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  • 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.

  • From AI Experimentation to Business Impact

    From AI Experimentation to Business Impact

    In 2024, a global manufacturing company ran 23 AI pilots across its business units. The pilots worked. Chatbots answered questions. Document processors extracted data. Forecasting models outperformed spreadsheets. Leadership declared success and… nothing changed. A year later, exactly zero of those pilots had reached production. The company had proven AI could work; they hadn’t proven it could deliver value at scale.

    This story repeats across enterprises worldwide. According to research from MIT, 95% of AI pilots fail to deliver measurable business value—most never scale beyond the experimental phase. In 2025, the average enterprise scrapped 46% of AI pilots before they ever reached production. Global investment in generative AI solutions more than tripled to roughly $37 billion in 2025, yet 74% of companies still struggle to scale their AI initiatives into real business impact.

    Why do some organizations break through while others remain trapped in what we call “pilot purgatory”? The answer isn’t technology—it’s how organizations approach the transition from experiment to production.

    The Pilot Trap

    Most enterprises approach AI the same way. They identify an interesting use case, assemble a team, run a pilot, declare success, and then stall. The pilot proved the technology works, but scaling requires investment, change management, and governance that organizations aren’t prepared to provide. The result is a graveyard of successful experiments that never delivered business value.

    The symptoms are unmistakable. Organizations have multiple proof-of-concepts but zero production deployments. Data science teams are enthusiastic while business stakeholders remain skeptical. There’s a “we did AI” checkbox without measurable outcomes to show for it. Security and compliance concerns block production deployment. No one owns the responsibility for scaling successful pilots into real operations.

    The ISG State of Enterprise AI Adoption Report 2025 quantifies this problem: only about one in four AI initiatives actually deliver their expected ROI, and fewer than 20% have been fully scaled across the enterprise. In a survey of 120,000+ enterprise respondents, only 8.6% of companies report having AI agents deployed in production, while 63.7% report no formalized AI initiative at all. The gap between AI adoption and AI value remains stubbornly wide.

    What Successful Organizations Do Differently

    1. Start with Business Problems, Not Technology

    Failed AI initiatives typically start with “We should use AI for something.” Successful ones start with “This business problem costs us $X million annually—can AI help?” The difference matters enormously.

    Business problems come with budgets and executive sponsors who have a stake in the outcome. Clear problems have measurable success criteria that everyone can agree on. Stakeholders are invested in solutions rather than experiments. When a pilot solves a quantified problem, the case for scaling writes itself.

    Before launching any AI initiative, quantify the business problem. If you can’t put a dollar figure on it, you probably don’t have the executive sponsorship needed to scale. The successful implementations follow what researchers call a counterintuitive split: 10% on algorithms, 20% on infrastructure, 70% on people and process. That last 70% requires business ownership, not just technical enthusiasm.

    2. Build Governance from Day One

    Pilots often skip governance because “we’ll figure it out later.” But when “later” arrives, the lack of logging, security controls, and compliance documentation blocks production deployment. Security teams rightfully refuse to approve systems they can’t audit. Compliance finds gaps that require redesign. What should have been a straightforward scale becomes a rebuild.

    Organizations that scale AI treat governance as a feature, not an afterthought. Security and compliance stakeholders are involved from the start. Logging and monitoring are built into the MVP, not bolted on later. Data handling practices are documented before production. Risk assessments happen during design, not after deployment.

    For a comprehensive framework on what governance should include, our CISO AI Governance Checklist provides the full requirements. The key insight: governance built early accelerates production; governance added late delays or blocks it entirely.

    3. Measure Outcomes, Not Activity

    “The chatbot handled 10,000 conversations” sounds impressive—but did it reduce support costs? Improve customer satisfaction? Drive revenue? Activity metrics are easy to collect but often misleading. Outcome metrics are harder to define but actually prove value.

    Activity metrics track what the AI does: chatbot conversations, AI completions, agent tasks, documents processed. Outcome metrics track what the business gains: cost savings, time saved, revenue impact, error reduction, customer satisfaction changes. The difference between “we processed 50,000 invoices” and “we reduced invoice processing costs by 60%” is the difference between a pilot that stalls and one that scales.

    Define outcome metrics before the pilot begins. Establish baselines so you can prove improvement. Our AI ROI measurement framework provides a structured approach to connecting AI activity to business outcomes.

    4. Plan for Change Management

    AI that changes workflows requires people to change behavior. Without change management, even great technology fails. Employees resist tools they don’t understand. Workarounds emerge that bypass the AI entirely. Training gaps lead to misuse and disappointment. The technology works but the adoption doesn’t.

    Successful organizations plan for adoption from the beginning. They involve end users in design and testing, building tools that fit how people actually work. They create training and documentation before launch, not after complaints pile up. They measure adoption rates and address resistance directly rather than hoping it resolves itself. They iterate based on user feedback, treating the human side of deployment as seriously as the technical side.

    Include change management in your pilot plan. Budget time and resources for training and adoption. A pilot that users love has a path to production; a pilot that users ignore doesn’t.

    5. Create a Path to Production

    Many pilots succeed in isolation but have no path to production. They’re built on different infrastructure than production systems. They lack integrations with enterprise tools. They don’t meet security and compliance requirements that production demands. No one owns ongoing maintenance once the pilot team moves on.

    Organizations that scale design pilots with production in mind from day one. They use production-like infrastructure from the start so there’s no migration surprise. They build integrations that will scale rather than proof-of-concept workarounds. They document operational requirements—monitoring, alerting, failover, maintenance. They assign ownership for post-pilot operation before the pilot begins.

    Before starting a pilot, define what production deployment looks like. Build the pilot to minimize the gap between demo and deployment.

    The Scaling Playbook

    When you’re ready to scale a successful pilot, the process typically unfolds in four phases.

    During the first two weeks, validate value rigorously. Review pilot metrics against the success criteria you defined at the start. Calculate ROI and payback period with real numbers, not projections. Document lessons learned and risks discovered during the pilot. Secure executive sponsorship for scaling—if you can’t get it now, your pilot hasn’t proven enough value.

    Weeks three through six are about preparing for production. Address security and compliance gaps identified during the pilot. Build production-grade infrastructure that can handle real load. Create monitoring and alerting that will catch problems before users do. Develop training materials that help users succeed with the new tools.

    Weeks seven through ten involve limited rollout. Deploy to a subset of users and monitor closely for issues. Gather feedback and iterate quickly. Validate that production metrics match pilot expectations. This phase catches problems at manageable scale before they become enterprise-wide crises.

    From week eleven onward, execute full deployment. Expand to all users with confidence built from the limited rollout. Complete training and communication across the organization. Establish ongoing monitoring that will support the system long-term. Report outcomes to stakeholders to demonstrate value and build support for future initiatives.

    Signs You’re Ready to Scale

    You’re ready to move from pilot to production when several conditions align. Metrics prove value with clear ROI and documented baselines—not projections, but measured results. Governance is in place with security and compliance sign-off on the production deployment. Infrastructure is ready with production-grade systems that can support scale. Ownership is clear with a team accountable for operation and improvement. Users are engaged, ideally asking for broader access rather than avoiding the pilot. Executive sponsorship is confirmed with leadership committed to the investment required.

    Signs You’re Not Ready

    Don’t scale if you can’t quantify the business value delivered—enthusiasm isn’t evidence. Don’t scale if security or compliance have outstanding concerns that haven’t been addressed. Don’t scale if users aren’t adopting the pilot solution—production won’t fix adoption problems. Don’t scale if no one owns ongoing operation—orphaned systems become liabilities. And don’t scale if you’re scaling to “prove AI works” rather than solve a business problem—that’s the path to expensive experimentation with no business impact.

    The Path Forward

    Moving from AI experimentation to business impact requires more than technology. It requires clear business problems with quantified value that justify investment. It requires governance that enables rather than blocks production deployment. It requires metrics that prove outcomes, not just activity. It requires change management that drives adoption. And it requires infrastructure that supports production scale.

    The enterprises that master this transition will compound their AI investments, building capability on capability. Those that don’t will keep running pilots—and keep wondering why AI isn’t delivering the transformation they were promised.

    The Future of Agentic use case library provides detailed examples of enterprise AI deployments that have successfully made this transition, with architecture patterns and governance frameworks you can adapt.

    Ready to scale AI with confidence? Schedule a demo to see how Olakai helps enterprises measure ROI, govern risk, and move from pilot to production.

  • AI Governance Checklist for CISOs

    AI Governance Checklist for CISOs

    AI is no longer an IT experiment—it’s an enterprise reality. Your employees are using AI tools (sanctioned or not), your vendors are embedding AI into their products, and your board is asking about AI strategy.

    For CISOs, this creates a challenge with no easy answers: How do you govern AI without blocking innovation? How do you protect data without slowing business? How do you maintain compliance when the technology moves faster than regulations?

    The stakes are high. According to the 2025 CSA AI Security Report, only about a quarter of organizations have comprehensive AI security governance in place—the remainder rely on partial guidelines or policies still under development. Meanwhile, 100% of organizations plan to incorporate generative AI, and Gartner predicts over 100 million employees will interact with AI by 2026. The gap between AI adoption and AI governance represents real risk.

    This checklist provides a structured framework for evaluating and improving your organization’s AI governance maturity.

    How to Use This Checklist

    For each question, score your organization from 0 (not in place—no capability or process exists), to 1 (partial—some capability exists but gaps remain), to 2 (mature—fully implemented and operational). Add scores within each category to identify strengths and weaknesses.

    Category 1: Visibility

    Can you see what AI is doing in your organization?

    Audit and Logging: Can we audit every agent decision? Do we have centralized logging for all AI interactions, including inputs, outputs, and decisions made? The ability to answer “what did this system do and why” is foundational to everything else in governance.

    Complete inventory: Do we have a complete inventory of all AI agents and tools in use—including shadow AI that employees may be using without approval? According to research, 78% of CISOs believe AI is affecting cybersecurity, but 45% admit they’re still not ready to address the problem. You can’t govern what you can’t see.

    Data lineage: Can we trace data lineage for any agent interaction? Do we know what data sources each agent accessed and what data it produced? This becomes critical during incidents and audits.

    Sensitive data access: Do we know which agents access sensitive data sources? Is there a registry mapping agents to the data they can access? Sensitive data exposure ranks as the leading AI security concern among survey respondents.

    Shadow AI detection: Can we detect shadow AI usage—unapproved tools that employees are using? Do we monitor for this actively? Given that most organizations lack formal AI risk management programs, shadow AI often operates completely below radar.

    Category 1 Score: ___ / 10

    Category 2: Control

    Can you control what AI does and who can change it?

    Deployment authority: Who can deploy agents? Who can change their prompts? Is there clear ownership and authorization for AI deployments? Without clear authority, agents proliferate without oversight.

    Role-based access: Do we have role-based access control (RBAC) for agent capabilities? Can we limit what different agents can do based on sensitivity? Not every agent needs access to every system.

    Approval workflows: Is there an approval process for new agents entering production? Do security, legal, and compliance review before deployment? The SANS report highlights a concerning lack of security team involvement in governing GenAI—many believe they should play a role but few organizations have formal processes.

    Policy enforcement: Can we enforce policies programmatically—not just through guidelines? Are guardrails built into the infrastructure? Policies that rely solely on human compliance will fail.

    Security testing: Do we test agents for security vulnerabilities before deployment? Do we check for prompt injection, jailbreaking, and data leakage risks? According to research, 62% of AI-generated code is either incorrect or contains a security vulnerability.

    Category 2 Score: ___ / 10

    Category 3: Data

    Is sensitive data protected when AI accesses it?

    Data source mapping: Which data sources can each agent access? Is there a clear registry of permissions and restrictions? Data access should be explicit, not assumed.

    PII protection: Do we have PII detection and masking in place? Can we prevent agents from exposing personally identifiable information? This is table stakes for any customer-facing AI.

    Regulatory compliance: Are we compliant with GDPR, CCPA, and other data regulations for AI-processed data? Have we verified this with legal? As of mid-2025, state legislatures had introduced some 260 AI-related bills during the 2025 legislative sessions—the regulatory landscape is rapidly evolving.

    Data retention: Do we have data retention policies for agent interactions? Do we know how long logs are kept and when they’re deleted? Compliance requirements vary by jurisdiction and data type.

    Right to deletion: Can we fully delete user data on request (right to be forgotten)? Does this include data in AI training sets and logs? This is a legal requirement in many jurisdictions and technically complex to implement.

    Category 3 Score: ___ / 10

    Category 4: Incident Response

    Can you respond when something goes wrong?

    Rollback capability: How do we roll back a rogue or compromised agent? Can we quickly revert to a previous version or disable an agent entirely? The faster you can respond, the smaller the impact.

    Incident runbooks: Do we have runbooks for common AI incidents—data leaks, hallucinations, prompt injection attacks, model compromise? AI introduces failure modes that traditional security runbooks don’t cover.

    Kill switch: Can we disable an agent in less than 5 minutes? Is this tested regularly? When an agent is causing harm, every minute matters.

    On-call ownership: Who is on-call for AI security incidents? Is there a clear escalation path and 24/7 coverage? AI systems don’t fail during business hours only.

    Post-mortems: Do we conduct post-mortems and share learnings after AI incidents? Is there a continuous improvement process? Learning from incidents prevents repetition.

    Category 4 Score: ___ / 10

    Category 5: Compliance and Audit

    Can you prove compliance to auditors and regulators?

    Audit readiness: Can we pass an AI audit today? If regulators asked to see our AI governance, could we demonstrate compliance? The CSA AI Controls Matrix provides 243 control objectives across 18 security domains—a useful benchmark.

    Immutable logs: Do we have immutable logs for sensitive operations? Can we prove logs haven’t been tampered with? Immutability is critical for legal and regulatory purposes.

    Policy documentation: Are AI governance policies documented and communicated? Do employees know what’s expected? Documentation is the foundation of demonstrable compliance.

    Compliance metrics: Do we measure and report Governance Compliance Rate? Can we show the percentage of AI interactions that comply with policies? Metrics make governance tangible.

    Board visibility: Is AI governance represented at the board level? Do executives understand AI risk exposure? AI risk is business risk and belongs in board conversations.

    Category 5 Score: ___ / 10

    Scoring Interpretation

    Total Score Maturity Level Recommended Action
    0-10 Foundational Start with visibility: establish inventory and basic logging before adding controls
    11-25 Developing Fill critical gaps: prioritize based on risk—data protection and incident response are typically highest priority
    26-40 Established Optimize and scale: strengthen existing capabilities and prepare for audit
    41-50 Advanced Lead: share practices, influence industry standards, and continue innovation

    Priority Actions by Risk Level

    If you’re processing customer PII: Prioritize PII detection and masking, comprehensive logging, RBAC, right to deletion capability, and regulatory compliance verification. Data protection failures have immediate regulatory and reputational consequences.

    If you’re in a regulated industry: Prioritize immutable audit logs, policy documentation, compliance metrics, approval workflows, and audit readiness. Key compliance pathways include mappings to the EU AI Act, NIST AI 600-1, ISO 42001, and BSI AIC4 Catalogue.

    If you’re scaling AI rapidly: Prioritize complete inventory, shadow AI detection, programmatic policy enforcement, kill switch capability, and incident runbooks. Speed without governance creates technical and compliance debt.

    If you’re just starting: Prioritize basic logging, agent inventory, clear ownership, simple approval process, and documentation. Foundation comes before sophistication.

    The AI Risk Heatmap

    Not all AI use cases carry equal risk. Prioritize governance based on both business value and risk sensitivity—a concept we explore in depth in our AI risk heatmap framework.

    High Value, High Risk (Govern Tightly): Customer support agents with PII access, financial data analysis agents, contract review and drafting, and HR policy chatbots need RBAC, PII protection, comprehensive logging, human-in-the-loop review, and regular audits.

    High Value, Medium Risk (Govern Moderately): Code assistants and copilots, sales research assistants, and AI meeting note takers need zero data retention agreements, code review requirements, consent mechanisms, and approved vendor lists.

    Medium Value, Low Risk (Govern Lightly): Internal knowledge assistants and content drafting tools need basic logging, user feedback loops, and source citation requirements.

    Getting Started

    If you scored below 25, focus on these immediate actions.

    First, conduct an AI inventory. Survey departments, review expense reports, analyze network traffic. You can’t govern what you can’t see, and the gap between what security teams believe is deployed and what’s actually in use is often substantial.

    Second, establish basic logging. Ensure all production AI agents have logging enabled. Centralize logs where possible. This creates the audit trail everything else depends on.

    Third, define ownership. Assign clear owners for AI governance. Create an AI governance committee if needed. Without ownership, governance becomes everyone’s problem and no one’s priority.

    Fourth, document policies. Write down acceptable use guidelines. Communicate them to all employees. Documentation transforms implicit expectations into enforceable standards.

    Fifth, plan for incidents. Create basic runbooks for data leaks, hallucinations, and unauthorized access. Incident response planned in advance is dramatically more effective than improvisation under pressure.

    For measuring the business impact of your governance investments, see our AI ROI measurement framework.

    The Bottom Line

    AI governance isn’t about blocking innovation—it’s about enabling it responsibly. The organizations that build strong governance foundations now will scale AI with confidence, while others will hit walls of compliance violations, security incidents, and audit failures.

    This checklist is a starting point. The goal isn’t perfection; it’s continuous improvement toward a governance posture that matches your AI ambitions. The Future of Agentic guide to agent characteristics provides additional context on what makes AI systems increasingly autonomous—and why governance becomes more critical as autonomy increases.

    Ready to improve your AI governance maturity? Schedule a demo to see how Olakai provides the visibility, controls, and compliance tools CISOs need.

  • How to Measure AI ROI: A Framework for Enterprise Leaders

    How to Measure AI ROI: A Framework for Enterprise Leaders

    “What’s the ROI on our AI investments?”

    It’s the question every board asks, every CFO needs to answer, and every AI leader dreads. Despite billions invested in AI, most enterprises can’t answer it with confidence. Pilots proliferate, costs accumulate, and proof of value remains elusive.

    The scale of this measurement gap is striking. According to McKinsey’s 2025 State of AI report, 88% of organizations report regular AI use in at least one business function. But only 39% report EBIT impact at the enterprise level. Organizations are spending on AI; they’re struggling to prove it’s working. S&P Global data shows that 42% of companies abandoned most of their AI projects in 2025—up from just 17% the year prior—often citing cost and unclear value as the primary reasons.

    This guide provides a practical framework for measuring AI ROI—one that works whether you’re evaluating a single chatbot or an enterprise-wide AI program.

    Why AI ROI Measurement is Hard

    Before diving into the framework, it’s worth understanding why AI ROI is harder to measure than other technology investments.

    Benefits are often indirect. When AI helps an employee work faster, the benefit shows up as productivity—not a direct cost reduction. Unless you’re tracking time saved and connecting it to business outcomes, the value remains invisible. The employee doesn’t disappear; they just do more. Proving the “more” matters requires discipline most organizations lack.

    Costs are distributed across model APIs, infrastructure, development time, training, change management, and ongoing maintenance. Without careful tracking, it’s easy to undercount the total investment. The API costs are visible; the engineering time spent debugging prompt failures often isn’t.

    Baselines are missing. How long did invoice processing take before AI? What was the error rate? Without pre-AI measurements, you can’t calculate improvement. Yet most organizations deploy AI first and ask measurement questions later—by which point the baseline is lost forever.

    Attribution is complex. When a sales team closes more deals, is it the AI-powered lead scoring, the new sales methodology, the improved economy, or the new sales leader? Isolating AI’s contribution requires experimental rigor that few commercial settings permit.

    The AI ROI Framework

    Effective AI ROI measurement requires four components working together: quantifying value created, capturing total cost of ownership, calculating ROI with appropriate rigor, and benchmarking against meaningful comparisons.

    1. Value Created

    Quantify the benefits AI delivers across four categories.

    Time Saved: Calculate hours saved multiplied by fully-loaded labor cost. If an AI agent saves an accountant 5 hours per week on invoice processing, and that accountant costs $75/hour fully loaded, that’s $375/week or approximately $19,500/year in value. The formula is straightforward: hours saved per week times weeks per year times fully-loaded hourly cost. According to research, AI adoption is delivering 26-55% productivity gains for enterprises that measure carefully—but only if that saved time converts to productive work.

    Errors Avoided: Calculate the cost of errors prevented. If AI reduces invoice processing errors from 5% to 0.5%, and each error costs $150 to correct, and you process 1,000 invoices monthly, that’s $675/month or approximately $8,100/year in avoided rework. The formula: error rate reduction times monthly volume times cost per error times twelve months.

    Revenue Impact: For customer-facing AI, measure impact on conversion, upsell, or retention. If AI-powered lead qualification increases conversion from 3% to 4%, and average deal size is $50,000, and you process 100 leads monthly, that’s an additional $50,000/month or $600,000/year. This is where the biggest ROI potential lies—but also where attribution gets most difficult.

    Risk Reduction: For governance and compliance use cases, calculate the expected value of risk reduction. If AI reduces the probability of a $1M compliance violation from 5% to 1%, the expected value is $40,000 annually. Risk reduction is real value, even though it’s harder to celebrate than revenue gains.

    2. Total Cost of Ownership

    Capture all costs associated with the AI investment—not just the obvious ones.

    Direct costs include model API costs (per-token or per-call charges from AI providers), infrastructure (cloud compute, storage, networking), and software licenses (AI platforms, tools, orchestration software). These are the easy ones to track because they show up on invoices.

    Development costs include engineering time spent building, integrating, and testing; data preparation including cleaning, labeling, and pipeline development; and training and prompting work to fine-tune models and optimize outputs. These costs often get buried in general engineering budgets where they’re invisible to ROI calculations.

    Operational costs include maintenance (ongoing updates, monitoring, bug fixes), support (helpdesk and user support for AI tools), and change management (training, communication, adoption programs). Organizations consistently underestimate these ongoing costs.

    Hidden costs include governance overhead (compliance, audit, risk management), opportunity cost (what else could the team have built?), and technical debt (costs of workarounds and shortcuts that accumulate). These rarely appear in ROI models but determine whether AI investments compound or drain resources over time.

    3. ROI Calculation

    With value and cost quantified, calculate ROI using the formula: value created minus total costs, divided by total costs, times 100. For a more complete picture, also calculate payback period (months until cumulative value exceeds cumulative cost), net present value (present value of future benefits minus present value of costs), and internal rate of return (discount rate at which NPV equals zero).

    According to Gartner research, 45% of high AI maturity organizations keep initiatives in production for three years or more, compared to only 20% in low-maturity organizations. The difference isn’t luck—it’s rigorous measurement. IBM’s research found companies realize an average return of $3.50 for every $1 invested in AI, but that average masks wide variation between disciplined organizations and those hoping for magic.

    4. Benchmarking

    Context matters. Compare your metrics against pre-AI baseline (how did the process perform before AI?), industry benchmarks (how do similar organizations perform?), and alternative investments (what ROI could you get from other uses of capital?). Without benchmarks, even impressive-sounding numbers may represent underperformance.

    Key Metrics by Use Case

    Different AI use cases require different metrics. For customer support agents, track adoption rate (percentage of eligible users actively using the AI), task success rate (tasks completed without errors or escalation), cost per interaction (total cost divided by number of interactions), and user satisfaction (customer and employee ratings).

    For invoice processing, track data extraction accuracy (percentage of fields correctly extracted), touchless processing rate (invoices processed without human intervention), exception rate (invoices requiring human review), and cost per invoice (target: $2-6 versus $15-25 for manual processing).

    For sales research and lead qualification, track research completeness (required data points gathered), qualification accuracy (agreement with actual sales outcomes), time to completion (minutes from assignment to delivery), and intelligence freshness (average age of data sources).

    For governance and compliance, track policy compliance rate (interactions complying with policies), shadow AI detection rate (unauthorized usage identified), and audit pass rate (success rate on AI-related audits).

    Common Pitfalls

    Avoid these mistakes when measuring AI ROI.

    Counting activity, not outcomes: “The chatbot handled 10,000 conversations” sounds impressive—but did it actually resolve issues? Were customers satisfied? Did it reduce support costs? Activity metrics are easy to collect but often misleading. Focus on whether the activity produced the business outcome you wanted.

    Overestimating time saved: “The AI saves 30 minutes per task” only matters if that time converts to productive work. If employees fill saved time with low-value activities—or if the organization doesn’t capture the savings through higher output—the benefit is illusory. Organizations getting good results invest 70% of AI resources in people and processes, not just technology, ensuring that time savings translate to business outcomes.

    Ignoring maintenance costs: Pilot costs are easy to track; ongoing maintenance often gets lost in general IT budgets. Make sure you’re capturing the full lifecycle cost, including the engineering time spent fixing edge cases and handling failures.

    Missing the baseline: Without pre-AI measurements, you can’t prove improvement. Establish baselines before deploying AI, not after. This is the single most common and most fatal measurement mistake.

    Cherry-picking metrics: It’s tempting to highlight the metrics that look good and ignore the rest. Present a complete picture—including metrics that show room for improvement. Selective reporting destroys credibility when the full picture eventually emerges.

    Getting Started

    Ready to measure AI ROI? Begin by establishing baselines now—for any process you’re considering automating, measure current performance including time, cost, error rate, and volume before AI enters the picture.

    Define success metrics upfront. Before deploying AI, agree on what success looks like. What specific metrics will you track? Who owns them? How will you report? McKinsey found that CEO oversight of AI governance is the factor most correlated with higher self-reported bottom-line impact—especially at larger companies where executive attention ensures metrics connect to outcomes that matter.

    Instrument from day one. Build measurement into your AI deployment. Capture logs, track costs, and monitor outcomes from the start. Adding instrumentation after deployment is always harder than including it from the beginning.

    Review regularly. AI ROI isn’t a one-time calculation. Review monthly, adjust for learnings, and report to stakeholders quarterly. Gartner found that 63% of leaders from high-maturity organizations run financial analysis on risk factors, conduct ROI analysis, and concretely measure customer impact—that discipline separates them from the majority still struggling to prove value.

    Connect to business outcomes. Tie AI metrics to the numbers executives care about: revenue, margin, customer satisfaction, risk exposure. Technical metrics matter for optimization; business metrics matter for funding and support. The Future of Agentic guide to agent economics provides additional frameworks for connecting AI investment to business value.

    The Bottom Line

    Measuring AI ROI is harder than measuring other technology investments—but it’s not impossible. With clear frameworks, consistent measurement, and a focus on business outcomes rather than technical metrics, you can prove the value of AI investments and make informed decisions about where to invest next.

    BCG research shows only 4% of companies have achieved “cutting-edge” AI capabilities enterprise-wide, with an additional 22% starting to realize substantial gains. The 74% struggling to show tangible value despite widespread investment aren’t failing because AI doesn’t work—they’re failing because they can’t prove it works. Measurement is the differentiator.

    The enterprises that master AI ROI measurement will scale AI with confidence while others remain stuck in pilot purgatory.

    Need help measuring AI ROI across your organization? Schedule a demo to see how Olakai provides the visibility and analytics you need to prove AI value and govern AI risk.

  • Shadow AI: The Hidden Risk in Your Enterprise

    Shadow AI: The Hidden Risk in Your Enterprise

    Your employees are using AI tools you don’t know about. Right now. They’re pasting customer data into ChatGPT to draft emails. They’re uploading financial documents to AI summarizers. They’re using unapproved coding assistants that send your source code to third-party servers.

    And the numbers are staggering. According to a Gartner survey of cybersecurity leaders conducted in 2025, 69% of organizations suspect or have evidence that employees are using prohibited public GenAI tools. Microsoft’s research found that 71% of UK employees admitted to using unapproved AI tools at work—with 51% doing so at least once a week. This isn’t occasional experimentation; it’s a systematic shadow operation running parallel to your official technology stack.

    This is shadow AI—the enterprise AI equivalent of shadow IT—and it represents one of the most significant and underestimated risks facing organizations today.

    What is Shadow AI?

    Shadow AI refers to AI tools and services that exist outside your organization’s visibility and governance. They’re not approved by IT, security, or compliance teams. They’re not visible in your technology inventory. They’re not governed by your data protection policies. And they’re not monitored for security, compliance, or cost implications.

    Just as shadow IT emerged when employees started using Dropbox, Slack, and other cloud tools without IT approval, shadow AI is spreading as employees discover that AI makes their jobs easier—regardless of whether it’s sanctioned. According to the 2025 State of Shadow AI Report, the average enterprise hosts 1,200 unauthorized applications, and 86% of organizations are blind to AI data flows. Nearly half (47%) of people using generative AI platforms do so through personal accounts that companies aren’t overseeing.

    Why Shadow AI is Different from Shadow IT

    Shadow AI carries risks that go beyond traditional shadow IT in fundamental ways.

    Data goes out, not just in. When an employee uses unauthorized Dropbox, they might store company files externally—a risk, but a bounded one. When they use unauthorized AI, they actively send sensitive data to third-party models. That customer complaint they pasted into ChatGPT? It might be used to train the model, potentially surfacing in responses to competitors. According to Cisco’s 2025 study, 46% of organizations reported internal data leaks through generative AI—data that flowed out through employee prompts rather than traditional exfiltration.

    Prompts reveal more than files. The questions employees ask AI reveal context that raw data doesn’t. “Summarize this contract and identify terms unfavorable to us” tells the AI (and its operator) not just the contract contents, but your negotiating strategy and concerns. The prompt itself is intelligence.

    Answers drive decisions. When AI provides analysis or recommendations, employees act on them. An unauthorized AI tool giving bad financial advice, incorrect legal interpretation, or flawed technical guidance can lead to costly mistakes with no audit trail. And there’s no recourse when things go wrong.

    The attack surface is enormous. Prompt injection, jailbreaking, and other AI-specific attacks create new vectors. An employee who pastes customer data into a compromised AI tool might unknowingly expose that data to attackers who’ve manipulated the model.

    The Scope of the Problem

    If you think shadow AI isn’t happening in your organization, the statistics suggest otherwise. Gartner predicts that by 2030, more than 40% of enterprises will experience security or compliance incidents linked to unauthorized shadow AI. That’s not a distant future risk—it’s the trajectory we’re already on.

    The financial impact is real and immediate. According to IBM’s 2025 Cost of Data Breach Report, shadow AI incidents now account for 20% of all breaches and carry a cost premium: $4.63 million versus $3.96 million for standard breaches. AI-associated cases caused organizations more than $650,000 extra per breach. The gap between AI adoption and AI governance is where shadow AI thrives—and where the costs accumulate.

    Perhaps most concerning: 83% of organizations operate without basic controls to prevent data exposure to AI tools. The average company experiences 223 incidents per month of users sending sensitive data to AI applications—double the rate from a year ago. And 27% of organizations report that over 30% of their AI-processed data contains private information, including customer records, financial data, and trade secrets.

    Common Shadow AI Scenarios

    These aren’t hypothetical risks. They’re happening in organizations like yours, every day.

    The helpful marketer uses an AI writing tool to draft blog posts. She pastes competitor analysis, product roadmaps, and customer testimonials as context. The tool’s terms of service allow training on user inputs. Your competitive intelligence is now potentially in someone else’s model—or in their training data, waiting to surface in responses to your competitors.

    The efficient developer uses an unapproved coding assistant to speed up development. He pastes internal API documentation and proprietary algorithms for context. The code generated might include those patterns in ways that constitute IP leakage, and the original code may be used for model training.

    The overwhelmed HR manager uses an AI tool to help screen resumes and draft interview questions. She pastes candidate information, salary data, and performance review excerpts. She’s now exposed PII to an unapproved processor, potentially violating GDPR and internal policies—with no documentation of consent or processing basis.

    The pressured analyst uses an AI tool to summarize earnings calls and model scenarios. He pastes material non-public information into prompts. If that information surfaces elsewhere—or even if someone later discovers it was processed through an unauthorized channel—it could trigger SEC scrutiny.

    Why Traditional Controls Don’t Work

    The approaches that worked for shadow IT often fail for shadow AI.

    Blocking doesn’t scale. You can’t block every AI tool—new ones appear daily. Employees use personal devices. VPNs and proxies circumvent network controls. Small businesses face the highest risk, with 27% of employees in companies with 11-50 workers using unsanctioned tools. These organizations average 269 shadow AI tools per 1,000 employees while lacking the security resources to monitor them.

    Policies aren’t enough. Acceptable use policies help, but they rely on employees reading, understanding, and following them. When AI makes someone dramatically more productive, policy compliance becomes an afterthought. According to research, 90% of security leaders themselves report using unapproved AI tools at work—with 69% of CISOs incorporating them into daily workflows. If the people writing the policies aren’t following them, you have a systemic problem.

    Training has limits. Security awareness training can highlight risks, but it can’t prevent every incident. Employees under deadline pressure make expedient choices.

    A Better Approach: Discovery, Governance, and Alternatives

    Effective shadow AI management requires a multi-pronged approach that acknowledges human nature while protecting organizational interests.

    Discovery: See what’s happening. You can’t govern what you can’t see. Modern shadow AI discovery involves monitoring network traffic for AI tool usage patterns, analyzing browser extensions and desktop applications, surveying employees about tools they’re using, and reviewing expense reports and credit card statements for AI subscriptions. The goal isn’t surveillance—it’s visibility. You need to know what’s being used so you can make informed governance decisions.

    Risk assessment: Prioritize what matters. Not all shadow AI carries equal risk. Assess each discovered tool against data sensitivity (what data types are being processed?), regulatory exposure (does usage implicate GDPR, CCPA, HIPAA, or SOX?), vendor risk (what are the tool’s data handling practices?), and business impact (how critical is this tool to the workflow?). For a framework on matching governance to risk levels, see our AI risk heatmap approach.

    Provide sanctioned alternatives. Heavy-handed blocking drives shadow AI underground. Instead, provide approved alternatives that meet employee needs: deploy enterprise AI tools with proper data protection, negotiate data processing agreements with AI vendors, configure guardrails like PII detection and content filtering, and communicate what’s available and how to access it. When approved tools are easy to use and meet employee needs, shadow AI becomes less attractive.

    Continuous monitoring. Shadow AI isn’t a one-time problem to solve—it’s an ongoing challenge to manage. Establish regular discovery scans to identify new tools, usage monitoring for sanctioned tools, incident response procedures for policy violations, and feedback loops to understand why employees seek alternatives. According to Delinea’s 2025 report, 44% of organizations with AI usage struggle with business units deploying AI solutions without involving IT and security teams. That gap needs ongoing attention.

    The Role of Governance

    Ultimately, shadow AI is a symptom of governance gaps. Organizations that struggle with shadow AI often lack visibility (no central inventory of AI tools and usage), policy (no clear guidelines on acceptable AI use), process (no fast-track approval for low-risk AI tools), alternatives (no sanctioned tools that meet employee needs), and culture (no psychological safety to ask “Can I use this?”).

    Building AI governance isn’t about creating barriers—it’s about creating clarity. Employees want to do the right thing. They just need to know what the right thing is. Our CISO governance checklist provides a comprehensive framework for building these foundations.

    Getting Started

    If you’re concerned about shadow AI in your organization—and if you’re paying attention to the statistics, you should be—start with a discovery exercise. Survey employees, analyze network traffic, review expense reports. Understand your current exposure before trying to solve it.

    Assess risks by prioritizing discovered tools by data sensitivity and regulatory exposure. Focus governance efforts on highest-risk usage first—you can’t solve everything at once.

    Provide alternatives by deploying sanctioned AI tools that meet legitimate employee needs. Make approved tools easier to use than shadow alternatives. If the official path is harder than the unofficial one, you’ll keep losing.

    Build governance foundations through policies, processes, and monitoring. But start lightweight—you can add sophistication over time. Gartner also predicts that by 2030, 50% of enterprises will face delayed AI upgrades and rising maintenance costs due to unmanaged GenAI technical debt. Building governance now prevents that debt from accumulating.

    Communicate clearly. Tell employees what’s approved, what’s not, and why. Create a safe channel to ask questions. The Future of Agentic use case library can help illustrate what good AI governance looks like in practice.

    The Bottom Line

    Shadow AI is already in your organization. The question isn’t whether it exists, but how you’ll respond. A heavy-handed approach drives usage underground. A permissive approach exposes you to risk. The right approach—discovery, governance, and alternatives—lets you get the benefits of AI while managing the downsides.

    Want to understand your shadow AI exposure? Schedule a demo to see how Olakai helps enterprises discover, assess, and govern AI usage across the organization.