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Author: Xavier Casanova

  • What 100+ AI Agent Deployments Taught Us About Proving ROI

    What 100+ AI Agent Deployments Taught Us About Proving ROI

    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, 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. The era of “trust us, AI is helping” is over.

    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 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, 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, 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 breaks down the methodology, and Future of Agentic’s success KPI library offers specific metrics by use case.

    Ready to see what your AI agents are actually worth? 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

    What ServiceNow’s $8B AI Acquisition Spree Tells Us About the Future of Enterprise AI

    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, 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, 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. 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 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 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.

    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. 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. Schedule a demo to see how Olakai delivers it without platform lock-in.

  • Shadow AI: The Enterprise Risk Hiding in Plain Sight

    Shadow AI: The Enterprise Risk Hiding in Plain Sight

    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, 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, 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 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. 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. 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. 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? Schedule a demo to find out.

  • Your Most Important 2026 Resolution: Measure Your AI

    Your Most Important 2026 Resolution: Measure Your AI

    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, 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 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 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. Your organization already measures what matters: revenue, costs, customer satisfaction, employee productivity, error rates, cycle times. Effective AI measurement 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? Schedule a demo to see how Olakai measures AI ROI across your entire organization.

  • AI Predictions for 2026: What Enterprise Leaders Need to Know

    AI Predictions for 2026: What Enterprise Leaders Need to Know

    As 2025 draws to a close, enterprise AI has reached an inflection point. Chatbots and copilots proved the technology works. 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, 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.

    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 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 now. Start with visibility (what AI is running?), then add controls (who can do what?), then measurement (is it working?). Our 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 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 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? Schedule a demo to see how Olakai helps enterprises measure ROI, govern risk, and scale AI with confidence.

  • How AI Agents Are Revolutionizing Cybersecurity

    How AI Agents Are Revolutionizing Cybersecurity

    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, 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 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, 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.

    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.

    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 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 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 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? 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

    AI in Finance: 5 Use Cases Every CFO Should Know

    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, 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, 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.

    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 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 handles the routine work, freeing finance teams to focus on analysis, planning, and decision support. According to Fortune’s CFO survey, 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? 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

    AI Risk Heatmap: Matching Governance to Business Value

    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, 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: 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, 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 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 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 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 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? 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

    The Evolution of Enterprise AI: From Prediction to Action

    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, 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, 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 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. According to Gartner, 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 for compliance. Without these, agents become liabilities rather than assets. Knowing how to measure AI ROI 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.

    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 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? 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

    7 AI Use Cases for Customer Success Teams

    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, 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 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, 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, 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, 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.

    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 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 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? Schedule a demo to see how Olakai helps you measure the impact of CS AI initiatives and govern them responsibly.