution plans and adjusts based on outcomes and changing conditions. Like a seasoned chef preparing Thanksgiving dinner—they know to start the turkey first, prep sides while it cooks, and adjust timing if guests arrive late. The planning capability is what transforms a responsive system into a proactive one. **Tool Use** allows the system to integrate with other systems via APIs, databases, and applications. It orchestrates multiple tools to complete end-to-end workflows. Think of a general contractor who doesn’t just plan your kitchen remodel—they actually pick up the phone to coordinate electricians, plumbers, and inspectors to get the job done. Agentic AI doesn’t just recommend calling the API; it calls it. **Memory** maintains context across interactions and sessions. The system remembers past decisions, user preferences, and workflow state. Like your family doctor who remembers your medication allergies from three years ago, your preferred pharmacy, and that you respond better to evening appointments. Memory transforms one-off interactions into ongoing relationships. **Reasoning** enables decisions based on goals, constraints, and context. The system evaluates trade-offs and selects optimal actions given the information available. Like a financial advisor who weighs your retirement goals against current cash needs and recommends whether to max out your 401(k) or pay down your mortgage. The reasoning is transparent and auditable. **Learning** allows the system to adapt from feedback, successes, and failures. It improves performance over time through experience and reinforcement. Like a barista who remembers you liked your latte extra hot last time, tries it that way again today, and asks for feedback to get your order perfect every visit. Learning agents get better the more they’re used. For a comprehensive exploration of these characteristics with interactive examples, the [Future of Agentic guide to agent characteristics](https://futureofagentic.com/agentic-ai-101/characteristics/) provides detailed analysis. ## Chat AI vs. Copilots vs. Agents: Key Differences Understanding the spectrum helps you set appropriate expectations. Dimension Chat AI Copilots Agentic AI **Autonomy Level** None—responds only when prompted Limited—suggests but doesn’t execute High—executes multi-step workflows **Human Oversight** 100% (every interaction) 80-90% (review before action) 10-30% (key decision points only) **Task Complexity** Single-turn Q&A Assisted completion Multi-step workflows **Response Time** Seconds Milliseconds to seconds Minutes to hours **Cost per Interaction** $0.001-0.01 $0.01-0.10 $0.10-1.00+ **Risk Level** Low (information only) Medium (human reviews) High (requires governance) While generative AI focuses on creating content such as text, images, or code, agentic AI focuses on action. Adding task specialization capabilities evolves AI assistants into AI agents with the capacity to operate and perform complex, end-to-end tasks. ## Real-World Examples What does agentic AI look like in practice? **Agentic Example: Invoice Processing.** When an invoice exceeds $50K or has mismatched PO numbers, an agentic system automatically flags it, updates the status to “Review Required,” adds a comment explaining the anomaly, and sends a Slack message to the appropriate approver based on department and amount thresholds. No human initiated these steps—the agent made decisions and executed actions autonomously based on policy and context. **Agentic Example: Travel Booking.** An employee submits a trip request: “Book me a flight to San Francisco next Monday, staying until Thursday.” The agent searches flights, books the cheapest option under $500 per company policy, reserves a hotel near the office, creates an expense report pre-filled with trip details, updates the employee’s calendar, and sends a confirmation email with the complete itinerary—all without human intervention. **Not Agentic: Code Completion.** A developer uses an AI-powered code editor that predicts what they’ll type next. The AI suggests function completions, but the developer must explicitly accept each suggestion. This is a copilot pattern—sophisticated assistance, but no autonomous execution. The human remains in the loop for every action. ## Why This Matters for Enterprise Leaders The shift to agentic AI has significant implications that go beyond technology decisions. **Higher stakes.** When agents take action autonomously, mistakes have real consequences. A chatbot that gives wrong information is annoying; an agent that executes wrong actions can cost money, damage relationships, or create compliance issues. Deloitte’s 2025 study found that while 30% of organizations are exploring agentic options and 38% are piloting solutions, only 14% have solutions ready to deploy and just 11% are actively using agents in production. The gap reflects how seriously enterprises are taking the governance requirements. **New governance requirements.** You need visibility into what agents are doing, controls to prevent unauthorized actions, and the ability to audit decisions after the fact. Traditional IT governance wasn’t designed for autonomous systems. Gartner predicts that guardian agents—specialized agents focused on governance and oversight—will capture 10-15% of the agentic AI market by 2030. For a comprehensive framework, see our [AI governance checklist for CISOs](/blog/ciso-governance-checklist/). **Different ROI model.** Agents cost more per interaction but can deliver dramatically higher value by completing end-to-end workflows. The economics shift from “cost per query” to “value per outcome.” In a best-case scenario, Gartner projects agentic AI could generate nearly 30% of enterprise application software revenue by 2035—surpassing $450 billion. For a framework on measuring this value, see our [AI ROI measurement guide](/blog/ai-roi-framework/). **Workforce implications.** Agents won’t replace humans wholesale, but they will change what humans do. Many roles will shift from execution to oversight and exception handling. By 2028, Gartner predicts 33% of enterprise software applications will include agentic AI, enabling 15% of day-to-day work decisions to be made autonomously. Organizations need to prepare their workforce for this shift. ## The Multi-Agent Future Just as monolithic applications gave way to distributed service architectures, single all-purpose agents are being replaced by orchestrated teams of specialized agents. Gartner reported a staggering 1,445% surge in multi-agent system inquiries from Q1 2024 to Q2 2025. By 2028, Gartner predicts 70% of AI applications will use multi-agent systems. This evolution means enterprise AI will increasingly involve ecosystems of specialized agents working together—finance agents, HR agents, security agents, customer service agents—coordinating to complete complex workflows that span organizational boundaries. ## Getting Started with Agentic AI If you’re considering agentic AI for your enterprise, start with low-risk, high-volume use cases. Lead qualification, invoice processing, and IT ticket routing are common starting points where autonomous action delivers clear value with manageable risk. 50% of enterprises using generative AI are expected to deploy autonomous AI agents by 2027, doubling from 25% in 2025. [Build governance from day one](/platform/). Don’t wait until you have a dozen agents to think about visibility, controls, and measurement. Establishing governance foundations early prevents painful retrofitting later. Our [AI risk heatmap framework](/blog/ai-risk-heatmap/) helps you match governance intensity to risk level. Measure what matters. Track not just agent activity but business outcomes: time saved, error rates, cost per transaction, and ROI. Without measurement, you can’t prove value or identify problems before they become crises. Plan for scale. Pilot projects often succeed; scaling is where most enterprises struggle. Consider how your infrastructure, governance, and change management will handle 10x the agents before you need to find out. ## The Bottom Line Agentic AI represents a fundamental shift from AI that informs to AI that acts. For enterprise leaders, this means new opportunities for automation and efficiency—but also new requirements for governance, measurement, and oversight. The enterprises that thrive will be those who embrace agentic AI while building the guardrails to use it responsibly. That means investing not just in the agents themselves, but in the infrastructure to measure their impact, govern their behavior, and ensure they’re delivering real business value. *Ready to implement agentic AI with confidence? [Talk to an expert](/schedule-a-demo/) to see how Olakai helps enterprises measure ROI, govern risk, and scale AI agents responsibly.* [5 AI Use Cases Every Sales Team Should Know](https://olakai.ai/blog/ai-sales-use-cases/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## What Is Ai Analytics Source: /blog/what-is-ai-analytics [← Back to Olakai's Blog](/blog/) # What Is AI Analytics? The Definitive Enterprise Guide ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. March 2, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) Gartner predicts that 40% of agentic AI projects will be canceled by 2027 due to unclear business value. BCG’s 2025 AI Radar survey of 1,803 C-suite executives found that only 25% of companies report realizing significant value from their AI investments. Thomson Reuters reported in 2026 that just 18% of organizations formally track AI ROI. These are not isolated findings. They describe a structural gap in how enterprises manage AI: the gap between deploying AI and actually measuring whether it works. AI analytics is the discipline that closes that gap. ![The Enterprise AI Measurement Gap: BCG reports only 25% of companies see significant AI value, PwC finds 56% of CEOs report no revenue increase from AI, and Thomson Reuters shows only 18% of organizations formally track AI ROI.](https://olakai.ai/wp-content/uploads/2026/03/ai-analytics-measurement-gap.webp) The measurement gap: most enterprises invest in AI but cannot prove it works. ## What Is AI Analytics? AI analytics is the practice of measuring the usage, performance, cost, and business impact of artificial intelligence tools across an enterprise. It answers the questions that every CIO, CFO, and board member is now asking: What AI are we using? How much is it costing us? And what are we getting back? Traditional business intelligence measures the outputs of human processes. AI analytics measures the outputs of AI-augmented and AI-automated processes. This includes everything from how often employees use a chatbot like ChatGPT or Copilot, to the success rate and cost-per-execution of autonomous agents running multi-step workflows in production. The distinction matters because AI adoption has outpaced AI measurement by years. Most enterprises now have dozens of AI tools in active use, each with its own vendor dashboard or no analytics at all. AI analytics provides a unified, vendor-neutral view across all of them, and the data it runs on is what we call an [AI system of record](/blog/what-is-an-ai-system-of-record/). ## Why AI Analytics Matters Now The urgency is driven by three converging forces. **The ROI reckoning.** [Deloitte’s State of AI 2026 survey](https://www.deloitte.com/us/en/about/press-room/state-of-ai-report-2026.html) of 3,235 business and IT leaders found that 74% of organizations want AI to grow revenue, but only 20% have actually seen it happen. PwC’s 2026 Global CEO Survey found that 56% of CEOs report no revenue increase from AI. Boards are no longer willing to fund AI programs on faith. They want numbers. AI analytics provides those numbers. **The agentic AI wave.** Deloitte projects that agentic AI usage will surge from 23% to 74% of enterprises within two years. Unlike chatbots that wait for human prompts, [agentic AI](/blog/what-is-agentic-ai/) takes autonomous actions: executing workflows, calling APIs, making decisions. An ungoverned chatbot gives a bad answer. An ungoverned agent executes a bad decision at scale. Measuring agent performance is not optional. It is the difference between a controlled deployment and an operational risk. **The shadow AI problem.** Employees are adopting AI tools faster than IT can track them. [Shadow AI](/blog/shadow-ai-enterprise-risk/) creates blind spots in security, compliance, and cost management. AI analytics starts with visibility: discovering what AI is actually being used, by whom, and for what purpose. ## The Four Pillars of AI Analytics A complete AI analytics practice spans four areas. Each one addresses a different question that enterprise leaders need answered. ![The Four Pillars of AI Analytics: Usage and Adoption, Performance and Quality, Cost and ROI, Risk and Governance.](https://olakai.ai/wp-content/uploads/2026/03/ai-analytics-four-pillars.webp) The four pillars of a complete AI analytics practice. ### 1\. Usage and Adoption Analytics This is the foundation: understanding what AI tools are in use across the organization and how deeply they are being adopted. Usage analytics answers questions like: How many employees actively use ChatGPT? Which teams have adopted Copilot? What percentage of licensed AI tools are actually being used? Without usage data, enterprises operate blind. They cannot optimize license spend because they do not know which tools are underutilized. They cannot identify [shadow AI](/shadow-ai/) because they do not have a baseline of sanctioned usage to compare against. According to Deloitte, workforce access to sanctioned AI tools expanded from under 40% to roughly 60% of employees in a single year. That growth rate makes continuous usage tracking essential. ### 2\. Performance and Quality Analytics Beyond knowing that AI is being used, enterprises need to know whether it is performing well. Performance analytics measures the quality and reliability of AI outputs across tools and use cases. For assistive AI (chatbots and copilots), this includes response accuracy, user satisfaction, and task completion rates. For agentic AI, it includes execution success rates, failure analysis, and decision quality. A custom agent that processes insurance claims might have a 94% success rate, but the 6% failure rate could represent millions in incorrectly handled claims. Performance analytics surfaces these patterns before they become problems. ### 3\. Cost and ROI Analytics This is where AI analytics becomes strategic. Cost analytics tracks the total cost of AI operations: API calls, compute, licensing, and human oversight time. ROI analytics ties those costs to business outcomes: revenue influenced, time saved, cost avoided, error reduction. BCG found that [60% of enterprises do not track financial KPIs for their AI programs](https://www.bcg.com/publications/2025/ai-radar). This means the majority of organizations cannot answer the most basic question their CFO will ask: Is our AI investment paying off? [AI ROI measurement](/ai-roi/) is the capability that separates enterprises scaling AI from those stuck in pilot purgatory. The math is straightforward but requires instrumentation. If a customer service AI handles 10,000 tickets per month at $0.12 per interaction and replaces a process that previously cost $8.50 per ticket with human agents, the monthly savings are $83,800. Without AI analytics, that number is an estimate. With it, that number is auditable and provable to a board. ### 4\. Risk and Governance Analytics The fourth pillar connects analytics to [governance](/ai-governance/). Risk analytics monitors AI usage for policy violations, data exposure, bias indicators, and compliance gaps. It answers questions like: Are employees sharing sensitive data with AI tools? Are autonomous agents operating within defined guardrails? Are AI outputs meeting regulatory requirements? This pillar is increasingly non-negotiable. The EU AI Act mandates risk-based oversight. The NIST AI Risk Management Framework provides voluntary guidance that is rapidly becoming the de facto standard in the United States. Companies in regulated industries such as financial services, healthcare, and government cannot scale AI without demonstrating continuous risk monitoring. ## AI Analytics vs. Traditional Observability Engineering teams are familiar with observability tools like Datadog, New Relic, and Splunk. These tools monitor infrastructure: server uptime, latency, error rates, and throughput. They are necessary but insufficient for AI programs. AI analytics differs from traditional observability in three fundamental ways. **It measures business outcomes, not just technical metrics.** Datadog can tell you that an API call to GPT-4 took 1.2 seconds. AI analytics tells you that the same call saved a sales rep 14 minutes of research and contributed to a deal worth $240,000. The audience is the CIO and CFO, not only the engineering team. **It spans tools and vendors.** Each AI vendor provides metrics for its own tool. Microsoft shows Copilot usage. OpenAI shows ChatGPT usage. Salesforce shows Einstein usage. But no vendor will ever show you the cross-vendor picture, because that is not in their interest. AI analytics provides vendor-neutral visibility across the entire AI ecosystem. **It connects usage to governance.** Traditional observability does not care whether an employee pasted customer PII into a chatbot. AI analytics does. The integration of usage data, risk signals, and governance policy into a single platform is what makes AI analytics a strategic capability rather than just another dashboard. ## What to Measure: Key AI Analytics Metrics The specific metrics that matter depend on the type of AI being measured and the audience consuming the data. Here is a framework organized by stakeholder. ### For the CIO and Board - **AI ROI by business unit**: Revenue influenced, cost saved, and time recovered, broken down by department or function - **Adoption rate**: Percentage of employees actively using AI tools, tracked over time - **AI maturity score**: A composite metric reflecting how effectively the organization uses AI across adoption, measurement, and governance - **Risk posture**: Number and severity of policy violations, shadow AI instances, and compliance gaps ### For the CFO - **Total cost of AI**: All-in spend across licensing, API usage, compute, and personnel - **Cost per AI interaction**: What each chatbot conversation, agent execution, or copilot suggestion costs - **License utilization**: Percentage of paid AI licenses that are actively used. Low utilization signals wasted spend. - **ROI by AI initiative**: For each major AI program, what is the measurable return relative to the investment? ### For the CISO - **Shadow AI inventory**: Unauthorized AI tools in use, how many users, what data they access - **Data exposure incidents**: Instances of sensitive data shared with AI tools - **Policy compliance rate**: Percentage of AI interactions that comply with content and data policies - **Agent guardrail adherence**: For autonomous agents, how often do they operate within defined boundaries? ### For Engineering and AI Teams - **Agent success rate**: Percentage of agent executions that complete successfully - **Latency and throughput**: Response times and processing capacity - **Error classification**: Types and frequency of AI failures, broken down by cause - **Model comparison**: Performance and cost differences across AI models and vendors for the same task ## How to Build an AI Analytics Practice Organizations typically progress through four stages when building an AI analytics capability. Understanding where you are today helps determine the right next step. ![The Four Stages of AI Analytics Maturity: Stage 1 Visibility, Stage 2 Measurement, Stage 3 Optimization, Stage 4 Governance at Scale.](https://olakai.ai/wp-content/uploads/2026/03/ai-analytics-four-stages.webp) Building AI analytics capability: from visibility to governance at scale. ### Stage 1: Visibility The first step is simply knowing what AI is in use. Most enterprises are surprised by the results of an [AI visibility audit](/blog/ai-visibility-audit/). Shadow AI is nearly universal: employees are using AI tools that IT has not sanctioned, often with company data. Stage 1 focuses on discovery and inventory: building a complete picture of the AI tools, users, and data flows across the organization. ### Stage 2: Measurement Once you have visibility, you can start measuring. This means defining [the metrics that matter](/blog/ai-metrics-that-matter/) for each AI initiative and instrumenting systems to capture them. The key shift at this stage is moving from vanity metrics (number of prompts, number of users) to value metrics (time saved, revenue influenced, cost avoided). Olakai’s [SEE, MEASURE, DECIDE, ACT framework](/blog/enterprise-ai-roi-playbook/) provides a structured approach to this transition. ### Stage 3: Optimization With measurement in place, enterprises can make data-driven decisions about their AI programs. Which tools deliver the highest ROI? Which pilots should scale to production? Which agents should be retired? [Structured pilot programs](/blog/30-day-ai-pilot/) with clear success criteria replace the ad hoc experimentation that traps most organizations in pilot purgatory. Optimization also includes cost management: identifying redundant tools, right-sizing API usage, and negotiating vendor contracts with actual usage data. ### Stage 4: Governance at Scale The final stage integrates analytics with governance. As AI programs grow from a handful of pilots to hundreds of production deployments, the analytics framework must support policy enforcement, compliance reporting, and [risk management](/ai-governance/) at scale. This is where organizations move from reactive oversight (responding to incidents) to proactive governance (preventing them). Analytics provides the continuous monitoring that makes proactive governance possible. ## The Vendor-Neutral Imperative One of the most common mistakes enterprises make is relying on AI vendors to provide their own analytics. Microsoft offers Copilot usage dashboards. OpenAI offers a usage portal for ChatGPT Enterprise. Salesforce shows Einstein adoption metrics. Each provides useful data about its own tool. None will ever provide the cross-vendor picture. This is not a criticism of those vendors. It is a structural limitation. Microsoft has no incentive to show you that a competitor’s tool outperforms Copilot for a given use case. OpenAI has no incentive to help you discover that your team stopped using ChatGPT and switched to Claude. The only way to get an honest, complete picture of AI performance across your organization is through a vendor-neutral analytics platform that sits above individual tools. Olakai was built specifically for this purpose. The platform provides [unified visibility](/complete-ai-monitoring/) across chatbots, copilots, agents, and AI-enabled SaaS, with [custom KPIs](/analytics-kpis/) tied to business outcomes rather than vendor-specific metrics. ## Frequently Asked Questions ### What is the difference between AI analytics and AI observability? AI observability focuses on the technical performance of AI systems: latency, error rates, model accuracy, and infrastructure health. AI analytics extends beyond technical metrics to include business outcomes, ROI measurement, cost analysis, and governance. Observability tells you whether the system is running. Analytics tells you whether it is delivering value. ### How do you measure AI ROI? AI ROI is measured by comparing the total cost of an AI initiative (licensing, compute, API calls, implementation, and human oversight) against the measurable business value it creates (time saved, revenue influenced, cost avoided, error reduction). The key is instrumenting AI systems to capture both sides of this equation continuously, not just during quarterly reviews. Olakai’s [AI ROI measurement](/ai-roi/) capability automates this process across all AI tools. ### What is shadow AI and why does it matter for analytics? Shadow AI refers to AI tools used by employees without IT approval or oversight. It matters for analytics because you cannot measure what you cannot see. If 30% of your AI usage is happening in unsanctioned tools, your analytics are incomplete, your cost estimates are wrong, and your security posture has blind spots. [Shadow AI detection](/shadow-ai/) is typically the first step in building an AI analytics practice. ### Do you need a dedicated platform for AI analytics? For organizations with one or two AI tools, vendor-provided dashboards may suffice. For enterprises using multiple AI tools across multiple teams, vendor dashboards create fragmented, siloed views. A dedicated AI analytics platform provides the unified, vendor-neutral perspective needed to make strategic decisions about the AI program as a whole, not just individual tools in isolation. ### What industries benefit most from AI analytics? Every industry deploying AI at scale benefits from analytics, but the urgency is highest in regulated industries. [Financial services](/industries/financial-services/), [healthcare](/industries/healthcare-life-sciences/), and government face regulatory requirements that demand continuous monitoring and audit-ready evidence. [Technology companies](/industries/technology-software/) benefit from the ROI optimization angle: understanding which AI investments deliver the highest return. ## Key Takeaways - AI analytics is the practice of measuring AI usage, performance, cost, and business impact across an enterprise - Only 25% of companies report significant value from AI (BCG), and only 18% formally track AI ROI (Thomson Reuters). The measurement gap is the primary barrier to scaling AI programs. - The four pillars are usage analytics, performance analytics, cost and ROI analytics, and risk and governance analytics - AI analytics differs from traditional observability by measuring business outcomes, spanning vendors, and integrating governance - Vendor-neutral analytics is essential because no AI vendor will provide an honest cross-vendor picture - Building an AI analytics practice follows four stages: visibility, measurement, optimization, and governance at scale **[Talk to an expert](/schedule-a-demo/)** to see how Olakai provides vendor-neutral AI analytics across your entire AI ecosystem. [The 30-Day AI Pilot That Actually Proves Value](https://olakai.ai/blog/30-day-ai-pilot/) [AI ROI EP. 4: ACT — From Approved Pilot to Enterprise-Wide Impact](https://olakai.ai/blog/ai-roi-act-framework/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## What Is An Ai System Of Record Source: /blog/what-is-an-ai-system-of-record [← Back to Olakai's Blog](/blog/) # What Is an AI System of Record? ![Executive between legacy ledger books and a modern glass office, representing the AI system of record](https://olakai.ai/wp-content/uploads/2026/09/what-is-an-ai-system-of-record-featured.webp) ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot-150x150.webp) [Xavier Casanova](https://www.linkedin.com/in/xaviercasanova) Founder & CEO Building the intelligence layer that makes enterprise AI accountable. September 10, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) **AI system of record, defined:** An AI system of record is the authoritative, vendor-neutral record of everything an organization’s AI does. It captures every AI interaction and outcome, from coding agents to assistants to autonomous agents, down to the token and its cost, and structures it into one data model by user, team, department, project, agent, model, and vendor, kept over time. Every system of record a large company runs today was built on the same assumption: a person types something in. A recruiter enters a new hire into the HR system, a sales rep logs a call in the CRM, and an accountant posts a journal entry to the general ledger. Even Git, the system of record for source code, only changes when a developer commits. These records are trustworthy because people are required to feed them, and their volume is bounded by how fast people work. AI breaks that assumption. A single engineer running a coding agent can generate thousands of model calls in an afternoon, an autonomous agent can work through a queue of support tickets overnight with nobody watching, and employees paste documents into chat assistants the company never approved. None of that activity gets typed into anything. It is scattered across vendor consoles, cloud bills, and browser tabs, and each of those sources sees only its own slice. That is why most enterprises can tell you exactly how many laptops they own, but not how many AI tokens they consumed last month, who consumed them, or what came back. An AI system of record closes that gap, and it is the first system of record that nobody types into. ## What Is a System of Record? The term is older than the cloud. In enterprise IT, a system of record is the authoritative source for a given kind of data: the one place everyone agrees is correct when two reports disagree. Geoffrey Moore popularized the contrast between systems of record, the transactional backbones such as finance, HR, and CRM that keep a business accurate, and systems of engagement, the collaborative tools people use every day; Josh Bersin’s [2012 piece in Forbes](https://www.forbes.com/sites/joshbersin/2012/08/16/the-move-from-systems-of-record-to-systems-of-engagement/) is a good primer on the distinction. Systems of record are not supposed to be exciting. Their job is to be complete, consistent, and durable, so that every report, audit, and decision downstream starts from the same numbers. Most enterprises already run one for each thing that matters to them, and each one answers a question nobody else in the company is allowed to answer differently. Domain System of record The question it answers People HRIS, such as [Workday](https://www.workday.com) Who works here, in what role, and at what cost? Customers CRM, such as [Salesforce](https://www.salesforce.com) Who are our customers, and what have we sold them? Money General ledger or ERP What did we spend and earn, and where? Code Git What changed, when, and who changed it? AI AI system of record What did our AI do, what did it cost, and what did it produce? *Where an AI system of record sits alongside the systems of record enterprises already run.* The last row differs from the others in three ways. Its data is captured automatically rather than entered by people, so completeness depends on coverage rather than on discipline. It spans vendors by design, because no company runs all of its AI through one provider. And its basic unit is the individual interaction and the tokens behind it, which means the volume is measured in millions of events rather than thousands of transactions. ## Why AI Spend Tracking and Governance Now Need a System of Record Enterprises got through the first years of generative AI without one, mostly because the spend was small and the usage was experimental. Three shifts have ended that. ### AI usage is fragmented across vendors Most organizations believe they know what AI they run, and most are wrong. In a [Cloud Security Alliance survey](https://cloudsecurityalliance.org/press-releases/2026/04/21/new-cloud-security-alliance-survey-reveals-82-of-enterprises-have-unknown-ai-agents-in-their-environments) published in April 2026, 68% of respondents reported high confidence in their visibility into AI agents, yet 82% had discovered previously unknown agents in their environment in the past year. Part of the reason is structural, since each AI vendor reports on itself and only itself. The [OpenAI](https://openai.com) admin console knows about OpenAI, the coding tool’s dashboard knows about the coding tool, and none of them can see the AI apps employees adopted on their own, which is where [shadow AI](/shadow-ai/) lives. Adding up the vendor totals by hand gives you a number, but not one anyone can trace back to a team or a decision. ### Cost moved from seats to tokens Even GitHub Copilot, long the archetypal per-seat AI tool, has changed models: [GitHub announced](https://github.blog/news-insights/company-news/github-copilot-is-moving-to-usage-based-billing/) in April 2026 that all Copilot plans would move to usage-based billing on June 1, with credits consumed based on token usage, including input, output, and cached tokens. A seat license is predictable, and procurement can manage it with a spreadsheet. Usage-based pricing ties spend to behavior, so a handful of power users or one runaway agent can move the monthly bill more than the rest of the company combined. Falling unit prices do not fix this, because usage grows faster than prices fall, a pattern we traced in [Your AI Got Cheaper. Your Bill Didn’t.](/blog/your-ai-got-cheaper-your-bill-didnt/) Once spend is variable, it has to be recorded at the level where it varies, which is the interaction and the token. ### Agents act without a human in the loop The same Cloud Security Alliance survey found that nearly two in three organizations (65%) had experienced AI agent-related incidents in the previous 12 months. When a person uses an assistant, there is at least someone who can explain what happened. When an agent opens a pull request, resolves a ticket, or calls an external API on its own, the only account of what it did is whatever was recorded at the time. Governance, audit, and incident response all depend on that account existing, being complete, and being kept somewhere the agent’s own vendor does not control. There is also a strategic reason underneath the operational ones. Models and compute are available to every competitor on the same terms, but the record of how your own organization uses AI, and what it gets back, has no market and cannot be bought, an argument Paul Brzozowski develops in [ClaudeForce, and the One AI Input You Cannot Buy](/blog/claudeforce-ai-system-of-record/). ## The Five Properties of an AI System of Record Plenty of tools hold some AI data. What makes a record a system of record is a specific set of properties, and each one exists because a particular question becomes unanswerable without it. ### 1\. Complete capture Without complete capture, every number is a partial number presented as a total. The record has to cover coding agents, workforce assistants, and autonomous agents, sanctioned tools and unsanctioned ones, because the questions leaders ask are about all of their AI rather than the part that happened to be convenient to instrument. A record that sees only approved tools produces confident answers about the wrong total, which is worse than having no answer at all. ### 2\. Cost down to the token, reconciled to the bill Without token-level cost, AI ROI has no denominator. Multiplying token counts by list prices gives a useful estimate, but discounts, caching, and committed-use agreements all change what you actually pay. The finance-grade version reconciles usage to the provider’s real invoice, as we described for [Google Vertex AI cost reconciliation](/blog/google-vertex-ai-cost-reconciliation/), and a good record labels which figures are reconciled bills and which are estimates, so nobody mistakes one for the other. ### 3\. One data model with attribution Without attribution, you know what was spent but not by whom or for what. Every interaction needs to resolve to a user, a team, a department, a project, an agent, a model, and a vendor, in one consistent schema rather than one per tool. Those dimensions overlap by design: the same dollar legitimately belongs to a developer, their department, their project, and the provider all at once, which is what lets a CFO and an engineering lead look at the same record from different angles and still reconcile. That structure is what makes [AI spend governance](/blog/inside-ai-spend-governance/) and cost attribution possible. ### 4\. Outcomes linked to activity Without outcomes, you are measuring activity and calling it value. Tokens are an input, and ranking teams by how many they burn rewards consumption rather than results, which is the trap we described in [Tokenmaxxing Is the New Lines of Code](/blog/tokenmaxxing-claudeonomics/). A system of record ties interactions to the outcomes the business cares about, such as merged pull requests, resolved tickets, or hours returned to a team, through [custom KPIs](/analytics-kpis/) the business defines itself. That link is what turns a usage log into [AI ROI measurement](/ai-roi/). ### 5\. Vendor-neutral and durable Without neutrality and history, you cannot compare vendors or look back. A provider’s own console is structurally one-sided, since it has no reason to show you where a competitor does the same work for less. The record also has to persist: trends, month-end forecasts, and year-over-year comparisons all depend on history, and so does the audit trail that [AI governance](/ai-governance/) requires, particularly now that [the EU AI Act is enforceable](/blog/eu-ai-act-enforcement-august-2026/). ## What an AI System of Record Is Not The phrase is used loosely, and several adjacent categories get mistaken for it. The distinctions matter when you are deciding what to buy or build. Often confused with What it does Why it is not the record An AI dashboard Displays charts built on some underlying data A dashboard is a view. The record is the data itself, kept over time, that any view can be built from. A vendor admin console Reports usage and billing for one provider’s product It sees one slice of your AI and has no reason to show you the rest. LLM observability and tracing Traces prompts, latency, and errors inside applications an engineering team instruments It covers the apps a team chose to instrument, for engineers, with no view of workforce tools, shadow AI, or cost against outcomes. An “agent system of record” workspace Gives agents and people a shared place to do work on common data That is where AI works. An AI system of record is the account of what AI did, wherever it worked. AI on top of existing systems of record Uses models to read and act on CRM, ERP, or HR data That puts AI on a record. It does not keep a record of the AI. *How an AI system of record differs from adjacent categories.* The last row deserves a second look, because the two ideas reinforce each other. The more AI reads and writes to your CRM, your ERP, and your codebase, the more consequential its actions become, and the more you need an independent record of what it did there. The relationship with AI analytics is simpler: [AI analytics](/blog/what-is-ai-analytics/) is the practice of measuring AI usage, cost, and impact, and the AI system of record is the data that practice runs on. ## The Hard Parts of Building One Capturing AI activity is the tractable part. The difficulty is making the record accurate enough that finance, security, and engineering all accept it, and anyone evaluating an approach, including ours, should ask how it handles the following. ### Attribution when the provider does not know the person Provider data often identifies an API key rather than a human. OpenAI, for example, reports usage per key, so a key shared by a team or a service collapses many people into one line. Resolving identity means joining several signals, such as single sign-on identity, commit emails, and device mapping, and a trustworthy record shows the portion it could not attribute instead of quietly spreading it across everyone. ### Estimates versus bills Token counts arrive in near real time, while reconciled costs can lag by a day or more; Google’s billing export, for instance, typically trails usage by 24 to 48 hours. A record needs both: the estimate for timely alerts and the reconciled figure for the numbers that go to finance, each clearly labeled. ### Seats and tokens do not add up cleanly Workforce assistants are still mostly licensed per seat, while coding agents and model APIs increasingly bill per token, and some tools, like GitHub Copilot in 2026, switch from one to the other. Forcing both into one cost-per-interaction figure produces a number that looks precise and is not. Seat costs have to be allocated, for example pro-rated across the projects a licensed user works on, and kept distinct from metered usage. ### What gets captured, and where it lives This is the first question a CISO asks, and it should be. Detecting sensitive data in prompts requires seeing prompt content, while measuring adoption of a long tail of AI apps only needs metadata such as the app, the user, and the time. A well-designed record captures content only where it is needed, supports redaction of sensitive fields before data leaves the application, and can be deployed inside the customer’s own infrastructure when regulation or contracts require it. The record is itself sensitive data, and it has to be governed like any other system of record, a standard we hold ourselves to on our [Trust & Security](/trust/) page. ### Linking activity to outcomes Counting tokens is mechanical, but deciding what a pull request or a resolved ticket was worth requires business definitions and assumptions, such as how much time a task used to take and what that time costs. Those assumptions should be visible and adjustable rather than buried, and ambiguous cases should be under-counted rather than over-claimed, which is how we approach [detecting AI-assisted code](/blog/how-olakai-detects-ai-usage/). ## What an AI System of Record Answers, and Who Owns It One record serves several leaders, each asking different questions of the same data. These four uses are where most organizations start. Use Primary reader Questions the record answers [AI ROI](/ai-roi/) Head of AI, CFO Which AI programs return more than they cost? Which should scale, and which should stop? [AI FinOps](/blog/inside-ai-spend-governance/) CFO, finance What will AI cost by month-end, and which team, project, or model is driving the change? [AI governance](/ai-governance/) CISO, compliance Where is sensitive data going into AI tools, including unapproved ones? Can we show an auditor what our AI did? [AI engineering productivity](/coding-iq/) VP of Engineering Are AI coding tools shortening cycle time, and which developers have licenses they are not using? *Four common uses of an AI system of record.* Ownership follows the general ledger model. Finance does not own every expense, but it owns the ledger, and every department accepts the ledger’s numbers. An AI system of record works best the same way: one accountable owner, usually the CIO or Head of AI, with finance, security, and engineering as primary consumers of a shared record rather than keepers of competing ones. In practice, the right owner is whoever answers to the board for the AI program as a whole. To check whether your organization already has one, try answering these five questions from what you have today, without starting a project: 1. What was our total AI spend last month across every vendor, and can we produce it within a day? 2. Which team, project, and model drove the largest change in that spend? 3. How many AI tools did employees use that IT never approved? 4. Which AI program produced a measurable business outcome, and what did each unit of that outcome cost? 5. If an auditor asked what a specific agent did last quarter, could we show them? If more than one of those answers requires a spreadsheet and a week of work, the organization has AI data, but not yet an AI system of record. ## Olakai: The System of Record for Enterprise AI Workday knows your people. Salesforce knows your customers. Olakai is the system of record for your AI. We built it around three steps: capture, structure, and act. **Capture.** Olakai captures every AI interaction and outcome across the organization, from coding agents to assistants to autonomous agents, down to the token and its cost. It collects from a browser extension for workforce chat assistants and shadow AI, from provider admin APIs for Anthropic, OpenAI, Cursor, GitHub Copilot, and Google Vertex AI, from coding agent hooks, from pull request analysis that needs nothing installed on developer machines, and from an SDK for the agents you build yourself. The full list is on our [integrations](/integrations/) page. **Structure.** Everything is structured into one data model, organized by user, team, department, project, agent, model, and vendor, and viewed through two lenses: [Olakai Agentic](/coding-iq/) for AI coding tools and autonomous agents, and [Olakai Assistive](/assistive-iq/) for the chatbots, copilots, and AI apps your employees use. **Act.** Budgets, month-end forecasts with a confidence range, and policy alerts turn the record into decisions before the invoice arrives, and [Kai](/kai/) lets anyone ask the record a question in plain language. Forecasts are projections rather than guarantees, and we label them that way. Olakai runs as SaaS or fully inside your own infrastructure, and you can see how the pieces fit together on the [platform overview](/platform/). ## Frequently Asked Questions ### What is an AI system of record? An AI system of record is the authoritative, vendor-neutral record of everything an organization’s AI does. It captures every AI interaction and outcome, including coding agents, assistants, and autonomous agents, down to the token and its cost, and structures it into one data model by user, team, project, agent, model, and vendor. ### How is an AI system of record different from an AI dashboard? A dashboard is a view of some data. An AI system of record is the underlying data itself, captured completely, attributed consistently, and kept over time, so any dashboard, report, forecast, or audit can be built from it and every team works from the same numbers. ### What is the difference between an AI system of record and AI observability? AI observability tools trace prompts, latency, and errors inside the applications an engineering team instruments. An AI system of record covers all AI across the organization, including workforce tools and shadow AI, and connects usage to cost and business outcomes for finance, security, and engineering leaders. ### Why can’t AI vendors provide their own system of record? Each vendor sees only its own product, and most enterprises use several AI vendors at once plus tools employees adopt on their own. A vendor console also has no reason to show where a competitor does the same work for less. A system of record has to be vendor-neutral to be complete and credible. ### Does an AI system of record store our prompts? It depends on the use. Detecting sensitive data requires seeing prompt content, while measuring adoption needs only metadata such as the app, user, and time. A well-designed record captures content only where needed, supports redaction before data leaves the application, and can run inside your own infrastructure. ## Key Takeaways - It is the first system of record that nobody types into, because AI activity is captured automatically across many vendors. - Its five properties are complete capture, token-level cost reconciled to the bill, one data model with attribution, outcomes linked to activity, and vendor neutrality with durable history. - It is not a dashboard, a vendor console, or an observability tool, and it is the data that AI analytics, AI FinOps, and AI governance run on. Every company will eventually keep a record of its AI, the same way it keeps one for its people, its customers, and its money. If you want to see what yours looks like, [schedule a demo](/schedule-a-demo/), and we will connect to the tools you already run and show you your own AI system of record. [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Your Ai Got Cheaper Your Bill Didnt Source: /blog/your-ai-got-cheaper-your-bill-didnt [← Back to Olakai's Blog](/blog/) # Your AI Got Cheaper. Your Bill Didn’t. ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot-150x150.webp) [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Co-Founder & CRO Bringing clarity to every AI investment conversation in the boardroom. July 21, 2026 · [AI Strategy](https://olakai.ai/blog/category/ai-strategy/) From the AI ROI Series, recorded 21 July 2026. [Apple](https://www.apple.com) raised prices on Macs and iPads and pointed straight at AI, saying the data centre buildout has driven memory chip costs up faster than they have ever seen and that they can no longer shield customers from it. Microsoft hiked the Xbox. Analysts expect smartphones to rise around 20% this year. Whatever else those are, they are not normal inflation. So the AI tax has stopped being an enterprise problem and turned up in the phone in your pocket. Which leaves the only question I actually care about here: if the cost of AI is leaking all the way into a MacBook, what is it doing to your token bill? ## The arithmetic nobody is connecting Your vendor will show you a chart of the per-token sticker price trending down, and they are all doing it, and they will call that savings. The sticker did fall, and that part is true. But the moment your enterprise plan flips to pay as you go, the same work runs five to ten times more, because you are now being metered on consumption rather than on seats. Both facts are true at once, which is why the chart and the invoice disagree so violently. The biggest labs are monetising hard on the back of it. [Anthropic](https://www.anthropic.com) just passed OpenAI in business spend, almost entirely because of Claude Code, since coding is the battlefield. And the uncomfortable part, which I do not think gets said plainly enough, is that they make more money when you burn more tokens. We have all become dependent, and we have not yet seen autonomous agents running at full scale. That is the 2027 bill, and it is still coming. Meanwhile almost nobody can answer the basic question. You have seen your invoices, so you roughly know what AI costs you, but is it working? Only about 15% of companies forecast their AI spend within 10% of reality, and most miss by more than a quarter, which means most teams are being driven by need rather than by science. That is exactly why boards are now turning to their executives and telling them to get a handle on it. ## Even Google is playing the same game Before anyone calls [Google](https://www.google.com) the cheap exception, look a little closer, because Google is running the smartest version of the same play. A free tier good enough to live on, Gemini bundled straight into your Workspace seats, and an API priced below cost, all to get you embedded before the meter starts to matter. The meter is still there, sitting behind the bundle for now. And when even Google has to cap Meta’s compute and tell them to use fewer tokens, the capacity ceiling and the pricing that follows it are quite real. Nobody is exempt from that, including the people selling you the exemption. ## Tokenmaxxing got us here. Tokenwising is how we make it pay. Tokenmaxxing was the 2025 story: burn everything, more is better. It was a reasonable place to end up, honestly, because it proved the value at a point when the value was still in question. 2026 is about tokenwising, which is spending like it is your own money. Here is what that looks like on a real floor, without naming names. ## The pilot A client of ours, an engineering org. Over the last 60 days we ran a pilot with about 30 developers, roughly a third of the department. The goal was never to brag about lines of code, since shipping thousands of lines of AI-written code is a vanity metric, and I have [said so about token leaderboards](/blog/tokenmaxxing-claudeonomics/) often enough. The goal was quality: code that passes testing, ships, and moves the business. Today about half their code is AI-written, and they want to push past 80% in the next six months. That is aggressive, it is expensive, and it is exactly the sort of thing you should not do blind. So we measured three things: who is getting real lift and who is simply burning tokens, which model each task actually needs rather than defaulting to the most expensive one available, and where the waste hides, which is in the re-prompts, the abandoned runs, and the agents quietly looping. None of that was about slowing them down. It was about making the 80% push a science project rather than a guess. One concrete piece of it is [model routing](/blog/model-routing-explained/). Match the model to the task and the subtask, and stop burning your most capable and most expensive model on mundane coding work where it produces no better result. Done well, that honestly takes 40% to 50% off the bill on those tasks, which is the difference between an 80% target that pays for itself and one that quietly bleeds. Route on the wrong unit, though, and you will cut the bill while destroying the value underneath it, which is a trap I walked into publicly and [corrected later](/blog/ai-bill-eating-everything-else/). ## The part I am most excited about, which is real guardrails Here is what is actually new. Your vendors will let you set one big account-level spend limit, and at enterprise scale that is close to useless, because it does nothing to stop a single agent going rogue and eating half the quarter’s budget over a weekend. Budgets, forecasting, and visibility with alerts baked into the workflow are largely solved at this point. The hard part is enforceable limits, real ones, at the project, team, department, and even the individual-developer level. Not a warning after the money is gone, but an actual ceiling, which is the thing that lets a chief data officer sleep. It is early, and I suspect a lot of you are quietly wrestling with the same problem, so I will go deeper on it in coming episodes. Some of the mechanics are already written up in [how the budgets and alerts work](/blog/inside-ai-spend-governance/). ## The move Tokenmaxxing got us here, and tokenwising is how the AI transformation actually pays. The check I would run this month is narrow enough to finish in an afternoon. Do you know your cost per unit of output, rather than your cost per seat or your total invoice? Do you know which of your developers are getting genuine lift, measured against something, and which are producing volume? And if one agent ran unattended over a weekend, is there anything in your stack that would stop it, or only something that would tell you about it on Monday? Most organisations can answer none of the three, which is the whole reason a measured view of [AI ROI](/ai-roi/) and a real record of [what your coding tools are doing](/coding-iq/) matter more this year than they did last year. The falling rate card is going to keep making the case that things are getting cheaper, and your invoice is going to keep disagreeing, and only one of those two has your name on it. One question to take into your next standup: how much of your code is AI-written right now, and do you know whether it is making you money or simply making more code? I’m Paul, co-founder of Olakai. Measuring what AI actually costs and what it actually returns, on your own workload, is the work I spend my days on. [Schedule your AI evaluation](/schedule-a-demo/), and we will connect to what you already run and show you your own record, in your own environment. [Even Google Can’t Ship Its Best AI](https://olakai.ai/blog/google-cant-ship-best-ai/) [Custom KPIs: The Four-Layer System Behind Olakai’s Metrics](https://olakai.ai/blog/custom-kpis-ai-measurement/) ## More posts - ### [What Is an AI System of Record?](https://olakai.ai/blog/what-is-an-ai-system-of-record/) [September 10, 2026](https://olakai.ai/blog/what-is-an-ai-system-of-record/) - ### [The Cost to Serve a Token](https://olakai.ai/blog/cost-to-serve-a-token/) [September 9, 2026](https://olakai.ai/blog/cost-to-serve-a-token/) - ### [ClaudeForce, and the One AI Input You Cannot Buy](https://olakai.ai/blog/claudeforce-ai-system-of-record/) [August 31, 2026](https://olakai.ai/blog/claudeforce-ai-system-of-record/) - ### [Their Revenue Forecast Is Your AI Budget](https://olakai.ai/blog/revenue-forecast-ai-budget/) [August 25, 2026](https://olakai.ai/blog/revenue-forecast-ai-budget/) --- ## Coding Iq Source: /coding-iq [The Platform](/platform/) \| The Products » Olakai Agentic · [Olakai Assistive](/assistive-iq/) # See, forecast, and control your AI coding tools. Maximize the ROI. AI coding tools are now usage-based. Spend is volatile, and most leaders cannot see what it costs or what it returns. Coding IQ gives you both, and turns it into measurable ROI. We provide this. With clear evidence. [Find out your AI ROI](https://olakai.ai/paul/) [Start Using Olakai](https://app.olakai.ai/signup) ![Coding IQ spend simulation - cumulative AI spend and a 90-day run-rate projection with a budget-overrun forecast](/wp-content/uploads/2026/06/coding-iq-simulate-hero.webp) Out of the box integrations ![Coding IQ budgets and forecasts - program ceiling, run-rate projection to month-end, and a budget-breach forecast](/wp-content/uploads/2026/06/coding-iq-budget-sim.webp) ### Budgets & Forecasts ## Forecast month-end spend. Set budgets. Get alerted before you overrun. Set monthly limits across six overlapping lenses – Program, Provider, Developer, Persona, Department, and Project – so the same dollar rolls up to a developer, their team, and the whole account. A daily job projects month-end spend from your run-rate and fires an alert the moment a budget is breached or forecast to breach, before the invoice arrives. - Run-rate month-end forecast with a confidence level — a projection, not a guarantee - Threshold alerts (e.g. 80% / 100% of limit) and forecast-breach alerts - Simulate the next 30 days — model growth or shift spend between providers ### PR Analysis ## See exactly how much faster AI-assisted PRs ship Coding IQ ingests PR data directly from your GitHub organization, no agent or SDK required. Every PR is automatically classified as AI-assisted or non-AI by reading commit co-author trailers, bot authorship, and PR body markers. The delta against your historical cycle time is unambiguous. - Coding time, review time, and total cycle time — AI vs non-AI - PR volume and size distribution over time - AI code ratio: % of merged lines that came from AI-assisted PRs ![Coding IQ PR analysis — PR mix of fully agentic, human + AI assisted, and non-AI pull requests, with volume and size trends](/wp-content/uploads/2026/06/coding-iq-productivity-platform.webp) ![Coding IQ Developers tab showing per-developer adoption cohorts, estimated and actual cost, and agent hook coverage](/wp-content/uploads/2026/06/coding-iq-devs-v2.webp) ### Developers ## Find the idle licenses. Coach the casual users. Reward the power users. Coding IQ segments every developer in your org into one of four adoption cohorts and shows you which tools they actually use, how often, and what their cycle time looks like compared to peers. It also attributes spend to each developer – estimated and reconciled actual cost side by side – so who’s using what, and what it’s costing, is one table, not a month-end reconciliation project. The result: a precise list of who to enable, who to train, and where to reclaim spend. ### Ask Kai ## Ask Kai where to cut, and how much you’ll save. Ask Kai in plain English, and it reasons across spend by provider, model, developer, and project, then surfaces the specific moves: idle licenses to reclaim, expensive models to swap for cheaper ones, budgets to set before the next overrun. Every answer shows its reasoning and the dollar impact. - “Which models are driving our spend, and where can we drop to a cheaper one?” - “Which seats and licenses are going unused this month?” - “Set a budget for the platform team and alert me before we hit 80%.” ![Kai analyzing AI coding costs and recommending where to cut spend, with the reasoning and dollar impact](/wp-content/uploads/2026/06/kai-analyze-costs-platform.webp) ## More than measurement ### AI ROI in dollars Coding IQ translates productivity gains into business impact with AI Equivalent Engineers — a dollar-value read on how much engineering capacity your AI tooling is adding, using a per-developer before-and-after method over fixed calendar windows. It’s the number that turns “AI made us faster” into a figure you can take to the board. ### Governance, scoped to code Coding IQ watches coding-agent traffic for PII, PHI, and secrets, with its own risk surface kept separate from Agent IQ so the two never double-count. Velocity and spend visibility never come at the expense of knowing what’s leaking into prompts. ## Coding IQ doesn’t live in a silo. It runs on the same platform as [Agent IQ](https://olakai.ai/agent-iq/) and [Assistive IQ](https://olakai.ai/assistive-iq/), so the engineering velocity and spend story rolls up into the same enterprise AI ROI dashboard your CFO and CAIO are already looking at. And because everything flows through [Kai](https://olakai.ai/kai/), you can ask your AI program a question in plain English and get a reasoned answer in seconds: *“Are we forecast to overrun any coding budget this month, and which team is driving it?”* ## See your spend. Kill the waste. Maximize your AI ROI. We provide this. With clear evidence. See your number, not a sandbox. A short working session on your real spend, tailored to your stack. No deck, no pitch. [Find out your AI ROI](https://olakai.ai/paul/) --- ## Company Source: /company Company # We Believe Measurement Protects People Olakai is the enterprise AI analytics and governance platform — built to measure ROI, control AI costs, and govern risk across every tool, every agent, and every team. [Try it live](#magic-link) Our Manifesto ## The Measurement Gap A Fortune 500 CIO told me her board asked a simple question: “What is our return on AI?” She had spent $4.2 million on AI tools in the past year. She had adoption dashboards from four different vendors. She had employee surveys showing high satisfaction. What she did not have was an answer. She is not unusual. According to a PwC global survey of more than 4,000 CEOs, 56% report zero financial benefit from their AI investments. Not disappointing returns. Zero. Meanwhile, enterprise AI spending is now measured in the hundreds of billions annually and is accelerating. Something does not add up. Either AI does not work, or we are not measuring it correctly. We believe it is the second one. ### Flying Blind at Scale Every AI vendor gives you a dashboard. OpenAI shows you token usage. Microsoft shows you Copilot adoption rates. Salesforce shows you Einstein interactions. Each dashboard answers a narrow question about its own tool. None of them answer the question that actually matters: is AI making this company more valuable? This is not a technology failure. It is a measurement failure. And it is structural. No AI vendor has an incentive to give you a cross-platform view of your AI spending and its outcomes. Their dashboards are designed to show you that their tool is being used, not whether your overall AI strategy is working. The result is that enterprises have more AI analytics than ever and less clarity than ever. We have spent decades building enterprise software. We have seen this pattern before. In the early days of digital marketing, every platform had its own metrics. Click-through rates here. Impressions there. Engagement scores somewhere else. None of it connected to revenue. It took a generation of analytics companies to close that gap and give marketers a unified view of what was actually driving results. AI is in that same moment. The tools are proliferating faster than the ability to measure them. And the stakes are significantly higher than marketing budgets. ## Why We Started Olakai We started Olakai because we saw this measurement gap widening and nobody building the infrastructure to close it. The problem is not that measurement is impossible. It is that it requires a layer that sits across every AI tool an enterprise uses, regardless of vendor. A layer that connects usage data, cost data, and business outcomes into a single view. No AI vendor will ever build this. It would require them to show you how their tool compares to their competitor’s tool. The incentive does not exist. So we built it. Olakai is a vendor-neutral analytics and governance platform that works across your entire AI stack. Copilot, ChatGPT, Gemini, Claude, custom agents, AI features buried inside your SaaS applications. All of it. One view. What does that look like in practice? It means a CFO can see that Copilot is delivering $14 per interaction in finance and $2 in marketing. It means a CISO can see which teams are using unsanctioned AI tools and what data they are exposing. It means a CIO can walk into a board meeting and answer the question that CIO could not answer: here is our return on AI, by tool, by team, by business outcome. And it means a CTO can see which AI coding tools are generating velocity gains versus burning budget — and forecast next month’s token bill before the invoice arrives. These are not exotic questions. They are the same questions enterprises ask about every other category of technology spending. The fact that AI has operated without this level of accountability for years is the anomaly. Three forces are converging to end it: enterprise AI budgets have moved from experimental line items to real P&L commitments, the EU AI Act is now in enforcement, and the workforce conversation has changed permanently. ### What Happens Without Measurement Every board in every major enterprise is now asking the same question: can a smaller team with AI outperform a larger team without it? Can we restructure around this technology with confidence? These are not unreasonable questions. The evidence that AI delivers real productivity gains, in the right context with the right implementation, is real. But the companies asking these questions are largely doing so without the measurement infrastructure to answer them. They restructure around tools they have not measured. They keep tools that do not perform because the data to prove underperformance does not exist. They cut in the wrong places because they cannot see where AI is adding value versus where it is just being used. And when those decisions turn out to be wrong, they cannot explain why — because they never had the data to explain why they made them in the first place. The cost of guessing is not just wasted AI investment. It is the organizational decisions that get made on top of it. Measurement is not a nice-to-have anymore. It is the prerequisite for every other AI decision your company will make. ### What We Believe AI is going to reshape how companies operate. That is not hype. It is already happening. The question is not whether it will happen but whether companies will navigate the transition with real data or with guesswork. We believe measurement protects people. When you can prove which AI initiatives are delivering value, you can invest in them with confidence. When you can prove which roles are being augmented rather than replaced, you can make workforce decisions that are honest and defensible. When you cannot prove any of that, every decision is political. That is the mission. Give enterprises the clarity to make AI decisions based on evidence. Not vendor promises. Not board pressure. Not a competitor’s results they cannot replicate. Evidence. We also believe governance cannot be separate from measurement. As AI moves deeper into operational workflows — autonomous agents making decisions, coding tools generating production code, AI tools touching regulated data — visibility alone is not enough. You need controls, audit trails, and the ability to enforce policy before a bad action happens. Olakai is built for both: the measurement layer and the governance layer, in one platform, across every AI tool and agent your organization runs. ![Xavier Casanova](/wp-content/uploads/2026/02/xavier-headshot.webp) ### Xavier Casanova Founder & CEO [LinkedIn](https://www.linkedin.com/in/xaviercasanova) ![Walt Mann](/wp-content/uploads/2026/02/walt-headshot.webp) ### Walt Mann Co-Founder & CTO ![Paul Brzozowski](/wp-content/uploads/2026/02/paul-headshot.webp) ### Paul Brzozowski Co-Founder & CRO [LinkedIn](https://www.linkedin.com/in/paulbrzozowski) ![Dean Sysman](/wp-content/uploads/2026/02/dean-headshot.webp) ### Dean Sysman Board Member [LinkedIn](https://www.linkedin.com/in/deansysman) ## What we built Two products, one platform. Same data model, same set of controls. ### Olakai Agentic Measure and govern every AI coding tool and autonomous agent your teams run — Claude Code, Cursor, Copilot, Codex, and more. Token spend by provider, team, and developer. Run-rate forecasting before the invoice arrives. Full execution logs for every agent decision, and policy enforcement before bad actions happen. [Explore Olakai Agentic →](https://olakai.ai/coding-iq/) ### Olakai Assistive Track every AI tool your employees use — approved and shadow. License utilization, time saved, data exposure, and policy compliance across 630+ tools. The visibility your security and finance teams need before anyone asks for it. [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ### Our Values **Evidence over opinion** Decisions should be based on data, not vendor promises or boardroom pressure. We build the infrastructure that turns AI activity into proof. * * * **Transparency by default** If you cannot see what your AI is doing, you cannot govern it. Visibility is not a feature request. It is the starting point. * * * **Measurement protects people** When you can prove what is working, workforce decisions are honest and defensible. When you cannot, they are political. We exist to close that gap. ### Our Mission ### Give enterprises the clarity to make AI decisions based on evidence — not vendor promises, not board pressure. That is the mission. Agentic AI Use Cases ## Future of Agentic AI Enterprise leaders evaluating agentic AI face a common challenge: separating proven use cases from hype. The Future of Agentic directory catalogs 200+ real-world agentic AI deployments across 15 industries, each with ROI data, implementation complexity ratings, and vendor-neutral analysis. Whether you are exploring autonomous agents for customer service, supply chain optimization, or software development, this research gives you the evidence base to build a business case and prioritize investments. Every use case is curated by the Olakai research team with input from enterprise practitioners. [Explore Agentic AI Use Cases →](https://futureofagentic.com) Shadow AI Intelligence ## Shadow AI Map Most enterprises don’t know which AI tools their employees are actually using. Shadow AI Map catalogs 700+ enterprise AI applications — browsable by category, with comparisons, alternatives, and enterprise readiness data — so you can understand what’s in use across your organization before your security team asks. Explore tools by category, compare options side by side, and evaluate against criteria that matter to enterprise buyers: security posture, compliance coverage, integration depth, and data handling. The intelligence layer that connects to Olakai’s [Assistive IQ](https://olakai.ai/assistive-iq/) shadow AI detection. [Explore the Shadow AI Map →](https://shadowaimap.com) ## Explore Olakai on your own terms. Drop your work email below and we’ll send you a private link to a live Olakai environment — pre-loaded with realistic enterprise data so you can explore AI spend by team, shadow AI tools detected, agent audit trails, and Kai ready to answer your governance questions at your own pace. No account to create. No demo call to book. No commitment. If you want a guided walkthrough after, we’re one click away. ### Get your Magic Link Enter your work email. We’ll send the link in seconds. First name Last name Work email Company Your role By submitting, you agree to receive a one-time email with your Magic Link. [Privacy](/privacy-policy/). --- ## Complete Ai Monitoring Source: /complete-ai-monitoring [Key Features](/platform/) \| [Measure ROI](/ai-roi/) · [Govern Risk](/ai-governance/) · [Agent IQ](/agent-iq/) · [Custom KPIs](/analytics-kpis/) · Monitor AI · [Shadow AI](/shadow-ai/) · [Integrations](/integrations/) · [Kai](/kai/) # Complete Visibility Across Every AI Tool Unified Visibility. Autonomous Agents. AI-Powered Tools. ## The Enterprise AI Visibility Problem Enterprises run two fundamentally different kinds of AI: autonomous agents that act independently and assistive tools that augment human work. **Most organizations have no unified view across both.** Agent performance lives in one dashboard. Copilot adoption lives in another. Cost data is scattered across vendor invoices. And nobody can answer the simplest question: “Is our AI actually working?” Without a unified analytics layer, you’re flying blind—making investment decisions based on vendor hype instead of operational reality. **Agent blind spots** Autonomous agents run 24/7 but their success rates, costs, and failure modes are invisible to business leaders. * * * **Copilot mystery** You’re paying for thousands of Copilot licenses but can’t measure whether they’re actually improving productivity. **Vendor lock-in dashboards** Each AI vendor shows only their own data—you can’t compare performance across tools or models. * * * **No unified metrics** Engineering tracks tokens. Finance tracks spend. Nobody tracks business outcomes across all AI investments. ### The Challenge ### Fragmented dashboards give you data. You need a unified view that gives you answers. ![Olakai Assistive IQ adoption dashboard showing cumulative AI adoption and retention by persona and department](/wp-content/uploads/2026/06/adoption-dashboard.webp) The Solution ## One Platform for All Your AI Monitor autonomous agents and assistive AI tools in a single view. Track performance, cost, and business impact across every vendor and deployment—from one control plane. ![Olakai Agent IQ dashboard showing agentic AI ROI, value created, executions, and governance compliance with per-agent value breakdown](/wp-content/uploads/2026/06/agentic-dash.webp) **Agentic Analytics** Track every autonomous agent’s success rate, cost per execution, and business impact. See which agents deliver ROI and which need optimization—with Agent IQ™ benchmarks. * * * **Assistive Analytics** Measure copilot and AI tool adoption, session quality, and productivity gains across teams. Understand whether your Copilot and ChatGPT investments are actually moving the needle. * * * **Cross-Vendor Comparison** Compare AI performance across OpenAI, Anthropic, Google, Microsoft, and custom models in a single view. Make vendor decisions based on data, not demos. * * * **Executive Dashboards** Board-ready views that translate AI activity into business language—hours saved, costs avoided, risks mitigated. Give leadership the confidence to scale AI investment. ### Ready for 360° AI visibility? Stop stitching. Start measuring. [Talk to an Expert](/schedule-a-demo/) --- ## Industries Source: /industries Industries # Olakai across industries One platform. Every AI tool. Every industry’s metrics, regulations, and workflows. AI doesn’t look the same in healthcare as it does in financial services. The tools are different. The regulations are different. The KPIs are different. Olakai is the measurement and governance layer that works across all of them — with the industry-specific context that makes the data actionable. - [Technology & Software](/industries/technology-software/) — Cycle time, ARR retention, pipeline velocity — measured across Claude Code, Cursor, Copilot, and every AI tool your teams use. - [Financial Services](/industries/financial-services/) — KYC, fraud detection, audit trails, model risk — governed and measured with the documentation regulators expect. - [Healthcare & Life Sciences](/industries/healthcare-life-sciences/) — Clinician time saved, PHI exposure risk, prior auth throughput — with HIPAA-grade evidence of where AI is running. - [Professional Services](/industries/professional-services/) — Billable hours leverage, contract review speed, practice group utilization — AI measured per practitioner, per engagement. - [Retail & E-Commerce](/industries/retail-ecommerce/) — Conversion lift, demand forecast accuracy, customer service deflection — every AI tool connected to GMV and margin. - [Manufacturing](/industries/manufacturing/) — Downtime avoided, defect rate reduction, supply chain savings — AI ROI measured line by line, plant by plant. ## Three lenses, one platform Every industry page above is built on the same platform — Olakai. **[Assistive IQ](/assistive-iq/)** tracks copilots and shadow AI. **[Coding IQ](/coding-iq/)** measures AI coding tool ROI. **[Agent IQ](/agent-iq/)** governs every autonomous agent. And **[Kai](/kai/)** lets anyone in the business ask a question and get a reasoned answer. The data model is the same. The governance controls are the same. What changes is the lens — and the industry-specific KPIs, workflows, and regulations that Olakai applies to your data. ## Explore Olakai on your own terms. Drop your work email below and we’ll send you a private link to a live Olakai environment — pre-loaded with realistic data so you can click around at your own pace, run a few Kai queries, and see exactly what your AI program would look like inside the platform. No account to create. No demo call to book. No commitment. If you want a guided walkthrough after, we’re one click away. ### Get your Magic Link Enter your work email. We’ll send the link in seconds. First name Last name Work email Company Your role By submitting, you agree to receive a one-time email with your Magic Link. [Privacy](/privacy-policy/). --- ## Financial Services Source: /industries/financial-services Financial Services # Control AI costs. Govern AI risk. Run it all inside your own cloud. Engineering teams at banks and insurers are spending millions on AI coding tools with no ROI visibility. Every AI-assisted decision needs an audit trail. And your data can’t leave your perimeter. ## AI spend is growing. Visibility isn’t. Hundreds of developers at your bank or insurer are using Cursor, GitHub Copilot, and Claude to modernize core systems. Token bills are growing fast — and in most cases, nobody can tell you which teams are getting ROI and which are burning budget on tools that haven’t changed their output. The engineering investment is real. The visibility isn’t. Meanwhile, your CISO and model risk team are asking a different set of questions: who approved these tools, what data are they touching, and where’s the audit trail? And your analysts and bankers are using AI tools that never went through procurement — on client portfolios, on regulated data, on decisions that examiners are starting to ask about. Every other platform that could help you answer these questions processes your data through their cloud. For a financial institution, that’s a non-starter. ### By the Numbers ### Engineering teams at major financial institutions are spending 3–5x more on AI coding tools than their original budgets — with no platform to show what it returned. *The firms that govern AI spend and output from the same platform are the ones that keep deploying. The rest stall at the board meeting.* ## Your data never leaves your infrastructure. Olakai deploys inside your cloud tenancy or on your own hardware — AWS, Azure, GCP, or on-premises. No code, no prompt data, no AI interaction logs are processed outside your perimeter. For financial institutions operating under SR 11-7, data residency mandates, and internal security policy, this isn’t a feature — it’s the prerequisite. Most AI governance vendors are SaaS-only. That disqualifies them before the conversation starts. - Deploy in your own AWS, Azure, or GCP tenancy — or fully on-premises - Zero data egress: code, prompts, and agent logs never leave your environment - Meets data residency, sovereignty, and internal security policy requirements out of the box - SR 11-7 and EU AI Act compliance documentation generated inside your perimeter ## Two lenses, one platform Olakai is one platform, one data model, one set of controls. **Olakai Agentic** and **Olakai Assistive** are the two lenses you use to look at it — they share data, share context, and share [Kai](https://olakai.ai/kai/). What you see in one lens is immediately visible in the other. ### Olakai Agentic Measure AI coding tool ROI and govern every autonomous agent decision, in one lens. Token spend by provider, team, and developer. Run-rate forecasting before the invoice arrives. Full audit trails for KYC automation, fraud triage, and credit workflows — generated automatically, not assembled by hand the week before. All inside your own infrastructure. [Explore Olakai Agentic →](https://olakai.ai/coding-iq/) ### Olakai Assistive Track every AI tool your analysts, bankers, and relationship managers are using — approved and shadow. License utilization, time saved per workflow, client data exposure detection. The visibility your model risk team needs before the examiner asks. [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ![Olakai Agentic budgets and forecasts showing AI spend by day, run-rate forecast, and month-end projection](/wp-content/uploads/2026/06/coding-iq-budgets-platform.webp) ### Olakai Agentic — Spend control ## See the overrun before it happens. Olakai tracks token spend across every AI coding tool — Cursor, GitHub Copilot, Anthropic, OpenAI — normalized by provider, team, and developer. Run-rate forecasting projects month-end costs from a 7-day trailing average, so a 3x overrun shows up as a trajectory problem weeks before it becomes an invoice. All of it running inside your own infrastructure. - Token spend by provider, team, and developer in one normalized view - Run-rate forecast with trajectory alerts at 50%, 80%, and 100% of budget - 30-day spend simulation to model the impact of limit changes before you make them ### Olakai Agentic — Audit trail ## The audit trail your examiner will ask for. Ready before they ask. For every autonomous agent running inside your institution — KYC workflows, fraud triage, credit decisioning — Olakai logs every input received, every tool called, every decision made, and every outcome. When your model risk team or regulator needs documentation, it exists and it’s current. Not reconstructed from logs the week before the review. - Full execution log: inputs, tools called, decisions, outcomes — per agent run - Policy enforcement at the workflow level — violations caught before the action is taken - SR 11-7 and EU AI Act compliance reports generated automatically, inside your perimeter ![Olakai Agentic detailed view showing full execution audit trail with inputs, decisions, and outcomes per agent run](/wp-content/uploads/2026/06/agent-iq-detailed-platform.webp) ## The KPIs that matter in financial services Every pillar gives you a different lens on AI performance. Here are the metrics Olakai actually measures — across every vendor, every team, inside your own infrastructure. ### Olakai Agentic **Token spend by provider, team, and developer** Normalized across Cursor, Copilot, Anthropic, and OpenAI in one view **Run-rate month-end forecast** Trajectory alerts at 50%, 80%, and 100% of any configured budget — before the overrun, not after **Agent executions with complete audit trail** Every input received, tool called, decision made, and outcome — per execution **Examiner-ready report generation** Automated — not assembled from logs by hand the week before the audit [Explore Olakai Agentic →](https://olakai.ai/coding-iq/) ### Olakai Assistive **Shadow AI tools touching client or regulated data** By tool, team, and risk classification — a prioritized list, not just a count **License utilization** Active vs. idle seats across every approved copilot, by team and by individual **Time saved per analyst and banker** Hours recovered per week from AI-assisted workflows, by role and department **Prompt policy compliance rate** Percentage of AI interactions that meet your data handling and usage policies [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ## Why Measurement Changes Everything ### From AI Spend Surprise to Cost Control Teams burn through annual AI coding budgets in four months when they’re watching monthly actuals instead of run-rate trajectory. Olakai catches the overrun before it happens — spend by team, by provider, by developer, in one view, inside your own infrastructure. * * * ### From Manual Audit Prep to Automatic Compliance When the examiner asks for an AI audit trail, your team shouldn’t spend two weeks assembling it from logs, emails, and screenshots. Olakai logs every agent decision in real time. The report exists before anyone asks for it. ### From Shadow AI Exposure to Governed Adoption Analysts and bankers are using AI tools you’ve never heard of on client data you can’t trace. Olakai makes every tool visible, quantifies the risk, and lets you bring high-value shadow AI inside the perimeter with proper oversight — instead of blocking everything and losing the productivity gains. ## Ask your AI program a question. Get a reasoned answer. [Kai](https://olakai.ai/kai/) synthesizes every Olakai data source — spend, adoption, agent logs, governance status — and answers in plain English. With the reasoning shown and the data behind every number. - “Which AI coding tools are our engineering teams spending on, and what is each one returning in velocity gains?” - “Which AI tools are touching client data right now, and which of those are unsanctioned?” - “Show me the full audit trail for every agent decision in our KYC workflow over the last 90 days.” ## Talk to an Expert. See how Olakai gives you the audit trail, spend control, and governance your bank or insurer needs — deployed inside your own infrastructure, no pitch. [Talk to an Expert](https://olakai.ai/schedule-a-demo/) --- ## Healthcare Life Sciences Source: /industries/healthcare-life-sciences Healthcare & Life Sciences # Clinical AI that stays inside your walls. Healthcare organizations can’t send patient data through a third-party cloud to govern their AI. Olakai runs inside your own infrastructure — HIPAA-compliant audit trails, clinical agent governance, and shadow AI visibility, without PHI ever leaving your perimeter. ## Your clinical teams are using AI tools your compliance team hasn’t approved. Nurses are using AI scribe tools. Physicians are using ChatGPT for clinical summaries. Your EHR vendor just launched a Copilot. Prior authorization agents are making decisions that affect patient care. Each of these is a governance gap — and the PHI exposure isn’t theoretical. Most health systems we talk to can name three approved AI tools and know about a dozen more their staff is using. Your compliance team needs to see all of it — not just the approved list — with the documentation to prove governance to auditors and accreditors. And none of that documentation can be built on a platform that sends patient data outside your walls. ### By the Numbers ### Healthcare organizations report that more than half of clinician AI tool usage involves applications that were never reviewed by compliance — with direct PHI exposure in many cases. *Visibility isn’t optional when patient data is involved. Neither is keeping it in your environment.* ## HIPAA-compliant by design. PHI never leaves your infrastructure. Olakai deploys inside your cloud environment or on your own hardware — AWS GovCloud, Azure Government, your on-premises data center. No clinical notes, no patient identifiers, no AI interaction logs are processed outside your perimeter. For healthcare organizations subject to HIPAA, HITRUST, and internal data governance policies, this isn’t a product feature — it’s the prerequisite that makes the conversation possible. - Deploy in AWS GovCloud, Azure Government, or fully on-premises — your choice of infrastructure - Zero PHI egress: patient data, clinical notes, and agent logs never leave your environment - HIPAA-compliant architecture with audit documentation generated inside your perimeter - HITRUST and internal data governance policy requirements met out of the box ## Two lenses, one platform Olakai is one platform, one data model, one set of controls. **Olakai Agentic** and **Olakai Assistive** are the two lenses you use to look at it — they share data, share context, and share [Kai](https://olakai.ai/kai/). What you see in one lens is immediately visible in the other. ### Olakai Agentic Govern every clinical and administrative agent — prior authorization automation, clinical decision support, revenue cycle agents — with full execution audit trails and policy enforcement before a bad action affects patient care. Also measures AI coding tool ROI for the health IT engineering teams building your EHR integrations and clinical apps: token spend by provider, team, and developer, with compliance reports generated automatically inside your perimeter. [Explore Olakai Agentic →](https://olakai.ai/agent-iq/) ### Olakai Assistive Track every AI tool your clinical and administrative staff are using — approved and unapproved. Identify shadow AI tools touching PHI, measure time saved per clinician per workflow, and give your compliance team the visibility they need before an audit. [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ![Olakai Agentic detailed execution view showing complete audit trail for clinical agent decisions with inputs, actions, and outcomes](/wp-content/uploads/2026/06/agent-iq-detailed-platform.webp) ### Olakai Agentic — Clinical agent audit trail ## Every clinical agent decision. Documented before anyone asks. For every autonomous agent running in your clinical or administrative environment — prior auth, clinical documentation, revenue cycle — Olakai logs every input, every tool called, every decision made, and every outcome. When your compliance team or accreditor needs documentation, it exists. Not assembled after the fact from fragmented logs, but generated continuously, inside your infrastructure. - Full execution log: clinical inputs, tools called, decisions made, outcomes — per agent run - Policy enforcement at the workflow level — violations blocked before affecting patient care or billing - HIPAA-compliant audit reports generated automatically, never leaving your environment ### Olakai Assistive — Shadow AI detection ## See every AI tool your staff is using. Including the ones compliance doesn’t know about. Olakai Assistive surfaces every AI tool in use across your organization — from approved EHR copilots to the ChatGPT tab a physician has open during patient documentation. Ranked by risk surface: PHI exposure, department, data sensitivity. The visibility your compliance and privacy teams need to close the gap between what’s approved and what’s in use. - Every AI tool in use surfaced — approved, shadow, and consumer apps touching clinical workflows - Risk classification by PHI exposure, department, and data type - Governance controls to bring high-value shadow AI tools into sanctioned use with proper oversight ![Olakai Assistive shadow AI detection view showing unauthorized AI tools in use across departments with risk classification](/wp-content/uploads/2026/06/assistive-iq-shadow-ai-platform.webp) ## The KPIs that matter in healthcare & life sciences Every pillar gives you a different lens on AI performance. Here are the metrics Olakai actually measures — across every vendor, every team, inside your own infrastructure. ### Olakai Agentic **Clinical agent executions with complete audit trail per run** Every input, tool called, decision, and outcome — per execution, inside your infrastructure **Policy violations caught before impact on patient care or billing** Enforcement at the workflow level, before the bad action is taken **Health IT team AI coding tool spend by provider and team** Normalized across every tool your engineers use — inside your infrastructure **Run-rate forecast with alerts before budget overruns** Trajectory alerts at 50%, 80%, and 100% of any configured budget [Explore Olakai Agentic →](https://olakai.ai/agent-iq/) ### Olakai Assistive **Shadow AI tools touching PHI — by tool, department, and risk classification** A prioritized list, not just a count — ranked by exposure surface **Approved vs. unsanctioned clinical tools by department and role** The complete picture your compliance and privacy teams need before an audit **License utilization: active vs. idle seats across every approved copilot** Reclaim idle seats before the next renewal cycle **Time saved per clinician per week from AI-assisted documentation and workflows** Hours recovered per role — the ROI your CMO can take to the board [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ## Why Measurement Changes Everything ### From PHI Exposure Risk to Governed Clinical AI Your staff is using AI tools on patient data — some approved, many not. Olakai makes every tool visible, classifies the PHI risk, and gives you the controls to govern high-value tools rather than blocking them and losing the clinical efficiency gains. * * * ### From Manual HIPAA Audit Prep to Automatic Documentation Pulling together AI audit documentation for a HIPAA review or accreditation shouldn’t take weeks of manual log assembly. Olakai generates it continuously, automatically, inside your perimeter. It exists before anyone asks. ### From Clinical AI Anxiety to Confident Deployment “We don’t know what AI tools our clinicians are using” is not a sustainable position. Olakai gives your CMO, CISO, and compliance team a complete picture — every tool, every interaction, every risk — so you can deploy AI with confidence instead of holding it back. ## Ask your AI program a question. Get a reasoned answer. [Kai](https://olakai.ai/kai/) synthesizes every Olakai data source — agent logs, tool usage, governance status, compliance coverage — and answers in plain English. With the reasoning shown and the evidence attached. - “Which AI tools are our clinical staff using that haven’t been reviewed by compliance — and which are touching PHI?” - “Show me the complete audit trail for prior authorization agent decisions over the last 30 days.” - “Which departments have the highest PHI exposure from unsanctioned AI tool usage right now?” ## Talk to an Expert. See how Olakai keeps clinical AI governed and HIPAA-compliant, without PHI ever leaving your walls — tailored to your environment, no pitch. [Talk to an Expert](https://olakai.ai/schedule-a-demo/) --- ## Manufacturing Source: /industries/manufacturing Manufacturing # From the plant floor to the boardroom — AI governed, measured, and kept on-site. Manufacturing organizations are deploying AI agents in operational environments alongside corporate AI programs across engineering, procurement, and supply chain. Olakai gives you complete visibility into every AI tool, every agent decision, and every dollar spent — deployed as SaaS for corporate teams or on-premises where your OT environment requires it. ## Operational AI is making decisions on the plant floor. Who’s governing it? Predictive maintenance agents are flagging equipment issues. Quality control AI is rejecting parts on the line. Supply chain agents are adjusting orders based on sensor data. Each of these is an autonomous AI decision with real operational and financial consequences — and most manufacturers we talk to have no audit trail for any of them. Meanwhile, your knowledge workers in engineering, procurement, and supply chain are using AI tools that weren’t part of any deployment plan. Your IT team is trying to map what’s running where. And your operations leadership is asking what AI is actually returning per dollar spent — not at the tooling level, but across the entire program. That’s three different problems that require the same underlying visibility. ### By the Numbers ### Manufacturing organizations deploying AI agents in operational environments report that fewer than 30% of agent decisions are logged with sufficient detail to investigate an anomaly or production failure. *When an AI agent makes a decision that affects the line, you need the audit trail. Most manufacturers don’t have it.* ## Deploy where your environment requires — SaaS, private cloud, or fully on-premises. Many manufacturing organizations run Olakai as SaaS for their corporate AI programs — knowledge workers, engineering teams, and supply chain functions that operate on standard IT infrastructure. For environments where operational data can’t leave the facility — plant-floor AI agents, OT-connected systems, or production workflows under IEC 62443 or NIST CSF — Olakai also deploys fully on-premises or inside your own private cloud. One platform, deployed where each part of your operation actually runs. - SaaS deployment for corporate IT and knowledge-worker AI programs — up in hours - On-premises or private cloud for OT environments where operational data cannot leave the facility - Works within OT/IT network constraints — no external dependencies for core governance functions - IEC 62443, NIST CSF, and internal operational security policy requirements met across all deployment models ## Two lenses, one platform Olakai is one platform, one data model, one set of controls. **Olakai Agentic** and **Olakai Assistive** are the two lenses you use to look at it — they share data, share context, and share [Kai](https://olakai.ai/kai/). What you see in one lens is immediately visible in the other. ### Olakai Agentic Govern every operational agent running in your manufacturing environment — predictive maintenance, quality control, supply chain optimization — with full execution audit trails and policy enforcement before a bad action affects the line. Also measures AI coding tool ROI for the engineering and IT teams building your OT applications and MES integrations: token spend by provider and team, tied to actual velocity gains. [Explore Olakai Agentic →](https://olakai.ai/agent-iq/) ### Olakai Assistive Track every AI tool your engineering, procurement, supply chain, and operations teams are using — approved tools and shadow AI alike. License utilization across every seat. Time saved per knowledge worker per week. Shadow AI detection before unsanctioned tools touch proprietary process data. [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ![Olakai Agentic home dashboard showing operational agents, execution volume, policy status, and performance metrics across manufacturing workflows](/wp-content/uploads/2026/06/agent-iq-home-platform.webp) ### Olakai Agentic — Operational agent governance ## Every decision an AI agent makes on the plant floor. Logged and governed. Olakai Agentic gives your operations and engineering teams full visibility into every autonomous agent running in your manufacturing environment — predictive maintenance, quality control, inventory, supply chain. Every execution logged: inputs received, data accessed, decisions made, outcomes produced. When an anomaly occurs, the audit trail is there. When a regulator or customer auditor asks what your AI systems decided and why, the documentation exists. - Full execution log per operational agent: inputs, data accessed, decisions, outcomes - Policy enforcement at the workflow level — violations blocked before production impact - Audit-ready logs for customer audits, quality certifications, and regulatory review — generated automatically on-site ### Olakai Agentic — Engineering AI spend ## Control what your engineering teams spend on AI. Before the invoice lands. Your digital manufacturing and OT engineering teams are using AI coding tools to build faster. Token spend is growing — and without run-rate forecasting, you won’t know there’s a problem until the month-end invoice arrives. Olakai Agentic tracks every provider, every team, every developer, and projects month-end spend from a 7-day trailing average. All across every vendor and team. - Token spend by provider, team, and developer — normalized across Cursor, Copilot, Anthropic, OpenAI - Run-rate month-end forecast with trajectory alerts at 50%, 80%, and 100% of budget - Idle license identification — reclaim seats before the next renewal cycle ![Olakai Agentic home dashboard showing AI coding tool spend, adoption by team, and run-rate forecast for engineering teams](/wp-content/uploads/2026/06/coding-iq-home-platform.webp) ## The KPIs that matter in manufacturing Every pillar gives you a different lens on AI performance. Here are the metrics Olakai actually measures — across every vendor, every team, across every vendor and team. ### Olakai Agentic **Operational agent executions with complete audit trail per run** Every input received, data accessed, decision made, and outcome — logged end-to-end **Policy violations caught before production or supply chain impact** Enforcement at the workflow level, before a bad agent decision reaches the line **Engineering and IT team AI coding tool spend by provider and team** Normalized across Cursor, Copilot, Anthropic, and OpenAI in one view **Run-rate month-end forecast — alerts at 50%, 80%, 100% of budget** Trajectory problem weeks before it becomes an overrun on the invoice [Explore Olakai Agentic →](https://olakai.ai/agent-iq/) ### Olakai Assistive **Shadow AI tools touching proprietary process or operational data** By tool, team, and risk classification — a prioritized list, not just a count **License utilization: active vs. idle seats across every approved knowledge worker tool** Engineering, procurement, supply chain — reclaim unused seats before the next renewal **Time saved per engineer, procurement specialist, and supply chain manager per week** Hours recovered from AI-assisted workflows, by role and department **Prompt policy compliance rate across operational teams** Percentage of AI interactions meeting your data handling and usage policies [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ## Why Measurement Changes Everything ### From Agent Decisions to Governed Operations An AI agent that adjusts a supply chain order or flags a quality defect is making a consequential decision. When something goes wrong — and eventually something will — you need the audit trail. Olakai logs every decision before you need it, not after. * * * ### From AI Spend Opacity to Engineering Cost Control Your engineering teams are spending on AI coding tools faster than procurement can track. Olakai gives you run-rate forecasting and spend visibility by team and provider — so overruns show up as trajectory problems weeks before the invoice. ### From Shadow AI Risk to Governed Knowledge Worker AI Your engineers, procurement teams, and supply chain managers are using AI tools you didn’t issue — on proprietary process data, on supplier relationships, on production plans. Olakai makes every tool visible, classifies the risk, and gives you the data to govern rather than block. ## Ask your AI program a question. Get a reasoned answer. [Kai](https://olakai.ai/kai/) synthesizes every Olakai data source — agent logs, tool usage, spend data, governance status — and answers in plain English. With the reasoning shown and the evidence attached. - “Which operational agents have had policy violations or anomalous decisions in the last 7 days?” - “What’s our total AI spend across engineering this month, and are we on track to overrun our budget?” - “Which AI tools are our knowledge workers using on proprietary operational data that aren’t formally approved?” ## Talk to an Expert. See how Olakai governs AI from the plant floor to the boardroom — deployed where each part of your operation runs — tailored to your environment, no pitch. [Talk to an Expert](https://olakai.ai/schedule-a-demo/) --- ## Professional Services Source: /industries/professional-services Professional Services # Measure what every AI tool returns to the practice. Then govern it. Consultants, lawyers, and accountants are using AI tools on client work — most without procurement approval. Olakai shows you every tool in use, what it’s returning per practitioner, and whether client data is leaving your environment. ## Your practitioners are using AI on client engagements. Most of it you didn’t approve. Associates at your firm are using Harvey, ChatGPT, and Copilot on client work. Consultants are using Perplexity to research client industries and AI to analyze client data. Senior practitioners are using tools that never went through procurement. The productivity gains are real — but so is the client confidentiality exposure. When a client asks “what AI tools are you using on our matter?” most firms can’t give a complete answer. When your managing partner asks what AI is returning per billable hour, nobody has the data. And when a partner’s laptop is running AI tools that send client work product to an external cloud, your data governance policy just became theoretical. Olakai gives you the complete picture — and the infrastructure to back it up. ### By the Numbers ### Professional services firms report that more than 70% of AI tool usage by practitioners happens outside formally approved and managed applications — on client matters, with client data. *You can’t prove client confidentiality or ROI on tools you don’t know about.* ## Client data never leaves your environment. Olakai deploys inside your cloud tenancy or on your own hardware — AWS, Azure, GCP, or on-premises. No client work product, no matter details, no AI interaction logs are processed outside your perimeter. For law firms, consulting firms, and accounting practices with strict client confidentiality obligations, this isn’t a product feature — it’s the prerequisite your clients would require if they thought to ask. - Deploy in your own AWS, Azure, or GCP tenancy — or fully on-premises - Zero data egress: client work product, prompts, and interaction logs never leave your environment - Meets client confidentiality obligations and data handling commitments out of the box - Audit trail for every AI interaction with client matter data — inside your perimeter ## Two lenses, one platform Olakai is one platform, one data model, one set of controls. **Olakai Assistive** and **Olakai Agentic** are the two lenses you use to look at it — they share data, share context, and share [Kai](https://olakai.ai/kai/). What you see in one lens is immediately visible in the other. ### Olakai Assistive Track every AI tool your practitioners are using — on client matters and internally. License utilization across every approved seat. Shadow AI tools on client engagements, surfaced and risk-classified. Time saved per practitioner per week. The data your COO and managing partner need to run a governed AI program. [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ### Olakai Agentic Measure AI coding tool ROI for the internal engineering and IT teams supporting the practice, and govern every AI agent running in your practice — document processing, due diligence automation, contract review pipelines. Token spend by provider and team, full audit trails for every agent execution, and documentation ready for client review or regulatory inquiry. [Explore Olakai Agentic →](https://olakai.ai/agent-iq/) ![Olakai Assistive license utilization dashboard showing active, idle, and shadow AI tool usage by practitioner and practice group](/wp-content/uploads/2026/06/assistive-iq-licences-platform.webp) ### Olakai Assistive — License utilization ## Stop paying for seats nobody uses. Start measuring what they return. Olakai Assistive maps actual usage to every AI license you’re paying for — by practitioner, by practice group, by tool. See which seats are idle before the next renewal cycle. Identify which practitioners are getting ROI and which need enablement. And surface every shadow AI tool being used on client matters alongside the approved ones, in one view. - Active vs. idle seats across every approved AI copilot — by practitioner and practice group - Time saved per billable hour from AI-assisted research, drafting, and analysis - Shadow AI tools on client matters surfaced and risk-classified alongside licensed tools ### Olakai Assistive — Per-tool governance ## Know exactly what each AI tool is doing on client work. Click into any AI tool — Harvey, Copilot, ChatGPT, Perplexity — and see its risk classification, governance status, data exposure profile, and usage across the firm. Which practice groups are using it, what data it’s touching, whether it’s been reviewed for client confidentiality compliance. Governance controls sitting right next to usage analytics, in the same view. - Per-tool risk classification: client data exposure, practice group usage, data sensitivity - Licensed vs. shadow status, governance controls, and policy enforcement per tool - Prompt quality scoring and data handling compliance across every practitioner interaction ![Olakai Assistive per-app analytics showing usage, risk level, governance status, and data exposure for each AI tool](/wp-content/uploads/2026/06/assistive-iq-app-detail-platform.webp) ## The KPIs that matter in professional services Every pillar gives you a different lens on AI performance. Here are the metrics Olakai actually measures — across every vendor, every practitioner, inside your own infrastructure. ### Olakai Assistive **Shadow AI tools on client matters** By tool, practice group, and data risk classification **License utilization** Active vs. idle seats across every approved AI tool **Time saved per practitioner per week** Hours recovered from AI-assisted workflows, by role and practice group **Prompt policy compliance rate** Percentage of AI interactions that meet your data handling policies across client matter interactions [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ### Olakai Agentic **AI coding tool spend by provider and internal team** Normalized across every provider in one view **Run-rate month-end forecast** Trajectory alerts at 50%, 80%, and 100% of budget — before the overrun, not after **Document processing and due diligence agent executions** Full audit trail — every input received, tool called, decision made, and outcome **Policy violations caught before output is delivered** Enforcement at the workflow level, before the bad output reaches a client or partner [Explore Olakai Agentic →](https://olakai.ai/agent-iq/) ## Why Measurement Changes Everything ### From “We Think They’re Using ChatGPT” to a Complete Tool Inventory Guessing about practitioner AI usage isn’t a governance posture. Olakai shows you every tool on every client engagement — approved, shadow, and personal — so you can manage what’s actually happening rather than what you assume. * * * ### From Client Confidentiality Risk to Governed AI Practices The risk isn’t that practitioners use AI on client work. The risk is that they do it with tools that send client data to an external cloud. Olakai enforces the boundary — client matter AI stays inside your perimeter. ### From Idle License Spend to Billable AI ROI Paying for 500 Copilot seats when 200 are active is a finance problem. Not knowing which 200 are delivering ROI is a strategy problem. Olakai solves both — utilization and outcome in one platform. ## Ask your AI program a question. Get a reasoned answer. [Kai](https://olakai.ai/kai/) synthesizes every Olakai data source — tool usage, license utilization, agent logs, governance status — and answers in plain English. With the reasoning shown and the data behind every number. - “Which AI tools are our practitioners using on active client matters, and which of those aren’t approved?” - “What’s our license utilization across every AI copilot — how many seats are actually active this month?” - “Which practice groups are getting the most value from AI tools per practitioner hour?” ## Talk to an Expert. See how Olakai measures what every AI tool returns to the practice, while keeping client data inside your environment — tailored to your firm, no pitch. [Talk to an Expert](https://olakai.ai/schedule-a-demo/) --- ## Retail Ecommerce Source: /industries/retail-ecommerce Retail & E-Commerce # AI across every team. Spend under control. Customer data protected. Merchandising, marketing, customer service, and supply chain teams are all using AI tools — approved and shadow. Your customer-facing agents are making decisions at scale. Olakai gives you visibility and cost control across all of it, with full visibility into what every tool is touching and what every agent is spending. ## Your AI footprint is larger than any single team knows. Your merchandising team uses AI for demand forecasting. Marketing uses Copilot and Jasper for content at scale. Customer service is running AI agents handling thousands of returns and inquiries a day. Supply chain teams have three AI tools that procurement didn’t approve. The AI footprint is real — and nobody has a unified view of what it’s spending or returning. When AI spend spikes before peak season, you need to know which team is driving it before the invoice arrives. When a customer-facing agent starts behaving unexpectedly, you need the audit trail before it becomes a customer complaint. And when a new privacy regulation asks what AI tools are touching customer data, your CDO needs a complete answer — not a guess. ### By the Numbers ### Retail organizations with AI in customer-facing workflows report 40–60% variation in agent performance by season — but fewer than 20% have continuous monitoring in place to catch issues before customers do. *Seasonal spikes in AI usage create seasonal spikes in risk. You need visibility before the season, not after.* ## Flexible deployment — SaaS by default, private cloud when your data policy requires it. Olakai runs as SaaS out of the box — the fastest path to visibility across your AI tools and agents. For retailers with strict customer data policies, regional data residency requirements, or enterprise security standards that prohibit third-party data processing, Olakai also deploys inside your own AWS, Azure, or GCP tenancy, or fully on-premises. Most retail teams start with SaaS. The option to bring it inside your perimeter is there when you need it. - SaaS deployment: immediate visibility into AI tools, agents, and spend — no infrastructure work required - Private cloud or on-premises: available for enterprise retailers with strict customer PII or data residency requirements - CCPA, GDPR, and regional privacy regulation compliance supported across both deployment models - Same audit trail and governance capabilities whether you run SaaS or on-prem ## Two lenses, one platform Olakai is one platform, one data model, one set of controls. **Olakai Assistive** and **Olakai Agentic** are the two lenses you use to look at it — they share data, share context, and share [Kai](https://olakai.ai/kai/). What you see in one lens is immediately visible in the other. ### Olakai Assistive Track every AI tool your merchandising, marketing, operations, and support teams are using — approved and shadow. License utilization across every seat. Time saved per workflow. The complete picture of how AI is spreading across the organization and what it’s actually returning. [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ### Olakai Agentic Govern every customer-facing and operational agent — recommendation engines, customer service chatbots, returns automation, inventory agents — with full execution logs and policy enforcement before customer impact. Also measures AI coding tool ROI for the engineering teams building your e-commerce platform: token spend by provider, team, and developer, with run-rate forecasting before the invoice arrives. [Explore Olakai Agentic →](https://olakai.ai/agent-iq/) ![Olakai Assistive overview dashboard showing AI tool usage, adoption rates, and value created across teams and departments](/wp-content/uploads/2026/06/assistive-iq-overview-platform.webp) ### Olakai Assistive — Cross-team AI visibility ## One view of every AI tool across every team. Olakai Assistive surfaces every AI tool in use across merchandising, marketing, customer service, and supply chain — approved licenses and shadow AI alike. Ranked by adoption, value created, and data exposure risk. When you need to answer “what AI tools are we running across the organization?” this is the view that gives you the answer in seconds, not weeks. - Every AI tool in use surfaced — approved, shadow, and consumer apps across all teams - Value created and time saved by team, department, and tool — not just adoption counts - 630+ tools tracked; shadow AI surfaces highlighted with risk and data exposure classification ### Olakai Agentic — Customer-facing agent governance ## Know what every agent is doing before customers tell you something’s wrong. Olakai Agentic gives you a real-time view of every AI agent in your retail environment — customer service chatbots, recommendation engines, returns automation, inventory agents. Execution volume, policy status, performance trends. When an agent starts behaving unexpectedly — before peak season, during a promotion — you see it in the platform, not in your NPS score. - Real-time visibility across every customer-facing and operational agent - Policy violations caught before they reach customers or generate regulatory exposure - Seasonal performance monitoring — catch degradation before it becomes a customer impact ![Olakai Agentic home dashboard showing active agents, execution volume, policy status, and performance across customer-facing workflows](/wp-content/uploads/2026/06/agent-iq-home-platform.webp) ## The KPIs that matter in retail & e-commerce Every pillar gives you a different lens on AI performance. Here are the metrics Olakai actually measures — across every vendor, every team, across every vendor and team. ### Olakai Assistive **AI tools in use across merchandising, marketing, and operations — approved and shadow** Every tool surfaced, ranked by adoption, value, and data exposure risk **License utilization: active vs. idle seats across every AI copilot** By tool, team, and individual — reclaim before the next renewal **Time saved per team member per week by department and workflow** Hours recovered from AI-assisted work, not just adoption counts **Prompt policy and data handling compliance rate** Percentage of AI interactions meeting your data handling and usage policies [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ### Olakai Agentic **Customer-facing agent executions with complete audit trail** Every input received, tool called, decision made, and outcome — per execution **Policy violations caught before customer impact** Enforcement at the workflow level, before the bad experience happens **Engineering team AI coding tool spend by provider and team** Normalized across Cursor, Copilot, Anthropic, and OpenAI in one view **Run-rate month-end forecast — alerts at 50%, 80%, 100% of budget** Trajectory problems visible weeks before they become invoice surprises [Explore Olakai Agentic →](https://olakai.ai/agent-iq/) ## Why Measurement Changes Everything ### From Scattered AI Spend to Unified Cost Control Multiple vendor bills, no unified view, no forecast. Olakai consolidates every AI spend signal — copilots, coding tools, agent infrastructure — into one view with run-rate forecasting. You know where the bill is headed before it arrives. * * * ### From Customer Agent Risk to Continuous Monitoring Shipping a customer-facing AI agent without monitoring isn’t a launch — it’s a liability. Olakai logs every execution and enforces policy in real time, so you catch issues before customers do, not after. ### From Shadow AI Tolerance to Governed Adoption Every team is using AI tools you didn’t issue. That’s not a problem to solve — it’s a signal to capture. Olakai makes every tool visible, quantifies the risk and the value, and gives you the data to decide what to sanction and what to shut down. ## Ask your AI program a question. Get a reasoned answer. [Kai](https://olakai.ai/kai/) synthesizes every Olakai data source — spend, adoption, agent logs, governance status — and answers in plain English. With the reasoning shown and the data behind every number. - “What’s our total AI spend this month across merchandising, marketing, and customer service?” - “Which customer-facing agents have had policy violations or unexpected behaviors in the last 7 days?” - “Which AI tools across the organization are touching customer PII without governance controls in place?” ## Talk to an Expert. See how Olakai connects AI spend to ROI across every team, from customer-facing agents to engineering — tailored to your stack, no pitch. [Talk to an Expert](https://olakai.ai/schedule-a-demo/) --- ## Technology Software Source: /industries/technology-software Technology & Software # Run AI like the product line it’s becoming. Tech companies are the biggest AI spenders and the biggest AI builders. Engineering teams burning token budgets, product teams shipping AI features, support orgs deploying customer-facing agents — all of it needs the same governance layer. ## You’re not just using AI. You’re shipping it. Your engineering teams are spending heavily on Cursor, GitHub Copilot, and Claude to ship faster. Most can’t tell you the ROI per developer or forecast next month’s token bill. The spend is real and growing — the visibility isn’t. Your product team is shipping AI-powered features to customers. Your support org is running agents that handle thousands of conversations a day. Your CISO is asking which of those have a proper audit trail. Most AI governance vendors treat you like an enterprise buyer of AI tools. You’re also a builder — and that distinction matters. ### By the Numbers ### Engineering teams at high-growth tech companies report AI coding tool adoption across 60–80% of developers — but fewer than 1 in 5 can demonstrate the productivity ROI in a board meeting. *The companies that govern AI spend and output from the same platform are the ones that keep deploying. The rest stall at the budget review.* ## SaaS or on-prem — your architecture, your choice. Most technology companies deploy Olakai as SaaS — up and running in hours, no infrastructure to manage. If your security policy, enterprise customer contracts, or data residency requirements call for something different, Olakai also runs inside your own AWS, Azure, or GCP tenancy, or fully on-premises. Same platform, same capabilities. The architecture fits your requirements, not the other way around. - SaaS deployment: fastest time to value, no infrastructure overhead - Private cloud or on-premises: available when SOC 2, data residency, or enterprise customer security requires it - Source code, prompts, and agent logs stay in your chosen environment — never processed outside it - Same governance capabilities and audit trails regardless of deployment model ## Two lenses, one platform Olakai is one platform, one data model, one set of controls. **Olakai Agentic** and **Olakai Assistive** are the two lenses you use to look at it — they share data, share context, and share [Kai](https://olakai.ai/kai/). What you see in one lens is immediately visible in the other. ### Olakai Agentic Measure AI coding tool ROI and govern every agent you ship, in one lens. Token spend by provider, team, and developer. Run-rate forecasting before the invoice arrives. Full execution audit trails and policy enforcement before a bad decision reaches a user. The observability layer your engineering and product teams need, from code to production. [Explore Olakai Agentic →](https://olakai.ai/coding-iq/) ### Olakai Assistive Track every AI tool your non-engineering teams are using — sales, marketing, support, operations. License utilization, shadow AI detection, time saved per workflow. The visibility your IT and security teams need before a compliance question lands on their desk. [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ![Olakai Agentic developer view showing per-developer adoption cohorts, AI-assisted PR ratio, and cost by developer](/wp-content/uploads/2026/06/coding-iq-developers-platform.webp) ### Olakai Agentic — Developer adoption ## Find who’s getting value. Reclaim what isn’t. Olakai Agentic segments every developer into one of four adoption cohorts — Power, Casual, New, or Idle — and shows you which tools they use, how often, and what their cycle time looks like against peers. Estimated and reconciled actual cost attributed per developer, side by side. The view that tells you exactly who to enable, who to train, and where to reclaim licenses before the next renewal. - Power (\>70% AI-assisted PRs), Casual (20–70%), New (first AI PR in 14 days), Idle (\<20%) — segmented automatically - Per-developer cost: estimated vs. reconciled actual, side by side - Idle license identification before renewal — reclaim before the next contract cycle ### Olakai Agentic — Production agent governance ## Know what every agent you shipped is doing in production. Olakai Agentic gives your engineering and product teams full visibility into every autonomous agent running in your environment — customer-facing chatbots, internal automation, AI features in your product. Every execution logged: inputs received, tools called, decisions made, outcomes produced. Policy enforcement at the workflow level means issues are caught before they reach users. - Every agent execution logged end-to-end — inputs, tools, decisions, outcomes, latency - Policy violations caught at the workflow level before user impact - Audit-ready logs for enterprise customer security reviews and SOC 2 compliance ![Olakai Agentic workflow performance view showing execution metrics, policy status, and outcomes across deployed agents](/wp-content/uploads/2026/06/agent-iq-workflow.webp) ## The KPIs that matter in technology & software Every pillar gives you a different lens on AI performance. Here are the metrics Olakai actually measures — across every vendor, every team, across every vendor and team. ### Olakai Agentic **Token spend by provider, team, and developer** Normalized across Cursor, Copilot, Anthropic, OpenAI in one view **Run-rate month-end forecast** Trajectory alerts at 50%, 80%, and 100% of budget — before the overrun, not after **Agent executions with complete audit trail per run** Every input received, tool called, decision made, and outcome produced **Policy violations caught before user impact** Enforcement at the workflow level — before the bad decision reaches a user [Explore Olakai Agentic →](https://olakai.ai/coding-iq/) ### Olakai Assistive **Shadow AI tools in use across sales, marketing, support, and operations** By tool, team, and risk classification — a prioritized list, not just a count **License utilization** Active vs. idle seats across every approved tool, by team and by individual **Time saved per team member per week** Hours recovered from AI-assisted workflows, by role and department **Prompt policy compliance rate** Percentage of AI interactions that meet your data handling and usage policies [Explore Olakai Assistive →](https://olakai.ai/assistive-iq/) ## Why Measurement Changes Everything ### From AI Spend Opacity to Engineering ROI Token bills grow. Headcount stays flat. But “we spent $2M on AI coding tools” isn’t a ROI story. Olakai connects spend to output — PR velocity, cycle time, code quality — by team, by provider, by developer. * * * ### From Shipped Agents to Governed AI Products Shipping an AI feature without an audit trail isn’t a tech debt problem — it’s a customer trust problem. Olakai gives every agent execution a complete log, so your enterprise customers can ask “what did your AI do?” and get an answer. ### From Shadow AI Tolerance to Measured Adoption “We know people use ChatGPT” is not a governance posture. Olakai shows you every tool in use across every team, quantifies the exposure, and gives you the data to decide what to sanction, what to govern, and what to turn off. ## Ask your AI program a question. Get a reasoned answer. [Kai](https://olakai.ai/kai/) synthesizes every Olakai data source — spend, adoption, agent logs, governance status — and answers in plain English. With the reasoning shown and the data behind every number. - “Which AI coding tools are producing the fastest PRs, and what’s our cost per merged line of code?” - “Which agents running in our product environment have had policy violations in the last 30 days?” - “What’s our total AI spend this month across engineering, product, and support teams?” ## Talk to an Expert. See how Olakai gives your engineering and product teams one governed view across every AI coding tool, agent, and copilot you run — tailored to your stack, no pitch. [Talk to an Expert](https://olakai.ai/schedule-a-demo/) --- ## Integrations Source: /integrations [Key Features](/platform/) \| [Measure ROI](/ai-roi/) · [Govern Risk](/ai-governance/) · [Agent IQ](/agent-iq/) · [Custom KPIs](/analytics-kpis/) · [Monitor AI](/complete-ai-monitoring/) · [Shadow AI](/shadow-ai/) · Integrations · [Kai](/kai/) # Connect every AI tool in your stack. VCS · Coding tools · Copilots · Agents · Model providers. ## Every AI tool you run. Already connected. Your AI usage, cost, and risk are scattered across a dozen vendor dashboards — each showing only its own slice, none tying a dollar to a team, a project, or an outcome. **Olakai connects to all of them out of the box** and normalizes everything into one view, so you can finally answer “is our AI working, and what is it costing us?” - GitHub & BitbucketPull-request analytics straight from your VCS — AI-assisted vs. human PRs, cycle time, and code ratio. No agent, no SDK to install. - Anthropic (Claude Code)Per-developer token usage and reconciled spend from the Claude admin API. - OpenAI (Codex & ChatGPT)Usage and cost across Codex and ChatGPT Enterprise, by user and model. - CursorSeat usage, model mix, acceptance, and spend from the Cursor admin API. - Google (Gemini & Vertex)Usage from Cloud Monitoring plus a reconciled bill from your BigQuery export — a real bill, not a token estimate. - AWS (Bedrock)Model usage and spend across Amazon Bedrock. - Microsoft CopilotM365 Copilot and GitHub Copilot adoption, seat ROI, and spend — see the featured integration below. - SDK, extension & automationDirect SDK/API, a browser extension, and Zapier / n8n capture any AI tool — 600+ assistive apps across your org. ### Up and running in minutes ### Connect your whole stack in an afternoon — not a quarter-long rollout. Drop in your provider admin API keys and point Olakai at your GitHub or Bitbucket org. Data flows within minutes. No heavy SDK rollout, no per-tool instrumentation, no waiting on every team to adopt a new agent. Start with what you have connected today and add sources as you go. ![Olakai integrations settings showing AI Model Providers, VCS (GitHub/Bitbucket), coding-agent hooks, and Jira connections, each feeding Coding IQ, Assistive IQ, and Agent IQ](/wp-content/uploads/2026/06/integrations-settings.webp) Featured partner ## Native Microsoft 365 Copilot analytics, the moment you connect. Olakai is a **Microsoft partner** with native access to your Microsoft 365 Copilot data — nothing to instrument. Connect once and immediately see how Copilot is used across **Word, Excel, PowerPoint, Outlook, and Teams**, with adoption, seat-level ROI, spend, and governance updating in near real time — the visibility Microsoft’s own dashboard won’t give you, alongside the rest of your AI stack. Microsoft Copilot — M365 & GitHub **Immediate Microsoft 365 visibility** From the moment you connect, see Copilot usage patterns across Word, Excel, PowerPoint, Outlook, and Teams — by app, team, and workflow. No instrumentation, no waiting on exports. * * * **Analytics & governance in near real time** Adoption, seat ROI, spend, and risk update continuously — not in a monthly report. Surface sensitive data in prompts and policy drift as it happens. * * * **Seat-level Copilot ROI** Prove whether thousands of M365 and GitHub Copilot licenses are actually paying off, user by user — the metric that justifies, or trims, the next renewal. * * * **One view with your whole stack** Compare Copilot side by side with Anthropic, OpenAI, Google, AWS, Cursor, and your agents. Make vendor decisions on data, not demos. ### Connect your AI stack. Start measuring. [Talk to an Expert](/schedule-a-demo/) --- ## Kai Source: /kai [Key Features](/platform/) \| [Measure ROI](/ai-roi/) · [Govern Risk](/ai-governance/) · [Agent IQ](/agent-iq/) · [Custom KPIs](/analytics-kpis/) · [Monitor AI](/complete-ai-monitoring/) · [Shadow AI](/shadow-ai/) · [Integrations](/integrations/) · Kai # Ask your AI program anything. Get a reasoned answer in seconds. Kai is the conversational layer on top of Olakai. Instead of building a dashboard or filing a request with the analytics team, executives just ask. ROI by department, shadow AI in legal, which agents are underperforming, which engineering teams are getting the most out of Cursor. Kai pulls the data, runs the analysis, and shows you the reasoning behind every answer. ![Kai delivering a board recommendation — governance health, measurable value created, and prioritized next actions](/wp-content/uploads/2026/06/kai-board-reco-platform.webp) ## Your board doesn’t want a dashboard. They want an answer. Most enterprise AI platforms force the same cycle: a question gets asked in a meeting, an analyst builds a report, three days later the answer arrives — by which point the question has changed. Kai compresses that loop to seconds, and shows the reasoning behind every number so you can trust it. ## Three questions. Three answers. Zero meetings. - For the CFO — “What’s our AI ROI this quarter, broken down by department?” · Kai pulls usage and cost data from Agent IQ, Assistive IQ, and Coding IQ, applies your wage configurations, calculates time saved, and returns a board-ready breakdown by department — in one chat exchange. Every number has a “show reasoning” link so finance can audit how it got there. - For the CISO — “Where is shadow AI hurting us right now?” · Kai surfaces unauthorized AI tools detected in the last 30 days, ranks them by risk surface (sensitive data, regulated departments, prompt content), and recommends what to govern, what to approve, and what to block — backed by the actual events Olakai captured in the browser extension. - For the VP Engineering — “Which engineering team has the highest cycle-time delta from AI coding tools?” · Kai compares AI-assisted vs non-AI cycle time across every team in your GitHub org, ranks them, and tells you which teams are getting the most leverage from Cursor, Copilot, Claude Code, and Windsurf. Then it recommends where to standardize and where to coach. ![Kai showing the expanded reasoning chain behind an answer](/wp-content/uploads/2026/06/kai-agent-roi-platform.webp) ### Transparent reasoning ## Every answer shows its work. This is the line that separates Kai from a chatbot. Click any number Kai gives you and you see the reasoning chain — which data sources were queried, which filters were applied, which calculations were run, and which assumptions were made. If your CFO doesn’t trust the number, they can audit it line by line. If they do trust it, they can put it in front of the board. - Cross-data synthesis across Agent IQ, Assistive IQ, and Coding IQ in a single answer - Actionable recommendations: what to scale, what to fix, what to investigate next - Scenario modeling: forecast ROI, run “what if” analyses on scaling decisions - Trend analysis that surfaces patterns and anomalies you didn’t think to ask about ## Kai is the front door to Olakai. Behind it, the platform is doing the work. [Agent IQ](https://olakai.ai/agent-iq/) is measuring every autonomous workflow. [Assistive IQ](https://olakai.ai/assistive-iq/) is tracking every copilot interaction and surfacing shadow AI. [Coding IQ](https://olakai.ai/coding-iq/) is connecting every PR to provider cost. Kai is what makes all of that accessible to anyone in the business — no SQL, no dashboards to learn, no analyst in the middle. ### Ready to measure what matters? Stop guessing. Start optimizing. [Talk to an Expert](/schedule-a-demo/) --- ## Partners Source: /partners Partner Program # Build Your AI Practice on Olakai The analytics and governance platform partners embed in every AI engagement Your clients deploy AI. You help them prove it works. Olakai is the analytics platform that makes measurement part of every engagement, from pilot to board report. [Apply to Partner Program](#apply) Partner Tracks ## Three Ways to Partner - Consulting Partners — For AI strategy boutiques, transformation firms, and implementation consultancies · Embed Measurement in Every Deliverable · Turn one-time strategy projects into recurring measurement retainers. Differentiate from Big 4 with real-time analytics instead of slide decks. Access Olakai Agentic and Olakai Assistive to deliver data-driven recommendations, with co-branded board-ready reports featuring your firm’s logo. - Technology Partners — For AI platforms, cloud providers, LLM vendors, and security tools · Integrate with Enterprise AI Analytics · Pre-built integrations and API access for seamless connectivity with Olakai Agentic and Olakai Assistive. Joint solution briefs and co-marketing opportunities. Shared enterprise pipeline with deal registration. - Referral Partners — For advisors, analysts, and anyone who sends qualified leads · Earn Revenue Connecting Enterprises to AI Analytics · 15-20% referral fee on closed deals with no deployment or support obligations. Refer enterprises evaluating Olakai Agentic or Olakai Assistive and get a dedicated partner success contact, deal registration, and full pipeline visibility. ## Your Clients Ask: Is AI Working? Now You Can Answer. - Prove Value, Not Just Strategy — Most AI consulting engagements end with a recommendation deck. With Olakai, you deliver continuous measurement, turning a one-time project into an ongoing advisory relationship. Your clients get real-time dashboards instead of quarterly status updates. - Win Against Big 4 — Enterprise clients are tired of six-figure strategy decks with no accountability. Olakai gives boutique firms the same analytical depth as McKinsey’s AI practice, with real-time data instead of quarterly reports. Prove outcomes, don’t just promise them. - From Pilots to Board-Level Impact — Help your clients move past pilot purgatory. Olakai Agentic and Olakai Assistive give executives the proof points they need to justify AI investment and scale adoption company-wide. You become the advisor who connects AI activity to business outcomes. Program Tiers ## A Program Designed to Grow With You - Referral Partner — Recommend Olakai in your strategy deliverables and earn referral revenue. No certification required. · 15-20% referral fee on closed deals · Deal registration portal · Partner newsletter and program updates - Certified Partner — Deploy Olakai as part of your AI engagements. Complete certification training and access co-sell resources. · 30-40% deployment margins · Co-sell with the Olakai team · Co-branded materials and case studies · Partner directory listing · Olakai certification training (virtual) - Founding Partner — Limited Availability · Shape the future of AI analytics alongside Olakai. Everything in Certified, plus exclusive benefits reserved for select firms. · Product roadmap input · White-label options · Executive access to Olakai leadership · Joint GTM campaigns and events · Dedicated success manager · Currently accepting applications from select firms. ## Our Founding Partners ![AI Aspire logo](https://olakai.ai/wp-content/uploads/2025/09/AI_Aspire_White_Logo_RGB.svg) AI Aspire, backed by Andrew Ng’s AI Fund, embeds Olakai’s measurement framework into enterprise AI strategy engagements, helping Fortune 500 clients prove AI ROI. > “Partnering with Olakai has opened doors to Fortune 500 clients and given us a unique governance story to tell in every conversation.” > > **Kirsty Tan**, Managing Director, AI Aspire ### SolusGuard SolusGuard builds workforce safety technology for high-risk industries, from lone worker monitoring to AI-driven incident prediction. Olakai gives their enterprise clients visibility into how AI is performing across safety operations. > “Our clients in healthcare and field services need to know their AI safety systems are working. Olakai gives us the analytics layer to prove it, and that proof is what turns a pilot into a company-wide rollout.” > > **Serese Selanders**, CEO & Founder, SolusGuard **Backed by AI Fund** (Andrew Ng) · Investor in Olakai Interested in joining as a Founding Partner? We’re selecting a small number of firms to shape the program. [Apply as Founding Partner](#apply) ## Everything You Need to Succeed Benefit Referral Certified Founding Referral fees (15-20%) ✓ ✓ ✓ Deal registration portal ✓ ✓ ✓ Partner newsletter ✓ ✓ ✓ Certification training — ✓ ✓ Deployment margins (30-40%) — ✓ ✓ Co-branded reports — ✓ ✓ Co-sell with Olakai — ✓ ✓ Partner directory listing — ✓ ✓ Product roadmap input — — ✓ White-label options — — ✓ Executive access — — ✓ Joint GTM campaigns — — ✓ Dedicated success manager — — ✓ ## Join the Olakai Partner Program We respond within 48 hours. " \* " indicates required fields Phone This field is for validation purposes and should be left unchanged. - First Name - Last Name - Business Email - Company - Role / Title - Partner Type --- ## Become A Partner Source: /partners/become-a-partner # become a partner ## Partner with Olakai Join a trusted ecosystem of advisors, design partners, and channel leaders helping the world’s largest enterprises adopt **Agentic & Assistive AI** with visibility, control, and measurable **ROI.** What You’ll Gain as a Partner or Advisor: - **Revenue Growth:** Unlock recurring revenue through resale, referral, and advisory programs. - **Market Differentiation:** Offer the only enterprise-grade AI Analytics Platform unifying **Agent IQ™,** **Assistive IQ™** and a Partner / Client Portal - **Early Access:** Receive roadmap previews, partner training, and exclusive GTM enablement. - **Joint GTM:** Collaborate on co-branded campaigns, enterprise events, and customer introductions. - **Executive Network:** Leverage Olakai’s C-suite relationships across finance, healthcare, and other regulated industries. > **Partnering with Olakai has given AI Aspire the ability to guide clients with measurable ROI benchmarks, enterprise-grade governance, and the confidence to scale AI responsibly. It’s a true intelligence layer for the future of enterprise AI.** > > – *Kirsty Tan, Managing Partner, AI Aspire* ### Book Your Partnership Workshop Your demo will be tailored to your practice and client ecosystem. " \* " indicates required fields Instagram This field is for validation purposes and should be left unchanged. - First Name - Last Name - Business Email - Company - Role / Title - Partner Type --- ## Platform Source: /platform The Platform \| The Products » [Olakai Agentic](/coding-iq/) · [Olakai Assistive](/assistive-iq/) # The World’s Most Robust Platform for AI Governance and ROI ### Technology & Solutions ## Overview Olakai unifies measurement, governance, and enablement into a single intelligence platform, giving leaders visibility across coding, assistive, and agentic AI — from AI coding tools to copilots, Chat/GenAI assistants, SaaS AI, and autonomous agents — with built-in risk control and provable ROI in hours and dollars. Enterprises are already running agents, copilots, and AI-powered SaaS workflows, but the activity is fragmented across tools, making it hard to see what’s working, what it’s costing, and where governance gaps exist. **Olakai turns AI interactions from any source into operational intelligence.** We capture agent runs, prompts, and workflow events, normalize them into a common model, store them securely, and apply our Insights Engine to detect patterns and quantify performance – usage, cost, quality, and outcomes. Those insights then power analysis and control: prioritize what to scale, cut waste, and enforce guardrails with confidence. - Finance Agent — Interaction - Olakai standardizes and cleans data — Data Normalization · Secure Storage · Insights Engine - Analysis and Control Olakai includes two products: **Olakai Agentic™** for proving the ROI of every AI coding tool and governing every autonomous agent workflow across every team and provider, and **Olakai Assistive™** for proving adoption and ROI across copilots, chat/GenAI, and AI-powered SaaS. All run on the same data foundation—one platform, one model, one set of controls. * * * ## Olakai Agentic™ **AI coding tools are everywhere — and their costs are now usage-based and climbing.** Olakai Agentic answers the three questions every engineering leader is being asked: are we actually adopting AI, is it making us faster, and what is it costing us — is that spend under control? One vendor-neutral view across Claude Code, Cursor, Codex, and Copilot, built on your pull-request data and per-developer tool usage and real spend. * * * What Olakai Agentic™ Does - Forecasts month-end AI spend and enforces budgets across six overlapping lenses — program, provider, developer, persona, department, and project — alerting before they overrun, not after - Translates productivity gains into dollars with the AI Equivalent Engineers ROI metric, using a per-developer before/after method over fixed calendar windows - Measures real velocity — AI-assisted vs. unassisted PR cycle time — from GitHub pull-request analytics (Bitbucket in beta) - Tracks genuine adoption cohorts (power, casual, new, idle) plus per-developer model and token usage from desktop agent hooks and provider admin APIs - Governs coding traffic with PII, PHI, and secret detection scoped to coding agents ![Olakai Agentic home dashboard showing AI coding spend summary and forecast](/wp-content/uploads/2026/06/coding-iq-home-platform.webp) #### Benefits - Vendor-neutral spend visibility and month-end forecasting across every coding provider - Velocity proof from PR cycle time, not acceptance-rate vanity metrics - One ROI dashboard finance and engineering both trust — and budgets that alert before they overrun #### Risk of inaction Token spend climbs unforecasted, idle licenses pile up, and “AI made us faster” stays a claim you can’t defend to the board. * * * What Agent IQ Does - Tracks and visualizes agentic workflows, actions, and performance in real time - Benchmarks Agent efficiency, collaboration, and ROI contribution - Correlates Agent usage with productivity, cost savings, and outcomes - Surfaces insights and alerts for governance, optimization, and scaling ![Olakai Agent IQ home dashboard showing agentic AI ROI, value created, executions, and governance compliance](/wp-content/uploads/2026/06/agent-iq-home-platform.webp) #### Benefits - Unified observability for all Agents across LLMs and SaaS systems - Real-time measurement of agentic value and performance trends - Transparency to manage, optimize, and trust autonomous workflows #### Risk of inaction Agents operate unseen, inefficiencies multiply, and automation ROI remains unproven. * * * ## Olakai Assistive™ **Enterprises cannot manage what they cannot measure.** The Olakai Insight Suite™ delivers enterprise-wide visibility and board-grade analytics that prove AI’s business value in hours and dollars, not anecdotes. * * * What Olakai Assistive IQ Does - Aggregates data from Agents, GenAI tools, and SaaS workflows into the **OLA Index™** - Benchmarks adoption and maturity across teams, regions, personas, and applications - Quantifies time saved, efficiency gains, and net dollar impact at every level - Produces board-ready dashboards on productivity, automation, and ROI ![Olakai Assistive license utilization dashboard showing per-seat usage and unused license spend](/wp-content/uploads/2026/06/assistive-iq-licences-platform.webp) #### Benefits - Real-time evidence of AI’s value across the enterprise - Trusted ROI and performance metrics for boards and finance teams - Data-driven prioritization of strategy and investment #### Risk of inaction AI success remains anecdotal, performance goes unmeasured, and confidence erodes. * * * ## Core Technology Olakai’s Insight Engine turns AI activity into actionable business intelligence through a four-step pipeline. First, we augment the data received from coding, assistive, or agentic AI by enriching each interaction with context – agent function, user role, and department—creating a standardized foundation for analysis. Next, our AI models classify every interaction by task type and subtask, automatically categorizing everything. Then we calculate the time saved by comparing AI-assisted work against traditional benchmarks, converting efficiency gains into hours reclaimed. Finally, we apply your organization’s wage configurations to translate those hours into dollar value—giving you a clear, defensible ROI figure for every AI investment. - AI Interaction — ChatGPT - Augment - Classify - Calculate - Value - Analysis and Control - Agent function — Context · User role · Department - Subtask taxonomy — AI Models · Task patterns - Efficiency formulas — Olakai API · Task benchmarks - Role multipliers — Wage Config · Hourly rate **One Platform, Every Data Source**. Olakai [connects to your AI stack](/integrations/) however it’s built – via direct API/SDK integration, vendor admin APIs (Cursor, Anthropic, Copilot), automation platforms (Zapier, n8n), or browser extension. Every signal flows into Olakai Analytics for a unified view of usage, cost, quality, and governance across all your AI tools. - TypeScript & Python — Olakai API/SDK - Automation Platforms — Zapier, n8n, Make - Cursor, Anthropic, Copilot — Admin APIs - Browser Extension — Chrome & Edge - Olakai ### Unlock the Power of Intelligent AI Today [Schedule a Call](https://olakai.ai/schedule-a-demo/) --- ## Podcast Source: /podcast PODCAST # Enterprise AI Unlocked Hosted by Paul Brzozowski, each episode uses a five-question framework to unpack what works, what doesn’t, and how to measure success when deploying AI at scale. [Latest Episode · Episode 11 · The AI Governance Roundtable: Who Owns It? · Most enterprises think they have AI governance, but they actually have a policy document. IBM’s HR AI Director and Lyra Labs’ COO reveal why 90% of organizations operate at “Governance 0.5” and share the four-part operating model that separates leaders… · May 25, 2026 · 34 min · with Kate Ashworth Brash, Rob Saltrese · Watch Episode →](https://olakai.ai/podcast/ai-governance-roundtable-who-owns-it/) - [The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) — Episode 10 · Anthropic's Claude Mythos breach within days of launch triggered a White House intervention and quietly re-priced cyber risk across governments… · May 4, 2026 · 34 min · with Jason Smith, Rob Saltrese - [The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) — Episode 9 · Join industry leaders as they dissect the real challenges of scaling AI in enterprise environments. Discover why 70% of AI… · Apr 9, 2026 · 34 min - [Your Job Isn’t to Buy AI. It’s to Buy Outcomes.](https://olakai.ai/podcast/your-job-isnt-to-buy-ai-its-to-buy-outcomes/) — Episode 8 · Most AI tools in sales become shelfware within 90 days. Anup Khera, named Top 25 CRO to Follow in 2026,… · Mar 24, 2026 · 43 min · with Anup Khera - [Why AI Adoption Keeps Failing Despite Leadership Priority](https://olakai.ai/podcast/why-ai-adoption-keeps-failing-despite-leadership-priority/) — Episode 7 · Despite AI being CEOs' top priority, 56% of companies report zero revenue benefit and 95% of pilots fail to impact… · Mar 10, 2026 · 43 min · with Rob Saltrese - [Fortune 500 AI Playbook](https://olakai.ai/podcast/fortune-500-ai-playbook/) — Episode 6 · After working with dozens of Fortune 500 clients, Helena Ristov reveals why 70% of enterprise AI projects never escape pilot… · Jan 8, 2026 · 37 min · with Helena Ristov - [Breaking Through Voice AI Accuracy Barriers](https://olakai.ai/podcast/breaking-through-voice-ai-accuracy-barriers/) — Episode 5 · Most voice AI systems hit an 83% accuracy ceiling before requiring human intervention—killing ROI for businesses operating on razor-thin margins…. · Dec 30, 2025 · 36 min · with Justin Foster - [Systems Thinking and Strategic AI Deployment](https://olakai.ai/podcast/systems-thinking-and-strategic-ai-deployment/) — Episode 4 · Former Marine Corps officer David Wood reveals why 90% of AI projects stall between pilot and production—and it's not a… · Dec 18, 2025 · 56 min · with David Wood - [Strategic AI Deployment for Private Equity and Venture Capital](https://olakai.ai/podcast/strategic-ai-deployment-for-private-equity-and-venture-capital/) — Episode 3 · Serial CEO Meshach Amuah-Foster has deployed AI across 20+ companies under PE/VC pressure—including putting AI agents directly in front of… · Dec 1, 2025 · 37 min · with Meshach Amuah-Foster - [AI Agents Revolutionizing Cybersecurity Operations](https://olakai.ai/podcast/ai-agents-revolutionizing-cybersecurity-operations/) — Episode 2 · Discover how AI agents are slashing cybersecurity investigations from 2.5 hours down to just 4 minutes in production environments. Nathan… · Nov 21, 2025 · 47 min · with Nathan Burke - 1 - [2](https://olakai.ai/podcast/page/2/) - [Next](https://olakai.ai/podcast/page/2/) --- ## Ai Agents Revolutionizing Cybersecurity Operations Source: /podcast/ai-agents-revolutionizing-cybersecurity-operations [← Back to Olakai's Podcast](/podcast/) - Episode 2 - November 21, 2025 - 47 min [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fai-agents-revolutionizing-cybersecurity-operations%2F)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fai-agents-revolutionizing-cybersecurity-operations%2F&text=AI+Agents+Revolutionizing+Cybersecurity+Operations) # AI Agents Revolutionizing Cybersecurity Operations *Discover how AI agents are slashing cybersecurity investigations from 2.5 hours down to just 4 minutes in production environments. Nathan Burke from 7AI reveals the critical difference between human work and non-human work, why “time saved” is the only ROI metric that matters, and how proper governance actually accelerates AI adoption. Learn the practical framework for identifying which security tasks should never require human intervention.* In this episode of Enterprise AI Unlocked, we sit down with Nathan Burke, Chief Marketing Officer at 7AI, to explore how AI agents are transforming cybersecurity operations from theory into production reality. Nathan shares firsthand insights on deploying autonomous security agents that investigate alerts in minutes instead of hours, and what that means for the future of enterprise AI. Originally published on [Future of Agentic](https://futureofagentic.com/podcast/cmi9ap52q0000p61s76ccml5t). ## Chapters 1. [0:00 Welcome & Episode Introduction](https://www.youtube.com/watch?v=WjYJjdxCk8k&t=0) 2. [2:15 What Autonomous Security Agents Actually Do](https://www.youtube.com/watch?v=WjYJjdxCk8k&t=135) 3. [7:45 Building Trust in AI Systems](https://www.youtube.com/watch?v=WjYJjdxCk8k&t=465) 4. [14:20 From Experimentation to Execution](https://www.youtube.com/watch?v=WjYJjdxCk8k&t=860) 5. [18:50 Proving Value with Time Saved](https://www.youtube.com/watch?v=WjYJjdxCk8k&t=1130) 6. [24:10 Automating Security Questionnaires Story](https://www.youtube.com/watch?v=WjYJjdxCk8k&t=1450) 7. [28:45 Prioritizing AI Initiatives](https://www.youtube.com/watch?v=WjYJjdxCk8k&t=1725) 8. [33:15 Human vs Non-Human Work](https://www.youtube.com/watch?v=WjYJjdxCk8k&t=1995) 9. [37:20 AI Governance and Auditability](https://www.youtube.com/watch?v=WjYJjdxCk8k&t=2240) 10. [41:45 Future of Enterprise AI](https://www.youtube.com/watch?v=WjYJjdxCk8k&t=2505) 11. [45:30 Key Takeaways & Closing](https://www.youtube.com/watch?v=WjYJjdxCk8k&t=2730) ## Guests & Hosts [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski/) Host Founding Team Member, Olakai Paul Brzozowski is a dynamic technology leader and founding team member at Olakai, where he drives innovation in product development and strategic growth. With extensive experience in startup ecosystems and digital transformation, Paul brings deep expertise in leveraging emerging technologies to solve complex business challenges. His background spans product strategy, engineering leadership, and entrepreneurial ventures, positioning him as a forward-thinking executive who bridges technical innovation with strategic business objectives. [Nathan Burke](https://www.linkedin.com/in/nathanwburke/) Guest Chief Marketing Officer, 7AI Nathan Burke is the Chief Marketing Officer at 7AI, where he leads strategic marketing initiatives for cutting-edge artificial intelligence technologies. With extensive experience in technology marketing and go-to-market strategies, Nathan has a proven track record of helping innovative AI companies develop compelling brand narratives and drive market adoption. His expertise spans digital marketing, product positioning, and helping organizations communicate complex technological solutions to diverse audiences. [Previous Episode EP1 From AI Experimentation to Measurable Business Impact](https://olakai.ai/podcast/from-ai-experimentation-to-measurable-business-impact/) [Next Episode EP3 Strategic AI Deployment for Private Equity and Venture Capital](https://olakai.ai/podcast/strategic-ai-deployment-for-private-equity-and-venture-capital/) ## Ready to measure your AI? See how Olakai helps enterprises prove ROI, govern risk, and scale AI confidently. [Schedule a Demo](https://olakai.ai/schedule-a-demo/) ## More Episodes - [The AI Governance Roundtable: Who Owns It?](https://olakai.ai/podcast/ai-governance-roundtable-who-owns-it/) — Episode 11 · with Kate Ashworth Brash, Rob Saltrese - [The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) — Episode 10 · with Jason Smith, Rob Saltrese - [The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) — Episode 9 --- ## Ai Governance Roundtable Who Owns It Source: /podcast/ai-governance-roundtable-who-owns-it [← Back to Olakai's Podcast](/podcast/) - Episode 11 - May 25, 2026 - 34 min [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fai-governance-roundtable-who-owns-it%2F)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fai-governance-roundtable-who-owns-it%2F&text=The+AI+Governance+Roundtable%3A+Who+Owns+It%3F) # The AI Governance Roundtable: Who Owns It? *Most enterprises think they have AI governance, but they actually have a policy document. IBM’s HR AI Director and Lyra Labs’ COO reveal why 90% of organizations operate at “Governance 0.5” and share the four-part operating model that separates leaders from laggards. Learn who really owns AI governance (hint: it’s not the CTO), how to govern agentic systems without losing human oversight, and actionable frameworks for moving beyond paperwork to operational excellence.* Most enterprises do not have a working AI governance program. They have a policy. Paul Brzozowski sits down with Kate Ashworth Brash, HR AI Director at IBM and focal point for HR and Talent on IBM’s AI Ethics Board, and Rob Saltrese, Co-Founder and COO of Lyra Labs, for an unfiltered conversation about the honest state of enterprise AI governance in 2026, and the operating model that separates organizations pulling ahead from the ones still writing the policy. Originally published on [Future of Agentic](https://futureofagentic.com/podcast/cmpmrhr1o01xgp6bt1nqfxev2). ## Chapters 1. [0:00 Welcome to The Roundtable](https://www.youtube.com/watch?v=gmzCoruqys4&t=0) 2. [2:00 From Policy to Operating Model](https://www.youtube.com/watch?v=gmzCoruqys4&t=120) 3. [4:00 Honest State of AI Governance](https://www.youtube.com/watch?v=gmzCoruqys4&t=240) 4. [11:00 Mature Operating Model Components](https://www.youtube.com/watch?v=gmzCoruqys4&t=660) 5. [20:00 System Data People Measurement Framework](https://www.youtube.com/watch?v=gmzCoruqys4&t=1200) 6. [28:00 Agentic AI and Human-in-the-Loop](https://www.youtube.com/watch?v=gmzCoruqys4&t=1680) 7. [33:00 Lightning Round](https://www.youtube.com/watch?v=gmzCoruqys4&t=1980) 8. [34:30 Takeaways and Close](https://www.youtube.com/watch?v=gmzCoruqys4&t=2070) ## Guests & Hosts [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski/) Host Co-Founder, Olakai Paul Brzozowski is co-founder of Olakai, where he leads the development of innovative solutions at the intersection of technology and business strategy. With deep expertise in building scalable platforms and driving product innovation, Paul brings a practical perspective to emerging technologies and their real-world applications. His work focuses on helping organizations navigate complex technical challenges while maintaining a clear vision for sustainable growth. [Kate Ashworth Brash](https://www.linkedin.com/in/kate-ashworth-brash-3b115a65/) Guest HR AI Director, IBM Kate Ashworth Brash is the HR AI Director at IBM, where she leads the strategic development and implementation of artificial intelligence solutions across human resources functions. With deep expertise in AI applications, organizational transformation, and workforce innovation, she drives initiatives that enhance employee experience and operational efficiency at scale. Her work bridges the gap between cutting-edge AI technology and practical HR strategy, helping organizations navigate the evolving landscape of AI-powered talent management. [Rob Saltrese](https://www.linkedin.com/in/rob-saltrese-17263a2/) Guest Co-Founder and COO, Lyra Labs Rob Saltrese is Co-Founder and Chief Operating Officer of Lyra Labs, where he leads operational strategy and business development for the company's AI-driven solutions. With extensive experience scaling technology ventures, Rob brings deep expertise in building high-performing teams and driving operational excellence in the AI and software sectors. His work focuses on translating complex technical innovation into sustainable business models that deliver measurable impact. [Previous Episode EP10 The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) ## Ready to measure your AI? See how Olakai helps enterprises prove ROI, govern risk, and scale AI confidently. [Schedule a Demo](https://olakai.ai/schedule-a-demo/) ## More Episodes - [The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) — Episode 10 · with Jason Smith, Rob Saltrese · 34 min - [The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) — Episode 9 · 34 min - [Your Job Isn’t to Buy AI. It’s to Buy Outcomes.](https://olakai.ai/podcast/your-job-isnt-to-buy-ai-its-to-buy-outcomes/) — Episode 8 · with Anup Khera · 43 min --- ## Breaking Through Voice Ai Accuracy Barriers Source: /podcast/breaking-through-voice-ai-accuracy-barriers [← Back to Olakai's Podcast](/podcast/) - Episode 5 - December 30, 2025 - 36 min [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fbreaking-through-voice-ai-accuracy-barriers%2F)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fbreaking-through-voice-ai-accuracy-barriers%2F&text=Breaking+Through+Voice+AI+Accuracy+Barriers) # Breaking Through Voice AI Accuracy Barriers *Most voice AI systems hit an 83% accuracy ceiling before requiring human intervention—killing ROI for businesses operating on razor-thin margins. Justin Foster, serial founder and CRO of Incept AI, reveals how his team broke through to 97% accuracy and the deployment playbook that actually works. Discover the three-dimensional bottleneck strangling voice AI adoption and why the 90% non-intervention rate is the economic threshold separating scalable systems from expensive experiments.* 73% of businesses say accuracy is why they don’t adopt voice AI. The ceiling? 83%. That’s where most voice AI systems hit before needing human intervention. In this episode, Justin Foster (Co-Founder & CRO of Incept AI) reveals how his company broke through to 97% accuracy, and shares the deployment playbook that actually works when AI meets noisy, chaotic reality. Justin’s been through multiple exits, and co-founded LiveClicker before building Incept. He’s raised $3M from Rally Ventures to solve what kills voice AI: the three-dimensional bottleneck of audio quality, latency, and economics in high-pressure customer environments. This isn’t about technology specs. It’s about the business model, deployment strategy, and hard-earned lessons that separate voice AI that scales from expensive experiments that burn budget. Originally published on [Future of Agentic](https://futureofagentic.com/podcast/cmjsuf4yp003cp6kehcqtrzs5). ## Chapters 1. [0:00 Voice AI Accuracy Ceiling Problem](https://www.youtube.com/watch?v=rWRXnEILQf8&t=0) 2. [4:20 Three-Dimensional Bottleneck Challenge](https://www.youtube.com/watch?v=rWRXnEILQf8&t=260) 3. [6:15 Incept AI Origin Story](https://www.youtube.com/watch?v=rWRXnEILQf8&t=375) 4. [9:30 From Experimentation to Execution](https://www.youtube.com/watch?v=rWRXnEILQf8&t=570) 5. [12:40 Non-Intervention Rate KPI](https://www.youtube.com/watch?v=rWRXnEILQf8&t=760) 6. [14:55 CFO Metrics That Matter](https://www.youtube.com/watch?v=rWRXnEILQf8&t=895) 7. [19:10 Customer-Driven Prioritization Framework](https://www.youtube.com/watch?v=rWRXnEILQf8&t=1150) 8. [22:30 Governance and Guardrails Strategy](https://www.youtube.com/watch?v=rWRXnEILQf8&t=1350) 9. [28:20 Future of Voice AI](https://www.youtube.com/watch?v=rWRXnEILQf8&t=1700) 10. [31:00 Serial Founder Lessons](https://www.youtube.com/watch?v=rWRXnEILQf8&t=1860) 11. [38:40 Why AI Hype Kills Products](https://www.youtube.com/watch?v=rWRXnEILQf8&t=2320) 12. [40:25 Setting Realistic Expectations](https://www.youtube.com/watch?v=rWRXnEILQf8&t=2425) 13. [42:30 Voice AI Deployment Checklist](https://www.youtube.com/watch?v=rWRXnEILQf8&t=2550) ## Guests & Hosts [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski/) Host Olakai Paul Brzozowski is the founder and CEO of Olakai, a pioneering technology company focused on innovative solutions in digital transformation and AI-driven business strategies. With over two decades of experience in technology leadership and entrepreneurship, Paul has consistently demonstrated expertise in helping organizations leverage cutting-edge technologies to drive operational efficiency and competitive advantage. His strategic insights and hands-on approach have positioned him as a respected thought leader in the intersection of technology, innovation, and business growth. [Justin Foster](https://www.linkedin.com/in/justinfoster/) Guest Co-Founder & CRO, Incept AI Justin Foster is the Co-Founder and Chief Revenue Officer of Incept AI, where he leads strategic growth and business development in the artificial intelligence and machine learning space. With a deep background in technology entrepreneurship, Justin has been instrumental in driving innovative AI solutions that help enterprises transform their operational capabilities and customer engagement strategies. His expertise spans AI commercialization, go-to-market strategies, and helping organizations leverage cutting-edge machine learning technologies to drive meaningful business outcomes. [Previous Episode EP4 Systems Thinking and Strategic AI Deployment](https://olakai.ai/podcast/systems-thinking-and-strategic-ai-deployment/) [Next Episode EP6 Fortune 500 AI Playbook](https://olakai.ai/podcast/fortune-500-ai-playbook/) ## Ready to measure your AI? See how Olakai helps enterprises prove ROI, govern risk, and scale AI confidently. [Schedule a Demo](https://olakai.ai/schedule-a-demo/) ## More Episodes - [The AI Governance Roundtable: Who Owns It?](https://olakai.ai/podcast/ai-governance-roundtable-who-owns-it/) — Episode 11 · with Kate Ashworth Brash, Rob Saltrese - [The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) — Episode 10 · with Jason Smith, Rob Saltrese - [The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) — Episode 9 --- ## Enterprise Ai Roundtable Source: /podcast/enterprise-ai-roundtable [← Back to Olakai's Podcast](/podcast/) - Episode 9 - April 9, 2026 - 34 min [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fenterprise-ai-roundtable%2F)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fenterprise-ai-roundtable%2F&text=The+Roundtable+Discussion+on+Enterprise+AI) # The Roundtable Discussion on Enterprise AI *Join industry leaders as they dissect the real challenges of scaling AI in enterprise environments. Discover why 70% of AI initiatives stall at the pilot stage and learn the proven frameworks that separate successful deployments from costly failures. Explore critical insights on governance, team restructuring, and ROI measurement that executives need to drive meaningful AI transformation across their organizations.* Most enterprise AI initiatives never make it past pilot. In this inaugural Roundtable episode of Enterprise AI Unlocked, Paul Brzozowski leads a wide-ranging conversation with industry leaders on what separates organizations that scale AI from the 70% that stall. The discussion ranges across the practical questions every CIO, Head of AI, and CFO is wrestling with: where the bottleneck actually sits between proof-of-concept and production, why governance is becoming a deployment accelerator rather than a brake, how to think about team and workflow restructuring without losing institutional knowledge, and which ROI signals matter when the board asks “is any of this paying off.” It’s a candid look at the gap between AI ambition and AI in production — and the specific frameworks the panel has seen work in their own portfolios. Originally published on [Future of Agentic](https://futureofagentic.com/podcast/cmnscbany06w7p6gtva5z4xyp). ## Chapters 1. [0:00 Enterprise AI Roundtable Introduction](https://www.youtube.com/watch?v=1jqN9btjJEI&t=0) 2. [3:00 Current State of Enterprise AI](https://www.youtube.com/watch?v=1jqN9btjJEI&t=180) 3. [8:00 Implementation Challenges and Barriers](https://www.youtube.com/watch?v=1jqN9btjJEI&t=480) 4. [13:00 Strategic Opportunities and Use Cases](https://www.youtube.com/watch?v=1jqN9btjJEI&t=780) 5. [18:00 Organizational Considerations and Planning](https://www.youtube.com/watch?v=1jqN9btjJEI&t=1080) 6. [23:00 Future of Enterprise AI](https://www.youtube.com/watch?v=1jqN9btjJEI&t=1380) 7. [28:00 Key Takeaways and Recommendations](https://www.youtube.com/watch?v=1jqN9btjJEI&t=1680) [Previous Episode EP8 Your Job Isn’t to Buy AI. It’s to Buy Outcomes.](https://olakai.ai/podcast/your-job-isnt-to-buy-ai-its-to-buy-outcomes/) [Next Episode EP10 The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) ## Ready to measure your AI? See how Olakai helps enterprises prove ROI, govern risk, and scale AI confidently. [Schedule a Demo](https://olakai.ai/schedule-a-demo/) ## More Episodes - [The AI Governance Roundtable: Who Owns It?](https://olakai.ai/podcast/ai-governance-roundtable-who-owns-it/) — Episode 11 · with Kate Ashworth Brash, Rob Saltrese · 34 min - [The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) — Episode 10 · with Jason Smith, Rob Saltrese · 34 min - [Your Job Isn’t to Buy AI. It’s to Buy Outcomes.](https://olakai.ai/podcast/your-job-isnt-to-buy-ai-its-to-buy-outcomes/) — Episode 8 · with Anup Khera · 43 min --- ## Fortune 500 Ai Playbook Source: /podcast/fortune-500-ai-playbook [← Back to Olakai's Podcast](/podcast/) - Episode 6 - January 8, 2026 - 37 min [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Ffortune-500-ai-playbook%2F)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Ffortune-500-ai-playbook%2F&text=Fortune+500+AI+Playbook) # Fortune 500 AI Playbook *After working with dozens of Fortune 500 clients, Helena Ristov reveals why 70% of enterprise AI projects never escape pilot purgatory. Discover the exact prioritization framework that depoliticizes AI decisions, why strong prototypes fail at production scale, and the one CFO metric that actually matters. Learn how treating governance as design thinking—not compliance theater—helps companies scale AI faster by slowing down to build trust into their foundations.* Most enterprise AI initiatives never escape pilot purgatory. After working with dozens of Fortune 500 clients at Protiviti and Capgemini, Helena Ristov has identified the exact patterns separating companies that scale AI successfully from those stuck running endless proof-of-concepts. In this episode (recorded as 2025 came to a close), Helena breaks down the practitioner’s playbook for moving from AI experimentation to measurable business impact including why strong prototypes fail at scale, how to prioritize competing use cases, and why governance should be treated as design thinking rather than compliance theater. Originally published on [Future of Agentic](https://futureofagentic.com/podcast/cmkmw1a6w000np6unaoc0nqgw). ## Chapters 1. [0:00 Introduction and Helena's Background](https://www.youtube.com/watch?v=wacsl5PJ1nw&t=0) 2. [3:15 AI Innovation Studios Validation Process](https://www.youtube.com/watch?v=wacsl5PJ1nw&t=195) 3. [8:42 Trust and Governance in Enterprise AI](https://www.youtube.com/watch?v=wacsl5PJ1nw&t=522) 4. [14:20 From Experimentation to Production Execution](https://www.youtube.com/watch?v=wacsl5PJ1nw&t=860) 5. [21:35 Measuring Value Through Net Operating Margin](https://www.youtube.com/watch?v=wacsl5PJ1nw&t=1295) 6. [26:50 Value vs Complexity Prioritization Framework](https://www.youtube.com/watch?v=wacsl5PJ1nw&t=1610) 7. [32:15 Governance as Design Thinking Enabler](https://www.youtube.com/watch?v=wacsl5PJ1nw&t=1935) 8. [38:40 Future of Enterprise AI Evolution](https://www.youtube.com/watch?v=wacsl5PJ1nw&t=2320) 9. [44:25 Assistive vs Agentic AI Deep Dive](https://www.youtube.com/watch?v=wacsl5PJ1nw&t=2665) 10. [50:10 Building Consensus Among Competing Stakeholders](https://www.youtube.com/watch?v=wacsl5PJ1nw&t=3010) 11. [54:30 Key Takeaways for AI Success](https://www.youtube.com/watch?v=wacsl5PJ1nw&t=3270) ## Guests & Hosts [Helena Ristov](https://www.linkedin.com/in/helena-ristov-a28a621/) Guest AI Innovation Studios and Gen AI Centers of Excellence Expert, Protiviti/Capgemini Helena Ristov is a distinguished leader in AI innovation and generative AI strategy, currently driving transformative initiatives as an AI Studios and Gen AI Centers of Excellence expert at Protiviti/Capgemini. With deep expertise in emerging technologies and strategic AI implementation, she helps organizations navigate complex digital transformation challenges and unlock the potential of cutting-edge artificial intelligence solutions. Helena is recognized for her thought leadership in bridging technical capabilities with business value, guiding enterprises in developing sophisticated AI strategies that drive meaningful operational and competitive advantages. [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Host Founding Team Member, Olakai Paul Brzozowski is a seasoned technology and product leader with extensive experience in building innovative digital solutions. As a founding team member at Olakai, he brings deep expertise in strategic product development and emerging technology implementation. With a track record of driving transformative digital initiatives, Paul is passionate about leveraging technology to solve complex business challenges and create meaningful user experiences. [Previous Episode EP5 Breaking Through Voice AI Accuracy Barriers](https://olakai.ai/podcast/breaking-through-voice-ai-accuracy-barriers/) [Next Episode EP7 Why AI Adoption Keeps Failing Despite Leadership Priority](https://olakai.ai/podcast/why-ai-adoption-keeps-failing-despite-leadership-priority/) ## Ready to measure your AI? See how Olakai helps enterprises prove ROI, govern risk, and scale AI confidently. [Schedule a Demo](https://olakai.ai/schedule-a-demo/) ## More Episodes - [The AI Governance Roundtable: Who Owns It?](https://olakai.ai/podcast/ai-governance-roundtable-who-owns-it/) — Episode 11 · with Kate Ashworth Brash, Rob Saltrese - [The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) — Episode 10 · with Jason Smith, Rob Saltrese - [The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) — Episode 9 --- ## Strategic Ai Deployment For Private Equity And Venture Capital Source: /podcast/strategic-ai-deployment-for-private-equity-and-venture-capital [← Back to Olakai's Podcast](/podcast/) - Episode 3 - December 1, 2025 - 37 min [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fstrategic-ai-deployment-for-private-equity-and-venture-capital%2F)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fstrategic-ai-deployment-for-private-equity-and-venture-capital%2F&text=Strategic+AI+Deployment+for+Private+Equity+and+Venture+Capital) # Strategic AI Deployment for Private Equity and Venture Capital *Serial CEO Meshach Amuah-Foster has deployed AI across 20+ companies under PE/VC pressure—including putting AI agents directly in front of customers at Kayako before it was trendy. Learn his Use Case Matrix Framework for prioritizing initiatives, why “technically impressive” AI features often fail, and how to get board buy-in by presenting multiple options with impact analysis. Plus: why shadow AI creates hidden risks in enterprise environments.* Most people deploy AI once and call themselves experts. Meshach Amuah-Foster has deployed AI multiple times across multiple companies in multiple high-stakes environments – and lived to tell about it. Serial CEO with 20+ years driving SaaS revenue growth at Vimeo, Kayako, ESW Capital portfolio companies, and Singletrack. He put AI agents directly in front of customers at Kayako (before everyone was doing it). He deployed AI-driven tools for sell-side bankers. He’s overseen AI strategies across 12 portfolio companies simultaneously, each at different maturity levels. When you’ve done it that many times under PE/VC pressure, you see patterns others miss. Originally published on [Future of Agentic](https://futureofagentic.com/podcast/cminc1xfz0000p6dkvz8f45ff). ## Chapters 1. [0:00 Serial CEO's AI Deployment Experience](https://www.youtube.com/watch?v=eYOWhpNouSw&t=0) 2. [3:15 Patterns Across Multiple AI Deployments](https://www.youtube.com/watch?v=eYOWhpNouSw&t=195) 3. [6:42 Kayako Story: Customer-Facing AI Agents](https://www.youtube.com/watch?v=eYOWhpNouSw&t=402) 4. [11:28 Managing 12 Portfolio Companies](https://www.youtube.com/watch?v=eYOWhpNouSw&t=688) 5. [16:55 Experimentation to Execution Framework](https://www.youtube.com/watch?v=eYOWhpNouSw&t=1015) 6. [21:33 Metrics That Actually Matter](https://www.youtube.com/watch?v=eYOWhpNouSw&t=1293) 7. [27:10 Prioritization Under Pressure](https://www.youtube.com/watch?v=eYOWhpNouSw&t=1630) 8. [32:45 Governance, Risk, and Control](https://www.youtube.com/watch?v=eYOWhpNouSw&t=1965) 9. [36:20 Future Outlook for Enterprise AI](https://www.youtube.com/watch?v=eYOWhpNouSw&t=2180) 10. [41:15 Sales Qualification Framework for AI](https://www.youtube.com/watch?v=eYOWhpNouSw&t=2475) 11. [45:30 AI in PE/VC Environments](https://www.youtube.com/watch?v=eYOWhpNouSw&t=2730) 12. [50:18 AI as Revenue Play](https://www.youtube.com/watch?v=eYOWhpNouSw&t=3018) ## Guests & Hosts [Meshach Amuah-Foster](https://www.linkedin.com/in/meshachamuah-fuster/) Guest Serial CEO, Singletrack Meshach Amuah-Foster is a serial entrepreneur and technology executive with a proven track record of building and scaling innovative companies in the tech ecosystem. As the CEO of Singletrack, he brings deep expertise in strategic leadership and technology development, having successfully launched and grown multiple ventures across different sectors. Meshach is recognized for his forward-thinking approach to business strategy and his ability to drive transformative technological solutions that create meaningful impact. [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski/) Host Founding Team Member, Olakai Paul Brzozowski is a seasoned technology and product leader with extensive experience in building innovative digital solutions. As a Founding Team Member at Olakai, he brings strategic insight and technical expertise to developing cutting-edge products at the intersection of AI and business transformation. With a proven track record of driving entrepreneurial ventures and leveraging emerging technologies, Paul is recognized for his ability to translate complex technical challenges into scalable, user-centric solutions. [Previous Episode EP2 AI Agents Revolutionizing Cybersecurity Operations](https://olakai.ai/podcast/ai-agents-revolutionizing-cybersecurity-operations/) [Next Episode EP4 Systems Thinking and Strategic AI Deployment](https://olakai.ai/podcast/systems-thinking-and-strategic-ai-deployment/) ## Ready to measure your AI? See how Olakai helps enterprises prove ROI, govern risk, and scale AI confidently. [Schedule a Demo](https://olakai.ai/schedule-a-demo/) ## More Episodes - [The AI Governance Roundtable: Who Owns It?](https://olakai.ai/podcast/ai-governance-roundtable-who-owns-it/) — Episode 11 · with Kate Ashworth Brash, Rob Saltrese - [The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) — Episode 10 · with Jason Smith, Rob Saltrese - [The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) — Episode 9 --- ## Systems Thinking And Strategic Ai Deployment Source: /podcast/systems-thinking-and-strategic-ai-deployment [← Back to Olakai's Podcast](/podcast/) - Episode 4 - December 18, 2025 - 56 min [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fsystems-thinking-and-strategic-ai-deployment%2F)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fsystems-thinking-and-strategic-ai-deployment%2F&text=Systems+Thinking+and+Strategic+AI+Deployment) # Systems Thinking and Strategic AI Deployment *Former Marine Corps officer David Wood reveals why 90% of AI projects stall between pilot and production—and it’s not a technology problem. Drawing from 45 years of systems engineering, Wood shares military frameworks for AI deployment: the production readiness test, triage methodology for investment decisions, and governance as rules of engagement. Learn when AI should have authority versus advise, and the one metric that actually predicts enterprise AI success.* Most AI projects fail. Not because the technology isn’t ready – but because organizations treat AI like a tool instead of a system. David Wood brings 45 years of systems thinking to the enterprise AI conversation, and his perspective cuts through the noise: AI is a technology, but AI deployment is a human challenge. Former Marine Corps officer with a master’s in systems engineering. 25 years selling enterprise technology. Now running Gladwood LLC, helping organizations understand human performance using the Hartman Value Profile while advising AI companies like [Neuroscale AI](https://neuroscale.ai) on strategy. When you’ve spent decades studying what makes complex systems succeed or fail, the answer is always the same: the human factor. Originally published on [Future of Agentic](https://futureofagentic.com/podcast/cmjboxdej0000p6z7ivkcq41o). ## Chapters 1. [0:00 Introduction: 45 Years Studying Complex Systems](https://www.youtube.com/watch?v=HXPV56GTKE4&t=0) 2. [3:42 From Marine Corps to Enterprise Technology](https://www.youtube.com/watch?v=HXPV56GTKE4&t=222) 3. [8:15 The Systems Engineering Lens on AI Deployment](https://www.youtube.com/watch?v=HXPV56GTKE4&t=495) 4. [14:20 Why AI Stalls Between Pilot and Production](https://www.youtube.com/watch?v=HXPV56GTKE4&t=860) 5. [19:45 The One Metric That Actually Matters](https://www.youtube.com/watch?v=HXPV56GTKE4&t=1185) 6. [25:10 Military Triage for AI Investment](https://www.youtube.com/watch?v=HXPV56GTKE4&t=1510) 7. [31:35 Governance as Rules of Engagement](https://www.youtube.com/watch?v=HXPV56GTKE4&t=1895) 8. [37:50 From Cute Assistant to Reliable Co-Worker](https://www.youtube.com/watch?v=HXPV56GTKE4&t=2270) 9. [44:15 Three Military Decision-Making Frameworks](https://www.youtube.com/watch?v=HXPV56GTKE4&t=2655) 10. [52:30 When Should AI Have Authority vs Advise](https://www.youtube.com/watch?v=HXPV56GTKE4&t=3150) 11. [58:40 The Systems Challenge Requiring Military-Grade Discipline](https://www.youtube.com/watch?v=HXPV56GTKE4&t=3520) ## Guests & Hosts [David Wood](https://www.linkedin.com/in/savoyie/) Guest Former Marine Corps Officer, Systems Engineering Expert, Gladwood LLC David Wood is a seasoned systems engineering professional with a distinguished background as a former Marine Corps Officer, bringing rigorous strategic leadership and technical expertise to complex technological challenges. As the founder of Gladwood LLC, he leverages his military and engineering experience to help organizations optimize their technological systems and operational strategies. With a proven track record of translating technical complexity into actionable insights, David is a respected consultant who bridges the gap between advanced engineering principles and practical business implementation. [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski/) Host Founding Team Member, Olakai Paul Brzozowski is a seasoned technology executive and founding team member at Olakai, where he drives strategic innovation in product development and business growth. With extensive experience in emerging technologies and entrepreneurial ventures, Paul brings deep expertise in scaling startups and developing transformative digital solutions. His background spans multiple technology sectors, with a proven track record of building high-performance teams and delivering cutting-edge technological products. [Previous Episode EP3 Strategic AI Deployment for Private Equity and Venture Capital](https://olakai.ai/podcast/strategic-ai-deployment-for-private-equity-and-venture-capital/) [Next Episode EP5 Breaking Through Voice AI Accuracy Barriers](https://olakai.ai/podcast/breaking-through-voice-ai-accuracy-barriers/) ## Ready to measure your AI? See how Olakai helps enterprises prove ROI, govern risk, and scale AI confidently. [Schedule a Demo](https://olakai.ai/schedule-a-demo/) ## More Episodes - [The AI Governance Roundtable: Who Owns It?](https://olakai.ai/podcast/ai-governance-roundtable-who-owns-it/) — Episode 11 · with Kate Ashworth Brash, Rob Saltrese - [The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) — Episode 10 · with Jason Smith, Rob Saltrese - [The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) — Episode 9 --- ## The Mythos Reckoning Source: /podcast/the-mythos-reckoning [← Back to Olakai's Podcast](/podcast/) - Episode 10 - May 4, 2026 - 34 min [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fthe-mythos-reckoning%2F)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fthe-mythos-reckoning%2F&text=The+Mythos+Reckoning) # The Mythos Reckoning *Anthropic’s Claude Mythos breach within days of launch triggered a White House intervention and quietly re-priced cyber risk across governments and insurance markets. Industry leaders dissect why this represents a governance reckoning for every enterprise running AI—from the 99% of boards lacking AI literacy to meet fiduciary duty, to shadow AI exposure mapping and vendor chain ownership challenges that most organizations haven’t addressed.* Anthropic gated Claude Mythos behind 50 hand-picked partners. Within days it was breached. This week the White House blocked further expansion. The Mythos story is no longer a security headline. It is a governance reckoning. Paul Brzozowski sits down with Jason Smith, AI Lead EMEA at Publicis Groupe, and Rob Saltrese, Co-Founder and COO of Lyra Labs, to unpack what Mythos actually means for every enterprise running an AI strategy right now. Originally published on [Future of Agentic](https://futureofagentic.com/podcast/cmorub0h80maop6ekox84uyxs). ## Chapters 1. [0:00 Open and Welcome](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=0) 2. [1:30 The Mythos Timeline](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=90) 3. [3:30 Re-pricing of Cyber Risk](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=210) 4. [6:30 Chernobyl Moment and Unknown Unknowns](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=390) 5. [10:00 The FOMO Trap](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=600) 6. [10:30 The 99 Percent Problem](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=630) 7. [13:30 FOMO in the CTO Office](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=810) 8. [17:00 Foundation First Approach](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=1020) 9. [22:30 AI Audit and Shadow AI](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=1350) 10. [26:00 Vendor Chain Reckoning](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=1560) 11. [26:30 AI Vendor Risk Ownership](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=1590) 12. [29:30 Lightning Round Advice](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=1770) 13. [31:30 Closing Thoughts and Takeaways](https://www.youtube.com/watch?v=7DmDjzVWSx8&t=1890) ## Guests & Hosts [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Host Co-founder, Olakai Paul Brzozowski is co-founder of Olakai, where he leads the development of AI-powered solutions designed to transform business operations. With a background in building scalable technology platforms, Paul brings deep expertise in artificial intelligence implementation and product strategy. He is passionate about helping organizations leverage AI to solve complex challenges and drive measurable business outcomes. [Jason Smith](https://www.linkedin.com/in/jasonsmith) Guest AI Lead EMEA, Publicis Groupe Jason Smith is the AI Lead for EMEA at Publicis Groupe, where he drives the adoption and implementation of artificial intelligence solutions across the region's marketing and communications services. With expertise in translating complex AI capabilities into practical business applications, he helps clients navigate digital transformation and leverage emerging technologies to enhance their marketing effectiveness. His work focuses on bridging the gap between cutting-edge AI innovation and real-world client needs across Europe, the Middle East, and Africa. [Rob Saltrese](https://www.linkedin.com/in/rob-saltrese-17263a2) Guest Co-Founder and COO, Lyra Labs Rob Saltrese is Co-Founder and Chief Operating Officer of Lyra Labs, where he leads operational strategy and scaling initiatives for the company's AI-driven platform. With a background in building and optimizing high-growth technology companies, Rob brings deep expertise in product development, team leadership, and business operations. He is passionate about advancing practical applications of AI technology and helping organizations leverage these tools for meaningful impact. [Previous Episode EP9 The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) [Next Episode EP11 The AI Governance Roundtable: Who Owns It?](https://olakai.ai/podcast/ai-governance-roundtable-who-owns-it/) ## Ready to measure your AI? See how Olakai helps enterprises prove ROI, govern risk, and scale AI confidently. [Schedule a Demo](https://olakai.ai/schedule-a-demo/) ## More Episodes - [The AI Governance Roundtable: Who Owns It?](https://olakai.ai/podcast/ai-governance-roundtable-who-owns-it/) — Episode 11 · with Kate Ashworth Brash, Rob Saltrese · 34 min - [The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) — Episode 9 · 34 min - [Your Job Isn’t to Buy AI. It’s to Buy Outcomes.](https://olakai.ai/podcast/your-job-isnt-to-buy-ai-its-to-buy-outcomes/) — Episode 8 · with Anup Khera · 43 min --- ## Why Ai Adoption Keeps Failing Despite Leadership Priority Source: /podcast/why-ai-adoption-keeps-failing-despite-leadership-priority [← Back to Olakai's Podcast](/podcast/) - Episode 7 - March 10, 2026 - 43 min [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fwhy-ai-adoption-keeps-failing-despite-leadership-priority%2F)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fwhy-ai-adoption-keeps-failing-despite-leadership-priority%2F&text=Why+AI+Adoption+Keeps+Failing+Despite+Leadership+Priority) # Why AI Adoption Keeps Failing Despite Leadership Priority *Despite AI being CEOs’ top priority, 56% of companies report zero revenue benefit and 95% of pilots fail to impact P&L. Rob Saltrese, who spent 20 years in recruitment before co-founding AI consultancy Lyra Labs, reveals why the technology isn’t the problem—the leadership-workforce gap is. Learn the workflow redesign most skip, why 12-month AI strategies backfire, and how shadow AI creates governance blind spots that sabotage enterprise adoption.* PwC surveyed 4,454 CEOs across 95 countries. AI is the number one boardroom priority. Yet 56% of companies report zero revenue or cost benefit from AI. MIT found 95% of pilots deliver no measurable P&L impact. And worker confidence in AI is collapsing even as adoption surges. The technology is not the problem. The gap between leadership and the workforce is. In this episode, host Paul Brzozowski sits down with Rob Saltrese, Co-Founder of Lyra Labs, to unpack why AI adoption keeps failing and what it actually takes to fix it. Rob spent over 20 years in recruitment and talent acquisition, giving him a front-row seat to how organizations actually work, hire, and resist change. He then pivoted into AI consulting and co-founded Lyra Labs to help enterprises bridge the gap between AI ambition and real business impact. Originally published on [Future of Agentic](https://futureofagentic.com/podcast/cmmkql6fo00qup6tpqege5iyt). ## Chapters 1. [0:00 Introduction and Rob's Background](https://www.youtube.com/watch?v=u1EjjEFU9iA&t=0) 2. [2:30 Career Pivot to AI Consulting](https://www.youtube.com/watch?v=u1EjjEFU9iA&t=150) 3. [6:00 Recruitment Experience Shaping AI Strategy](https://www.youtube.com/watch?v=u1EjjEFU9iA&t=360) 4. [10:00 Why AI Pilots Fail](https://www.youtube.com/watch?v=u1EjjEFU9iA&t=600) 5. [14:30 Defining Value Before Deployment](https://www.youtube.com/watch?v=u1EjjEFU9iA&t=870) 6. [18:00 Avoiding Long-Term AI Strategies](https://www.youtube.com/watch?v=u1EjjEFU9iA&t=1080) 7. [22:00 Shadow AI and Governance Challenges](https://www.youtube.com/watch?v=u1EjjEFU9iA&t=1320) 8. [26:30 The Real Year of AI Agents](https://www.youtube.com/watch?v=u1EjjEFU9iA&t=1590) 9. [30:00 AI Transforming Recruitment End-to-End](https://www.youtube.com/watch?v=u1EjjEFU9iA&t=1800) 10. [38:00 Building Lyra Labs](https://www.youtube.com/watch?v=u1EjjEFU9iA&t=2280) 11. [43:00 Closing Takeaways](https://www.youtube.com/watch?v=u1EjjEFU9iA&t=2580) ## Guests & Hosts [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski) Host Founding team member, Olakai Paul Brzozowski is a founding team member at Olakai, where he helps build innovative solutions in the AI and technology space. With experience in early-stage company development, he brings deep expertise in product strategy and scaling emerging technologies. Paul is passionate about leveraging AI to solve real-world problems and create meaningful impact. [Rob Saltrese](https://www.linkedin.com/in/rob-saltrese-17263a2/) Guest Co-Founder, Lyra Labs Rob Saltrese is Co-Founder of Lyra Labs, where he leads the development of advanced AI solutions for enterprise applications. With a background in building scalable technology platforms, Rob brings deep expertise in artificial intelligence, machine learning, and product strategy to help organizations navigate digital transformation. His work focuses on making cutting-edge AI accessible and practical for businesses of all sizes. [Previous Episode EP6 Fortune 500 AI Playbook](https://olakai.ai/podcast/fortune-500-ai-playbook/) [Next Episode EP8 Your Job Isn’t to Buy AI. It’s to Buy Outcomes.](https://olakai.ai/podcast/your-job-isnt-to-buy-ai-its-to-buy-outcomes/) ## Ready to measure your AI? See how Olakai helps enterprises prove ROI, govern risk, and scale AI confidently. [Schedule a Demo](https://olakai.ai/schedule-a-demo/) ## More Episodes - [The AI Governance Roundtable: Who Owns It?](https://olakai.ai/podcast/ai-governance-roundtable-who-owns-it/) — Episode 11 · with Kate Ashworth Brash, Rob Saltrese - [The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) — Episode 10 · with Jason Smith, Rob Saltrese - [The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) — Episode 9 --- ## Your Job Isnt To Buy Ai Its To Buy Outcomes Source: /podcast/your-job-isnt-to-buy-ai-its-to-buy-outcomes [← Back to Olakai's Podcast](/podcast/) - Episode 8 - March 24, 2026 - 43 min [](https://www.linkedin.com/sharing/share-offsite/?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fyour-job-isnt-to-buy-ai-its-to-buy-outcomes%2F)[](https://twitter.com/intent/tweet?url=https%3A%2F%2Folakai.ai%2Fpodcast%2Fyour-job-isnt-to-buy-ai-its-to-buy-outcomes%2F&text=Your+Job+Isn%26%238217%3Bt+to+Buy+AI.+It%26%238217%3Bs+to+Buy+Outcomes.) # Your Job Isn’t to Buy AI. It’s to Buy Outcomes. *Most AI tools in sales become shelfware within 90 days. Anup Khera, named Top 25 CRO to Follow in 2026, reveals why AI pilots fail and shares his 3-question prioritization filter for evaluating tools. Learn about the “distraction tax,” measuring AI’s real value beyond activity metrics, and building AI-native sales teams without losing human connection. Insights from scaling Attentive internationally and three major exits including ExactTarget’s $3B Salesforce acquisition.* Most AI tools in sales become shelfware within 90 days. In this episode, Paul Brzozowski sits down with Anup Khera, Chief Sales Officer at Onclusive, to unpack what it actually takes to build AI-native sales organizations that deliver results. Anup was just named one of the Top 25 CROs to Follow in 2026. He was voted UK’s \#1 Sales Leader in both 2022 and 2023. He built Attentive’s international business from employee \#1 outside North America to massive eight-figure growth on the path to $100M ARR in under three years. He has been part of three major exits including ExactTarget’s IPO and its close to $3B acquisition by Salesforce. Over 20 years scaling SaaS revenue engines across three continents. Now at Onclusive, Anup is reviewing the entire AI stack for sales and building the frameworks to make sellers AI-native without losing the human touch that makes great sellers great. Originally published on [Future of Agentic](https://futureofagentic.com/podcast/cmn4qr30101ngp60sefezm2mh). ## Chapters 1. [0:00 Introduction and Guest Background](https://www.youtube.com/watch?v=xqawhmussy8&t=0) 2. [3:30 Building Attentive International from Zero](https://www.youtube.com/watch?v=xqawhmussy8&t=210) 3. [12:00 Three Major Exits and Lessons](https://www.youtube.com/watch?v=xqawhmussy8&t=720) 4. [17:00 AI Experimentation to Success Hurdles](https://www.youtube.com/watch?v=xqawhmussy8&t=1020) 5. [22:00 Measuring AI's True Value](https://www.youtube.com/watch?v=xqawhmussy8&t=1320) 6. [25:00 Distraction Tax and Prioritization Framework](https://www.youtube.com/watch?v=xqawhmussy8&t=1500) 7. [29:00 AI Governance Without Bureaucracy](https://www.youtube.com/watch?v=xqawhmussy8&t=1740) 8. [34:00 AI's Real Impact Areas](https://www.youtube.com/watch?v=xqawhmussy8&t=2040) 9. [38:00 What AI-Native Sales Means](https://www.youtube.com/watch?v=xqawhmussy8&t=2280) 10. [41:00 Change Management for AI Adoption](https://www.youtube.com/watch?v=xqawhmussy8&t=2460) 11. [44:00 AI as Lever Not Strategy](https://www.youtube.com/watch?v=xqawhmussy8&t=2640) ## Guests & Hosts [Paul Brzozowski](https://www.linkedin.com/in/paulbrzozowski/) Host Olakai Paul Brzozowski is the founder and CEO of Olakai, where he leads the development of innovative solutions at the intersection of technology and business strategy. With deep expertise in building scalable platforms and driving digital transformation, Paul brings a strategic vision to solving complex challenges in the modern technology landscape. His work focuses on creating practical, impactful solutions that help organizations navigate rapid technological change. [Anup Khera](https://www.linkedin.com/in/anupkhera/) Guest Chief Sales Officer, Onclusive Anup Khera is Chief Sales Officer at Onclusive, a leading AI-powered communications intelligence platform that helps organizations measure and optimize their earned media impact. With extensive experience building and scaling sales organizations in the martech and communications technology space, Anup drives go-to-market strategy and revenue growth for enterprise clients. His expertise spans AI-driven analytics, customer success, and helping brands leverage data to inform their communications strategies. [Previous Episode EP7 Why AI Adoption Keeps Failing Despite Leadership Priority](https://olakai.ai/podcast/why-ai-adoption-keeps-failing-despite-leadership-priority/) [Next Episode EP9 The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) ## Ready to measure your AI? See how Olakai helps enterprises prove ROI, govern risk, and scale AI confidently. [Schedule a Demo](https://olakai.ai/schedule-a-demo/) ## More Episodes - [The AI Governance Roundtable: Who Owns It?](https://olakai.ai/podcast/ai-governance-roundtable-who-owns-it/) — Episode 11 · with Kate Ashworth Brash, Rob Saltrese - [The Mythos Reckoning](https://olakai.ai/podcast/the-mythos-reckoning/) — Episode 10 · with Jason Smith, Rob Saltrese - [The Roundtable Discussion on Enterprise AI](https://olakai.ai/podcast/enterprise-ai-roundtable/) — Episode 9 --- ## Pricing Source: /pricing Pricing # Make every AI dollar prove itself. Olakai measures, governs, and optimizes your AI investment across AI Coding Agents, Autonomous Agents and employee AI assistive apps. Deployed inside your own cloud by default, so your data never leaves your control. Product ### Olakai Starter #### Try Olakai, on us. No credit card, no catch. Choose the Olakai Assistive or Olakai Agentic edition at signup — one product, full platform, free for your first 4 seats. Free /forever · Up to 4 seats For teams who want real usage and ROI data before rolling out further. * * * - Full platform access for up to 4 seats - One edition: Olakai Assistive or Olakai Agentic - Real usage, adoption, and cost data from day one - Ask Kai AI Assistant for answers in plain language - Deploys in your cloud or ours - Upgrade anytime as your team grows [Start Using Olakai](https://app.olakai.ai/signup) Product ### [Olakai Assistive](https://olakai.ai/assistive-iq/) #### Govern every AI your employees use, and prove what it’s worth. Full visibility into every interaction across ChatGPT, Claude, MS Copilot, and 800+ AI tools. Compliance, Shadow AI control, and real adoption ROI in one view. $5 /mo/employee For enterprises rolling AI out across the workforce that need to govern it and know what’s actually working. * * * - Every employee AI interaction - Shadow AI detection across 800+ tools - Data risk, PII, and policy enforcement (DLP) - Adoption and efficiency by team, role, and region - Business impact and time saved, in dollars - Idle license optimization, tool consolidation - Ask Kai AI Assistant for answers in plain language - Deploys in your cloud or ours [Start Using Olakai](https://app.olakai.ai/signup) Product ### [Olakai Agentic](https://olakai.ai/coding-iq/) #### AI tokens are your most expensive asset. Prove they pay. Budget, forecast, and control your AI coding tools and agents. See the spend, prove the return, maximize AI ROI across Claude Code, Cursor, Copilot, and Codex. $25 /mo/developer For leaders who manage AI budget and need to prove their coding tools and agents are worth the bill. * * * - Budgets and forecasts that alert before you overrun - Optimize cost per merged PR, the receipt for every token spent - Model routing that cuts the bill, not the output - AI ROI in dollars, plus Agent IQ for autonomous agents - Control and govern every coding tool in one place - Ask Kai: where to cut, and what you’ll save - Deploys in your cloud or ours [Start Using Olakai](https://app.olakai.ai/signup) Product ### Olakai Enterprise #### Your whole AI program, proven and governed, in your own cloud. Everything in Olakai Assistive and Olakai Agentic together, running on your infrastructure, with the SLAs and audit-ready reporting your risk team needs. Custom · Unlimited users For regulated and large organizations that need the full picture, full control, and audit-ready proof. * * * - Private cloud, on-prem, or managed SaaS - Zero Olakai access to your data - Custom SLAs and dedicated support - Audit-ready compliance and risk reporting - Volume and multi-year pricing [Talk to an expert](/schedule-a-demo/) Compare Plans ## Every Tier, Every Feature Capability Olakai Starter Olakai Assistive Olakai Agentic Olakai Enterprise Assistive AI usage tracking ✓ (1 edition) ✓ — ✓ Shadow AI detection & DLP ✓ (1 edition) ✓ — ✓ Custom KPIs & OLA Index — ✓ — ✓ Coding IQ (AI coding tool analytics) ✓ (1 edition) — ✓ ✓ Agent IQ (autonomous agent analytics) ✓ (1 edition) — ✓ ✓ AI spend governance (budgets, forecasting, alerts) — — ✓ ✓ Ask Kai (conversational insights) ✓ ✓ ✓ ✓ Seat cap 4 Unlimited Unlimited Unlimited On-prem deployment — — — ✓ Custom SLAs & dedicated support — — — ✓ Audit-ready compliance reporting — — — ✓ --- ## Resources Source: /resources RESOURCES # AI Enterprise Intelligence. Enterprise-grade insights, strategies, and templates to scale AI responsibly and measurably. ## Resources ### Filter Resources ### Resource Type ### Topic ### STAY INFORMED #### Get Updates for In-Depth Resource Knowledge [Subscribe](#) By subscribing you are agreeing to our Privacy Policy - [Shadow AI: The Hidden Risk and the Opportunity](https://olakai.ai/blog/resources/shadow-ai-the-hidden-risk-and-the-opportunity/) — 46 · 78% of employees use AI tools not approved by their employer. This nine-page white paper gives CISOs, CFOs, and boards a framework to address the data exposure risk and capture the adoption signal. - [Proving AI ROI: The Enterprise Measurement Playbook](https://olakai.ai/blog/resources/proving-ai-roi-the-enterprise-measurement-playbook/) — 46 · Only 6% of enterprises achieve meaningful EBIT impact from AI. This nine-page white paper gives CFOs and Heads of AI the framework to close the measurement gap and move from visibility to scale. - [Kai Data Sheet](https://olakai.ai/blog/resources/kai-data-sheet/) — 45 · Kai synthesizes AI analytics across Coding IQ, Assistive IQ, and Agent IQ — answering board-level questions in seconds. Three-page data sheet covering Kai capabilities and example queries. - [Agent IQ Data Sheet](https://olakai.ai/blog/resources/agent-iq-data-sheet/) — 45 · Cost and performance visibility into autonomous AI workflows — measure what each agent run costs, what it produces, and whether it is worth scaling. - [Assistive IQ Data Sheet](https://olakai.ai/blog/resources/assistive-iq-data-sheet/) — 45 · Measure ROI, detect shadow AI, and enforce data policy across your entire assistive AI portfolio. Three-page data sheet covering Assistive IQ capabilities and key metrics. - [Coding IQ Data Sheet](https://olakai.ai/blog/resources/coding-iq-data-sheet/) — 45 · Measure what AI coding tools actually return — cycle time, cost per PR, and team-level ROI. Three-page data sheet covering Coding IQ capabilities and key metrics. --- ## Schedule A Demo Source: /schedule-a-demo # See your AI ROI ## Find the 10 to 30% you’re losing on AI A working session on your AI spend, tailored to your stack. No slide deck, no pitch. We map where your tokens are going across coding agents, copilots, and autonomous agents, and show you what you can preserve, in dollars. And with **Kai**, anyone on your team can ask “is this AI actually paying off?” and get a reasoned answer in seconds. What you will see: - Where your AI coding spend is going, and how to cap runaway tokens before the invoice arrives (Coding IQ) - Shadow AI across your org, plus real adoption and productivity from copilots and AI SaaS (Assistive IQ) - Whether your autonomous agents pay for themselves, by cost per execution (Agent IQ) - A plain-language answer to “is this AI paying off?”, plus board-ready ROI proof (Kai and dashboards) > “Olakai gives us the intelligence to measure agent performance, boost adoption, and unlock enterprise-wide productivity.” CIO · Fortune 500, Design Partner > “Claude Code went usage-based and our token bill 5x’d in a quarter. I had no idea who was burning tokens, let alone any way to put limits and controls in place.” VP of Engineering · Global software company > “We set a token budget late last year and blew past it by May. Everyone wanted AI, but no individual developer realized how much they were burning. Now we can see it by developer and model, and set budgets with alerts before we overrun again.” CFO · Fortune 500 financial services > “We finally have one view of every AI tool in the company, what it touches and where the risk is, before it becomes an incident.” CISO · Healthcare enterprise > “For the first time, we walked into the board with a number, not a hope. It turned ‘we think AI is helping’ into ‘here’s exactly what it returned.’” Chief AI Officer · Global manufacturer ### Request your personalized demo Your demo will be tailored to your environment and AI stack. " \* " indicates required fields Phone This field is for validation purposes and should be left unchanged. - First Name - Last Name - Business Email - Company - Role / Title --- ## Shadow Ai Source: /shadow-ai [Key Features](/platform/) \| [Measure ROI](/ai-roi/) · [Govern Risk](/ai-governance/) · [Agent IQ](/agent-iq/) · [Custom KPIs](/analytics-kpis/) · [Monitor AI](/complete-ai-monitoring/) · Shadow AI · [Integrations](/integrations/) · [Kai](/kai/) # Find and Control the AI You Don’t Know About Discover. Assess. Govern Unauthorized AI Usage. ## The Shadow AI Problem Your employees are already using AI—the question is whether you can see it. **Over 60% of enterprise AI usage happens outside IT’s visibility.** Employees adopt ChatGPT, Gemini, Claude, and dozens of other tools on their own, feeding them customer data, source code, and financial projections with zero oversight. Every unauthorized AI tool is a potential data leak, compliance violation, and security incident waiting to happen. And you can’t govern what you can’t see. **Invisible tools** Employees use browser-based AI tools that bypass your security stack entirely—no installs, no approvals, no visibility. * * * **Data leakage** Sensitive data flows into unvetted AI models with no DLP controls, training opt-outs, or retention policies. **Compliance exposure** Unauthorized AI usage violates data handling policies, creating regulatory risk you discover only during audits. * * * **Zero inventory** No one knows how many AI tools are in use, who’s using them, or what data they’re processing. ### The Challenge ### You can’t govern AI you can’t see. And right now, most of it is invisible. ![Olakai Assistive IQ shadow AI detection showing unapproved AI tools and sensitive data leaving the organization](/wp-content/uploads/2026/06/assistive-iq-shadow-ai-platform.webp) The Solution ## Shadow AI You Can See and Control Automatically discover every AI tool in use across your organization, assess risk in real time, and enforce governance policies—without blocking the innovation your teams need. ![Olakai shadow AI controls showing acceptable-use policies with approved, monitored, and blocked AI tools and risk scoring](/wp-content/uploads/2026/06/shadow-ai-acceptable-policies.webp) **Auto-Detection** Continuously scan for unauthorized AI tools across browsers, APIs, and integrations. Olakai discovers new AI services as employees adopt them—no manual inventory required. * * * **Risk Scoring** Every detected AI tool is automatically scored for data sensitivity, compliance exposure, and security risk. Prioritize remediation by actual impact, not guesswork. * * * **Approval Workflows** Route newly detected AI tools through approval workflows that match your organization’s risk tolerance. Approve, monitor, or block—with full audit trails for every decision. * * * **Usage Analytics** See exactly who is using which AI tools, how often, and what data they’re sharing. Identify high-risk usage patterns and enforce data handling policies automatically. ### Ready to see what AI your teams are really using? Stop guessing. Start governing. [Talk to an Expert](/schedule-a-demo/) --- ## Terms Of Service Source: /terms-of-service ## Terms of Service *Last updated: March 30, 2026* Welcome to Olakai! These Terms of Service govern your use of our web pages located at [https://olakai.ai/](https://olakai.ai/) and any related services provided by Olakai. Please read these Terms of Service carefully before using our Service. Your access to and use of the Service is conditioned on your acceptance of and compliance with these Terms. These Terms apply to all visitors, users and others who access or use the Service. By accessing or using the Service you agree to be bound by these Terms. If you disagree with any part of the terms then you may not access the Service. ### 1\. Accounts When you create an account with us (e.g., via Google SSO), you must provide us information that is accurate, complete, and current at all times. Failure to do so constitutes a breach of the Terms, which may result in immediate termination of your account on our Service. You are responsible for safeguarding the password or access method (e.g., your Google account) that you use to access the Service and for any activities or actions under your password/access method. You agree not to disclose your password to any third party. You must notify us immediately upon becoming aware of any breach of security or unauthorized use of your account. ### 2\. Use of Service You agree not to use the Service: - In any way that violates any applicable national or international law or regulation. - For the purpose of exploiting, harming, or attempting to exploit or harm minors in any way by exposing them to inappropriate content or otherwise. - To transmit, or procure the sending of, any advertising or promotional material, including any “junk mail,” “chain letter,” “spam,” or any other similar solicitation. - To impersonate or attempt to impersonate Olakai, an Olakai employee, another user, or any other person or entity. - In any way that infringes upon the rights of others, or in any way is illegal, threatening, fraudulent, or harmful, or in connection with any unlawful, illegal, fraudulent, or harmful purpose or activity. - To engage in any other conduct that restricts or inhibits anyone’s use or enjoyment of the Service, or which, as determined by us, may harm or offend Olakai or users of the Service or expose them to liability. ### 3\. Intellectual Property The Service and its original content (excluding Content provided by users), features and functionality are and will remain the exclusive property of Olakai and its licensors. The Service is protected by copyright, trademark, and other laws of both the United States and foreign countries. Our trademarks and trade dress may not be used in connection with any product or service without the prior written consent of Olakai. ### 4\. Termination We may terminate or suspend your account and bar access to the Service immediately, without prior notice or liability, under our sole discretion, for any reason whatsoever and without limitation, including but not limited to a breach of the Terms. If you wish to terminate your account, you may simply discontinue using the Service, or contact us to request account deletion. All provisions of the Terms which by their nature should survive termination shall survive termination, including, without limitation, ownership provisions, warranty disclaimers, indemnity and limitations of liability. ### 5\. Disclaimer of Warranties; Limitation of Liability Our Service is provided on an “AS IS” and “AS AVAILABLE” basis. Olakai makes no representations or warranties of any kind, express or implied, as to the operation of their services, or the information, content or materials included therein. You expressly agree that your use of these services, their content, and any services or items obtained from us is at your sole risk. Neither Olakai nor any person associated with Olakai makes any warranty or representation with respect to the completeness, security, reliability, quality, accuracy, or availability of the services. In no event will Olakai, its directors, employees, partners, agents, suppliers, or affiliates, be liable for any indirect, incidental, special, consequential or punitive damages, including without limitation, loss of profits, data, use, goodwill, or other intangible losses, resulting from your access to or use of or inability to access or use the Service. ### 6\. Governing Law These Terms shall be governed and construed in accordance with the laws of the State of California, USA, without regard to its conflict of law provisions. Our failure to enforce any right or provision of these Terms will not be considered a waiver of those rights. If any provision of these Terms is held to be invalid or unenforceable by a court, the remaining provisions of these Terms will remain in effect. These Terms constitute the entire agreement between us regarding our Service, and supersede and replace any prior agreements we might have had between us regarding the Service. ### 7\. Changes to Terms We reserve the right, at our sole discretion, to modify or replace these Terms at any time. If a revision is material we will provide at least 30 days’ notice prior to any new terms taking effect. What constitutes a material change will be determined at our sole discretion. By continuing to access or use our Service after any revisions become effective, you agree to be bound by the revised terms. If you do not agree to the new terms, you are no longer authorized to use the Service. ### 8\. Contact Us If you have any questions about these Terms, please contact us: - By email: \[redacted\] --- ## Trust Source: /trust Trust & Security # Enterprise-grade security, built in from day one Olakai is designed to meet the security and compliance requirements of enterprise organizations. This page documents our infrastructure, controls, and practices so your security and procurement teams have what they need. ### On This Page ## Compliance & Certifications Olakai maintains rigorous security practices across all infrastructure and operations, backed by an independent SOC 2 Type II examination. [![AICPA SOC 2 Type II Seal](/wp-content/uploads/2026/08/soc2-seal-color.png)](https://www.aicpa.org/soc4so) #### SOC 2 Type II SOC 2 Type II examination completed (report period March 1 – May 31, 2026; Trust Services Criteria: Security). Report available under NDA. #### Vendor Coverage All core infrastructure vendors — AWS, Stripe, GitHub, Okta, Sentry, and Upstash — maintain SOC 2 Type II or equivalent certifications. #### PCI DSS Payment processing is handled exclusively by Stripe (PCI DSS Level 1). Olakai does not store payment card data. ## Hosting & Infrastructure Olakai is hosted entirely on Amazon Web Services (AWS) in the `us-east-1` region, using isolated private networking throughout. - **Compute:** Containerized Next.js applications on AWS ECS Fargate with auto-scaling (2–6 instances based on CPU/memory thresholds) - **Database:** Amazon RDS PostgreSQL 16 in a private subnet with no direct internet access; Multi-AZ enabled in production for high availability - **Storage:** Amazon S3 with server-side encryption for all document storage - **CDN:** Amazon CloudFront for static asset and document delivery - **Email:** Amazon SES for transactional email - **Network:** VPC with public/private subnet isolation; database and application containers reside in private subnets, accessible only via internal routing ## Encryption All data is encrypted in transit and at rest using industry-standard algorithms. - Data in Transit — TLS 1.2+ on all external connections. HTTPS enforced on all endpoints with HTTP 80 redirecting to 443. - Data at Rest — AES-256 encryption via AWS RDS (database) and AWS S3 (file storage). AES-256-GCM for sensitive application fields. - Sessions & Credentials — HMAC-SHA256 signed JWTs in HTTP-only, Secure, SameSite cookies. Passwords stored using bcrypt one-way hashing. ## Access Control Olakai uses Role-Based Access Control (RBAC) with strict multi-tenant isolation enforced at every layer. #### Customer Roles - USEROwn data only - ANALYSTAccount-wide read access to analytics and dashboards - ADMINFull account management — users, billing, configuration #### Multi-Tenant Isolation Every database query is scoped by `accountId`. No customer can access another customer’s data. This is enforced at the application layer across all repositories, use cases, and server actions, supplemented by automated tests and query guards. #### Authentication Methods - **Web Application:** OAuth 2.0 / OIDC via Okta or Google, or email/password credentials - **SDKs:** API key authentication (`x-api-key` header) - **CLI:** OAuth 2.0 Device Flow (RFC 8628) with 30-day JWT tokens - **Enterprise Provisioning:** SCIM 2.0 with bearer token authentication - **Internal Operations:** Olakai staff use a separate MFA-protected console. Support sessions grant time-limited (4-hour) access via single-use tokens, fully audited with initiator identity, target account, and IP address. ## Network Security Our network architecture minimizes attack surface through strict subnet isolation and controlled egress. - **Public endpoints:** Only Application Load Balancers are internet-facing (HTTPS 443 only) - **Private subnet:** Application containers, scheduled tasks, and database have no inbound internet access - **Outbound traffic:** Routed through NAT Gateway; limited to required external services (AI providers, Stripe, SSO, monitoring) - **Rate Limiting:** Sliding window rate limiting on all API endpoints via Upstash Redis, with fail-open graceful degradation and alerting - **CORS:** Dashboard endpoints enforce same-origin policy; monitoring API allows cross-origin requests with API key required ## Data Privacy & Governance Customer data is never shared across tenants and AI providers process data via API only — no persistent storage at the provider level. - Prompt Privacy Mode — Optional per-account setting requiring explicit authorization for non-admin users to view prompt content. All access attempts are audit-logged. - AI Processing — AI providers (Anthropic, OpenAI, Google, Mistral) process data via API calls only — no persistent storage at the provider. All API calls use TLS 1.2+. - Acceptable Use Policies — Admins can define and enforce policies users must accept before using monitored AI applications. Acceptance records are permanently retained. ## Availability & Reliability Olakai is designed for high availability with automated scaling, redundancy, and graceful degradation. - Auto-Scaling — ECS scales from 2–6 instances based on CPU (60%) and memory (70%) thresholds. Continuous ALB health checks. - Database HA — Multi-AZ RDS deployment in production with automated failover. Daily automated backups retained for 7 days. - Monitoring — Sentry for real-time error tracking. CloudWatch for infrastructure and application logs. AI analytics fail-open if upstream services are unavailable. ## Audit & Monitoring Every significant action in the platform is logged with full attribution — who did what, when, and from where. - **Configuration Changes:** All changes to agents, workflows, KPIs, feature flags, and policies logged in the `ApplicationChangeLog` with user attribution and timestamps - **Support Access:** Every support agent login recorded with initiating user, target account, token ID, and client IP - **Login Tracking:** IP address and timestamp recorded for every user login - **Billing Events:** Stripe webhook events stored with idempotency keys for audit and deduplication - **Infrastructure Logs:** Application and scheduled job logs stored in Amazon CloudWatch ## Security Testing Olakai maintains a layered security testing program combining automated scanning with manual penetration testing. - Automated Scanning — Dependabot enabled across all repositories, continuously monitoring for known vulnerabilities. Security advisories trigger automated PRs reviewed and merged on a priority basis. - Internal Testing — Regular internal security assessments covering OWASP Top 10 vulnerabilities, authentication/authorization flows, multi-tenant isolation, and API security. - Third-Party Pen Testing — Independent penetration testing by qualified external security firms on a periodic basis, covering web application, APIs, and infrastructure. ## Incident Response Olakai maintains a documented incident response process ensuring timely detection, containment, and customer communication. - **Detection:** Real-time alerting via Sentry (application errors), CloudWatch (infrastructure anomalies), and Dependabot (dependency vulnerabilities) - **Classification:** Incidents classified by severity (Critical, High, Medium, Low) based on scope of impact, data sensitivity, and customer exposure - **Response:** Defined escalation path with designated incident leads responsible for containment, root cause analysis, and remediation - **Communication:** Affected customers notified promptly with incident details, impact assessment, and remediation steps taken - **Post-Incident Review:** All significant incidents undergo a post-mortem documenting root cause, timeline, corrective actions, and preventive measures ## Vendor Security All vendors are evaluated for security posture and held to compliance standards appropriate for their role in our stack. #### Infrastructure - **AWS** — Hosting, database, storage, CDN (SOC 2 Type II, ISO 27001, FedRAMP) - **Stripe** — Billing and payments (PCI DSS Level 1) - **Okta** — Enterprise SSO (SOC 2 Type II, ISO 27001) - **Upstash** — API rate limiting (SOC 2 Type II) #### Development & Monitoring - **GitHub** — Code repository, PR analytics (SOC 2 Type II) - **Sentry** — Error monitoring (SOC 2 Type II) #### AI Providers - **Anthropic, OpenAI, Google Cloud AI, Mistral, Perplexity** — API-only, minimal retention, no persistent data storage ### Questions about our security posture? Contact our team for NDA-protected documentation, pen test summaries, or to discuss your organization’s specific requirements. [Talk to Our Team](/schedule-a-demo/) --- ## Use Cases Source: /use-cases ## Use Cases # Enterprise AI Use Cases: Measure ROI, Govern Risk, Control Costs **Agentic and Assistive AI workflows are outpacing measurement.** AI Agents and Assistive AI tools now drive work across enterprise systems, yet most leaders lack visibility into where automation happens, how it performs, or what ROI it delivers. ### Use Cases ### Intelligence as the Engine of Productivity *Olakai captures every AI interaction across Agents, Assistive AI tools, and systems – then transforms it into actionable intelligence. Leaders get the insights they need to make AI accountable, measurable, and scalable. Each use case shows how to turn AI activity into proven ROI and productivity gains.* * * * ## Prove AI ROI **Boards demand ROI.** Finance wants AI tied to measurable business outcomes, not pilots and spend. Olakai proves it with before-and-after measurement — converting the time AI saves into dollars and headcount-equivalent capacity your CFO can defend. * * * ### What Olakai Does - Tracks efficiency gains, time saved, and cost savings per agentic workflow, bottom-up and verifiable - Benchmarks performance by persona, department, and enterprise, with trend visibility - Delivers board-grade reporting and ROI insights for CIOs and CFOs #### Benefits - Board-ready proof of agentic and AI-driven business impact - Optimized cost structures with clear reinvestment levers. - Strategic allocation of AI resources to the highest-yield areas. #### Risk of Inaction Budgets shrink, adoption slows, and AI investment loses executive support. [Prove AI ROI](https://olakai.ai/ai-roi/) * * * ## Control AI Costs **AI spend is now usage-based, and climbing.** As coding tools, copilots, and agents move to token-metered pricing, the bill grows every month. Olakai gives you per-team, per-provider, and per-developer visibility — plus budgets and forecasts that catch overruns before the invoice lands. * * * ### What Olakai Does - Tracks AI spend by team, provider, project, and developer across every tool - Forecasts month-end spend from your run-rate, with alerts before a budget overruns - Reconciles real provider bills, not just token estimates, for a defensible number - Flags idle licenses and wasted spend to reclaim #### Benefits - Spend you can forecast and cap, not just watch after the fact - Idle licenses reclaimed and wasted spend cut - A defensible cost-per-value number finance trusts #### Risk of Inaction Token costs climb unforecasted, idle licenses pile up, and finance finds out from the invoice. [Control AI Costs](https://olakai.ai/coding-iq/) * * * ## Agent Governance & Shadow AI **Adoption without governance is risk.** Agentic workflows now span GenAI tools and SaaS systems – yet unapproved Agents, unmanaged prompts, and hidden integrations create Shadow AI. Enterprises need visibility and control to turn this complexity into safe, governed automation. * * * ### What Olakai Does - Acts as a semantic firewall across Agents, LLMs, and SaaS environments - Uses SDK and API integrations to detect, log, and classify all AI and Agent activity - Enforces compliant adoption through adaptive, role-based policies - Applies AI Semantic Filters™ to monitor and remediate risky workflows in real time #### Benefits - Unified visibility across all agentic and AI usage - Continuous enforcement of enterprise policies without disrupting productivity - Audit-ready transparency for compliance, risk, and board reporting #### Risk of Inaction Shadow AI expands, Agent behavior goes unchecked, and governance collapses. [Control AI Governance](https://olakai.ai/ai-governance/) * * * Explore by role [For Heads of AI](/use-cases/head-of-ai/) · [For CFOs](/use-cases/cfo/) · [For CISOs](/use-cases/ciso/) · [For VPs of Engineering](/use-cases/vp-engineering/) Explore by industry [Olakai by industry →](/industries/) — Technology, Financial Services, Healthcare, Professional Services, Retail, and Manufacturing. ### Unlock the Power of Intelligent AI Today [Talk to an Expert](https://olakai.ai/schedule-a-demo/) --- ## Cfo Source: /use-cases/cfo For CFOs # Walk into any board meeting with auditable AI ROI. Usage-based AI pricing changed the math. Token bills are up 5 to 10x for some teams. Most finance teams cannot see where the spend is going, let alone prove what it returned. Olakai gives you the auditable, dollar-denominated proof across every AI investment — coding agents, copilots, and autonomous workflows — vendor-neutral by design. ![Olakai AI ROI dashboard showing value created, cost, and return by department](/wp-content/uploads/2026/06/ai-roi-dashboard.webp) ## Every AI vendor sells you a tool. None of them tell you the truth. Vendor dashboards show you their adoption numbers. None of them warn you when the month-end run rate is headed for a 3x overrun. And none of them connect that activity to business outcomes. Olakai sits above every vendor and gives you the truth — spend by team, ROI by tool, and a budget alert before the invoice arrives. **Olakai sits above every vendor and gives you the truth: spend by team, ROI by tool, and a budget alert before the invoice arrives — vendor-neutral by design.** ## What you get with Olakai ### Spend control before the invoice arrives Token spend by provider, team, and developer — normalized across Anthropic, Cursor, OpenAI, and Copilot. Run-rate forecasting from a 7-day trailing average. Budget alerts that fire when the trajectory overruns the threshold, not after the limit is hit. ### Cost per outcome, not cost per token AI Equivalent Engineers, cost per PR by provider, cost per agent execution. The metrics that connect AI spend to business output. Across [copilots](https://olakai.ai/assistive-iq/), [AI coding tools](https://olakai.ai/coding-iq/), and [autonomous agents](https://olakai.ai/agent-iq/). ### Board-ready in 60 seconds Ask [Kai](https://olakai.ai/kai/) “what’s our AI ROI this quarter, broken down by department?” and get a reasoned, auditable answer in seconds. Click any number to see the math. Walk into the board meeting with confidence. ![Olakai Agentic budget forecasting showing token spend run-rate and month-end projection](/wp-content/uploads/2026/06/coding-iq-budget-sim.webp) ### Olakai Agentic — Budget forecasting ## See the run-rate before the invoice arrives. Olakai tracks token spend by provider, team, and developer — and projects month-end spend from a 7-day trailing average. When the trajectory crosses your configured threshold, you get an alert before the limit is hit. Not after. Teams that burned their annual AI coding budget in four months were watching monthly actuals. Run-rate forecasting watches the trajectory daily. - Token spend by provider, team, and developer — normalized across Anthropic, Cursor, OpenAI, and Copilot - Run-rate month-end forecast with confidence level and trajectory trend - Threshold alerts at 50%, 80%, and 100% of any configured budget — before the limit is hit, not after > Set spend thresholds by provider, team, or developer. Alerts fire at 50%, 80%, and 100% of any limit you configure — before you overrun, not after. ![Olakai Assistive license utilization dashboard showing active, idle, and shadow AI seats by team](/wp-content/uploads/2026/06/assistive-iq-licences-platform.webp) ### Olakai Assistive — License utilization ## Stop paying for seats nobody uses. Olakai Assistive tracks every AI tool your employees touch — approved copilots and shadow AI alike — and maps actual usage to the licenses you’re paying for. See which tools have idle seats, which teams are double-paying for overlapping tools, and where you can consolidate before the next renewal cycle. - Active vs idle seats across every AI copilot license — by team and by individual - Shadow AI cost exposure: tools employees expense out-of-pocket vs. company-approved licenses - Renewal recommendations: which tools to cut, consolidate, or expand based on actual usage ### Kai — Board-ready AI ROI ## Ask the board question. Get the board answer. Kai is the conversational layer across both Olakai Agentic and Olakai Assistive — ask it anything and get a reasoned, dollar-denominated answer in seconds. The CFO view: value returned per dollar invested, by department, by tool, and across the portfolio. Ask in plain English. Click any number to see the math. - “What’s our AI ROI this quarter, broken down by department?” - “Which AI tools are generating value, and which should we cut at renewal?” - “Are we going to overrun our coding AI budget before month-end?” ![Kai recommending an AI spend budget with reasoning and dollar impact](/wp-content/uploads/2026/06/kai-board-reco-platform.webp) ## One platform. Spend, ROI, and governance — in one number. Olakai sits above every vendor — copilots, coding agents, autonomous workflows — and gives you the unified financial picture no individual vendor can provide. The same platform your Head of AI uses to run the program gives you the board-ready ROI proof. Across [Olakai Agentic](https://olakai.ai/coding-iq/) and [Olakai Assistive](https://olakai.ai/assistive-iq/). *“What did we spend on AI last quarter, what did it return, and which team has the best ROI?”* ## Talk to an Expert. See how Olakai connects AI spend to the ROI numbers your board actually wants — tailored to your finance stack, no pitch. [Talk to an Expert](https://olakai.ai/schedule-a-demo/) --- ## Ciso Source: /use-cases/ciso For CISOs # Govern every AI tool. Including the ones you didn’t approve. Your employees use ChatGPT, Microsoft Copilot, Claude, Perplexity, Harvey, and 630+ other AI tools every day — most without going through procurement. Sensitive data is leaving your tenant in prompts you can’t see. And your engineering teams are running autonomous agents that make decisions, call APIs, and handle data without a human in the loop. Olakai gives you the visibility, controls, and audit trails to govern all of it — without slowing the business down. ![Olakai shadow AI detection view showing risk surfaces across the enterprise](/wp-content/uploads/2026/06/assistive-iq-shadow-ai-platform.webp) ## You can’t govern what you can’t see. SSO logs miss most of it. Expense reports are too late. Network DLP can’t read prompts. And none of it covers what your autonomous agents are doing between steps. The only way to govern AI is to see every tool, every prompt, and every agent execution — in real time — with controls in place before a bad action happens. **The only way to govern AI is to see every tool, every prompt, and every agent execution — in real time — with controls in place before a bad action happens. That is what Olakai does.** ## What you get with Olakai - [Discover every AI tool in use](https://olakai.ai/assistive-iq/) — The browser extension surfaces every AI tool an employee touches — even the ones nobody approved. Ranked by risk surface (PII, PHI, code, regulated departments). The shadow AI detection engine that sees what your SSO doesn’t. Over 630+ tools tracked. See Olakai Assistive. - Agent governance, end to end — Every autonomous agent execution logged: intent, steps taken, data accessed, outcome. Policy enforcement at the workflow level, not just the prompt. Audit-ready evidence for EU AI Act, SOC 2, and HIPAA — generated automatically, not assembled by hand after the fact. - [Audit-ready compliance](https://olakai.ai/kai/) — Every interaction logged. Every policy decision auditable. Reports built for the EU AI Act, SOC2, and your internal governance committee. Ask Kai “what shadow AI tools is legal using right now?” — and get an answer with the receipts attached. ![Olakai Assistive app-level view showing per-app analytics, governance status, and data risk indicators](/wp-content/uploads/2026/06/assistive-iq-app-detail-platform.webp) ### Olakai Assistive — App governance ## Governance and analytics, tool by tool. Click into any AI tool — ChatGPT, Copilot, Harvey, Perplexity, or any of the 630+ apps Olakai Assistive tracks — and see its risk level, governance status, data exposure, and usage in one view. Prompt quality scoring. PII and PHI detection. Licensed vs. shadow AI status. IT governance controls sitting right next to the usage data, so risk and analytics live in the same place. - Per-app risk classification: PII, PHI, code, and security data exposure - Licensed vs. shadow AI status, governance controls, and policy enforcement per tool - Prompt quality scoring and data risk indicators across every team ### Execution audit trail ## Trace any agent execution. Govern any decision. For every autonomous agent in your enterprise — across Salesforce Agentforce, Microsoft Copilot Studio, ServiceNow Now Assist, and your custom workflows — see exactly which inputs the agent received, which tools it called, and which decisions it made. The audit trail your governance committee needs. - Inputs received, tools called, decisions made, and outcomes — per execution - Policy enforcement at the workflow level: prevent bad actions before they happen - Audit-ready reports for EU AI Act, SOC 2, and HIPAA — generated automatically ![Agent IQ execution drill-down with full audit trail and decision log](/wp-content/uploads/2026/06/agent-iq-detailed-platform.webp) ## One governance view for every AI system. [Olakai Assistive](https://olakai.ai/assistive-iq/) governs employee-facing AI tools; [agent governance](https://olakai.ai/agent-iq/) is built into Olakai Agentic. Together, they give your security team the complete picture — every AI tool, every agent, every data risk, in one platform. And when the board asks “are we governing AI responsibly?” [Kai](https://olakai.ai/kai/) gives you the answer with the evidence. *“What shadow AI tools are in use across legal, finance, and HR — and which ones are handling PII?”* ## Talk to an Expert. See how Olakai gives you the audit trail, policy enforcement, and shadow AI visibility your governance program needs — tailored to your risk posture, no pitch. [Talk to an Expert](https://olakai.ai/schedule-a-demo/) --- ## Head Of Ai Source: /use-cases/head-of-ai For Heads of AI # Run your AI program like a business unit. You answer to the CFO on ROI, the CISO on risk, and the CEO on strategy. Each question pulls from the same data — but most AI programs have no single source of it. Olakai unifies Olakai Agentic and Olakai Assistive into one platform, and surfaces the answer to any question through Kai — in seconds, with the reasoning shown. ![Olakai board-ready AI program dashboard for the Head of AI](/wp-content/uploads/2026/06/kai-board-ready-platform.webp) ## Every team has AI. Nobody is running the program. Usage-based pricing has changed the calculus. Token bills are up 5 to 10x for some teams. Shadow AI is handling sensitive data without governance. Autonomous agents are making decisions without audit trails. Running an AI program now means owning all three problems simultaneously — and your vendors only show you their slice of it. **Running an AI program now means owning spend control, tool governance, and agent ROI simultaneously — and answering each board question with data that isn’t vendor-provided and isn’t three weeks old.** ## What you get with Olakai ### Every AI investment in one view Coding tool spend, copilot adoption, agent ROI — unified across every vendor and team. When the board asks “what did we get for our AI budget this year?” you have the number, with the math behind it. When a token bill doubles month over month, you know which team before the invoice lands. Across [Olakai Agentic](https://olakai.ai/coding-iq/) and [Olakai Assistive](https://olakai.ai/assistive-iq/). ### Answer the board, the CFO, and the CISO from one place Ask [Kai](https://olakai.ai/kai/) any question about your AI program — ROI, risk, adoption, cost, cycle time — and get a reasoned, data-backed answer in seconds. No reports to build. No analyst in the middle. ### Decide what to scale, fix, or retire See which tools produce AI Equivalent Engineers worth scaling. See which seats nobody is using. See which agent workflows earn their cost per execution and which ones don’t. Every renewal decision backed by data, not vendor anecdote. ### Olakai Agentic — Spend visibility ## See what every team is spending on AI. Before the invoice. Olakai pulls token spend from Anthropic, Cursor, OpenAI, and Copilot — normalized by provider, team, and developer. Run-rate forecasting projects month-end costs from 7-day trailing averages, so an overrun shows up as a trajectory problem before it becomes an invoice surprise. Set limits across teams and providers. Get alerts before they’re hit. - Token spend by provider, team, and developer — normalized across every AI coding tool - Run-rate forecast with month-end projection and confidence level - Budget limits by team, provider, or developer — alerts fire at 50%, 80%, and 100% ![Olakai Agentic budgets and run-rate forecasting showing AI spend by day and month-end projection](/wp-content/uploads/2026/06/coding-iq-budgets-platform.webp) ### Olakai Assistive — Program visibility ## Every AI tool your employees use. Including the ones you didn’t approve. Olakai Assistive surfaces every AI tool in use across the org — from ChatGPT and Microsoft Copilot to the 630+ tools employees expense without procurement approval. Track adoption by department, measure ROI by tool, and see which licenses are sitting idle. The unified view of your assistive AI program that no single vendor can give you. - AI value created and time saved, across every approved and shadow AI tool - Adoption by department, team, and role — who’s getting real value vs. clicking through - 630+ tools tracked, shadow AI surfaces highlighted with risk classification ![Olakai Assistive overview dashboard with AI value created, time saved, total cost, and data risks](/wp-content/uploads/2026/06/assistive-iq-overview-platform.webp) ### Kai — Across both products ## Ask your AI program a question. Get a reasoned answer. Kai is the conversational layer across both Olakai Agentic and Olakai Assistive — ask it anything about your AI program and get cross-cutting answers in seconds, with full reasoning shown. The way the Head of AI actually wants to interact with their program: in plain English, in real time, with audit-ready math behind every number. - “What’s our total AI spend this month, and which team is driving it?” - “Which AI tools are producing the most value per dollar spent?” - “Are we forecast to overrun any coding budget before month-end?” ![Kai answering a cross-product AI ROI question with reasoning](/wp-content/uploads/2026/06/kai-engineering-team-roi-platform.webp) ## The whole AI program. One platform. One question. Olakai connects [Olakai Agentic](https://olakai.ai/coding-iq/) and [Olakai Assistive](https://olakai.ai/assistive-iq/) into one unified picture — and surfaces the answer to any question through [Kai](https://olakai.ai/kai/) in seconds, with the reasoning shown. The CFO gets the ROI view. The CISO gets the governance view. You get the program view. *“What’s our AI ROI across coding tools, copilots, and agents this quarter — broken down by department?”* ## Talk to an Expert. See how Olakai gives you one governed view across every AI tool, agent, and coding assistant your teams run — tailored to your stack, no pitch. [Talk to an Expert](https://olakai.ai/schedule-a-demo/) --- ## Vp Engineering Source: /use-cases/vp-engineering For VPs of Engineering # Prove every AI coding tool’s ROI. Standardize with confidence. Your engineers use Claude Code, Cursor, GitHub Copilot, and more — often all at once. Anthropic went usage-based. Token bills are up 5x in a quarter for some teams. Procurement asks “is this paying off?” You need two answers: whether AI makes your engineers faster, and whether the cost is under control. Olakai gives you both from one platform. ![Olakai Agentic home dashboard with AI code ratio, review time, and developer adoption](/wp-content/uploads/2026/06/coding-iq-home-platform.webp) ## Every coding tool sells you adoption. None of them sell you outcome. Cursor shows you Cursor adoption. Anthropic shows you Claude Code spend. GitHub shows you Copilot acceptance rates. None of them connect that activity to shipping velocity — or warn you when a team’s token burn is trending 3x over the monthly budget. Olakai connects them all: cost and velocity, end to end, from every PR. **You need two answers: whether AI makes your engineers faster, and whether the cost is under control. Most tools give you one. Olakai gives you both — from the same platform.** ## What you get with Olakai - [Cycle time impact, by tool](https://olakai.ai/coding-iq/) — Cycle time delta for AI-assisted vs non-AI PRs, broken out by every provider you run. Coding time, review time, total cycle. The exact metric procurement asks you for — backed by your real GitHub data. See Olakai Agentic. - Adoption coaching — Every developer in your org segmented into Power, Casual, New, or Idle cohorts — with the data you need to coach the casual users, reclaim the idle licenses, and standardize on what your power users have already chosen. - Budget control before the overrun — Token spend by provider, team, and developer — normalized across Anthropic, Cursor, OpenAI, Copilot, and Windsurf. Run-rate forecasting based on 7-day trailing averages. Budget alerts that fire before limits are hit. Cost per PR by provider so you know which tool earns its seat. ### Olakai Agentic — Budget forecasting ## See spend before the invoice arrives. Olakai tracks token spend by provider, team, and developer — normalized across Anthropic, Cursor, OpenAI, and Copilot in one view. Run-rate forecasting projects month-end overruns from 7-day trailing averages. Budget alerts fire before limits are hit, not after. The cost control your CFO is now asking for — from the same platform where you already measure velocity. - Token spend by provider, team, and developer — normalized across Anthropic, Cursor, OpenAI, and Copilot in one view - Run-rate month-end forecast from 7-day trailing averages — a trajectory, not a surprise - Budget alerts fire at 50%, 80%, and 100% of any limit you configure — before you overrun > Set budgets by provider, by team, or by individual developer. Alerts fire at 50%, 80%, and 100% of any limit you configure. ![Olakai Agentic budgets and run-rate forecasting showing AI spend by day and month-end projection](/wp-content/uploads/2026/06/coding-iq-budgets-platform.webp) ![Olakai Agentic Developers tab showing per-developer adoption cohorts and cost attribution](/wp-content/uploads/2026/06/coding-iq-developers-platform.webp) ### Olakai Agentic — Developer cohorts ## Find the idle licenses. Coach the casual users. Reward the power users. Olakai Agentic segments every developer in your org into one of four adoption cohorts — Power, Casual, New, or Idle — and shows you which tools they actually use, how often, and what their cycle time looks like compared to peers. Estimated and reconciled actual cost attributed per developer, side by side. The view that tells you exactly who to enable, who to train, and where to reclaim licenses before the next renewal. - Power (\>70% AI-assisted PRs), Casual (20–70%), New (first AI PR in 14 days), Idle (\<20%) - Per-developer cost: estimated vs. reconciled actual, side by side - Precise reclaim list: idle licenses, underused seats, and coaching opportunities ### PR Analysis ## See exactly how much faster AI-assisted PRs ship. Olakai Agentic reads PR data directly from your GitHub org and plots cycle time for AI-assisted vs non-AI PRs side by side — across every repo, every team, every provider. AI-assisted PRs are typically 25–40% faster. Olakai Agentic tells you whether yours are. - Coding time, review time, and total cycle time — AI-assisted vs. non-AI, side by side - PR volume and AI code ratio: % of merged lines that came from AI-assisted PRs - Provider breakdown: which tool produces faster PRs on your actual repos ![Olakai Agentic PR Analysis with cycle time delta between AI-assisted and non-AI PRs](/wp-content/uploads/2026/04/coding-iq-pr-analysis.webp) ## Velocity and cost, on the same platform as governance. Olakai Agentic shares a platform with [Olakai Assistive](https://olakai.ai/assistive-iq/) — so the velocity and spend story rolls up into the same enterprise AI ROI view your CFO, CISO, and Head of AI are already looking at. And [Kai](https://olakai.ai/kai/) connects the dots so you can answer “is AI making us faster and is it under control?” from a single question. *“Are we forecast to overrun any coding budget this month, and which team is driving it?”* ## Talk to an Expert. See how Olakai connects your AI coding tool spend to real engineering ROI — cycle time, adoption, cost per PR — tailored to your stack, no pitch. [Talk to an Expert](https://olakai.ai/schedule-a-demo/)