Half of CEOs believe their jobs are on the line if AI doesn’t pay off. Yet according to BCG’s AI Radar 2026 survey, 90% of chief executives believe agentic AI will deliver measurable ROI this year. That’s a remarkable level of conviction given what the data actually shows: IBM found that only 29% of executives can confidently measure their AI returns, and just 16% have scaled AI initiatives enterprise-wide.
The confidence is there. The measurement capability is not. And that gap — between what leaders believe AI can do and what they can prove it has done — is where budgets get cut, pilots stall, and competitors pull ahead.
This is why we built the SEE, MEASURE, DECIDE, ACT playbook — a four-step framework that takes enterprises from “we think AI is working” to “here’s exactly what it’s worth.” It’s the same methodology we use with every enterprise we work with, and the same framework that separates the 20% of organizations seeing real revenue impact from AI from the 74% who want it but can’t prove it.
The Playbook Gap
Deloitte’s 2026 State of AI survey captured the problem in a single data point: 74% of enterprises say they want AI to drive revenue growth. Only 20% have achieved it. That’s 3,235 business leaders across 24 countries essentially saying the same thing — we’re investing heavily, but we can’t connect the investment to results.
The issue isn’t the technology. AI models are more capable than ever. The issue is that most enterprises lack a systematic approach to proving value. They launch pilots without defining what success looks like. They measure activity (tokens processed, queries handled) instead of outcomes (revenue influenced, costs avoided). And when the CFO asks “what’s our return?”, the answer is a shrug wrapped in a slide deck full of usage charts.
BCG found that companies plan to double their AI spending in 2026, pushing AI investment to roughly 1.7% of total revenues. CEOs are committing more than 30% of their AI budgets specifically to agentic AI. The money is flowing. But without a measurement playbook, most of it flows into a black box.
Step 1: SEE — Map Your AI Ecosystem
You can’t measure what you can’t see. And in most enterprises, the AI landscape is far more sprawling than leadership realizes.
Workforce access to AI tools expanded by 50% in just one year, according to Deloitte — from fewer than 40% of workers to roughly 60% now equipped with sanctioned AI tools. That’s just the sanctioned ones. Factor in the tools employees adopt on their own — the shadow AI that bypasses procurement and IT review — and the real number is significantly higher.
The SEE step is an AI visibility audit. It answers three questions: What AI tools and models are running across the organization? Who is using them? And what data are they touching? This isn’t a one-time inventory. It’s an ongoing discovery process, because AI adoption in enterprises is a moving target — new tools appear weekly, usage patterns shift monthly, and the risk surface evolves with every new integration.
Most enterprises discover during this step that they have three to five times more AI touchpoints than they thought. Customer service teams running chatbots that marketing doesn’t know about. Engineering teams experimenting with code assistants that security hasn’t reviewed. Sales teams piping prospect data through AI tools that legal hasn’t vetted. Until you see the full picture, every other step in this playbook is built on incomplete information.
Step 2: MEASURE — Connect Activity to Business Outcomes
Once you can see what’s running, the next step is measuring what matters. And “what matters” is almost never what teams measure first.
The natural instinct is to track operational metrics: response time, tokens consumed, uptime, error rates. These are useful for engineering but meaningless to the CFO. The measurement step connects AI activity to the business KPIs that drive budget decisions — revenue influenced, costs reduced, risk mitigated, time recovered.
This is where most enterprises stall. IBM’s research found that while 79% of organizations see productivity gains from AI, only 29% can measure ROI confidently. The productivity is real but unquantified. A customer success agent saves each rep 45 minutes per day — but nobody has connected that time savings to the additional accounts each rep can now manage, or the churn reduction that comes from faster response times.
Effective AI measurement requires three elements. First, a baseline: what was the metric before AI? Without a counterfactual, you’re reporting output, not impact. Second, attribution: which portion of the improvement is actually due to AI versus other factors? Third, a time horizon that matches the business cycle. An AI agent that qualifies leads doesn’t show revenue impact in week one. It shows impact when those leads close, which in enterprise B2B might be 90 days later.
The 20% of enterprises that prove AI revenue impact aren’t using more sophisticated models. They’re using more sophisticated measurement. They define the success KPI before deployment, not after. They instrument their AI systems to capture business outcomes, not just technical telemetry. And they present results in the language the CFO speaks — dollars, not tokens.
Step 3: DECIDE — Turn Data Into Scaling Decisions
Measurement without decision-making is just reporting. The DECIDE step uses the data from MEASURE to answer the questions that actually move AI forward in an organization: Which pilots get promoted to production? Which get sunset? Where should the next investment go?
This is where the 30-to-45-day structured pilot becomes critical. Rather than running open-ended experiments that drift for months, a time-boxed pilot with predefined KPIs produces a clear decision point. At the end of 30 days, you have data. Not opinions, not anecdotes — data that shows whether the AI investment is generating the business outcome you defined in the MEASURE step.
The enterprises stuck in pilot purgatory almost always lack this decision framework. They have pilots running for six, nine, twelve months with no clear criteria for what constitutes success or failure. The result is the worst possible outcome: continued investment without conviction, where the AI initiative is too expensive to ignore and too poorly measured to champion.
A proper DECIDE framework answers four questions with data: Is the AI system delivering the outcome KPI we defined? Is the cost-to-value ratio favorable? Can the governance and risk profile support scaling? And does the organization have the operational readiness to absorb the change?
Google Cloud’s research found that top-performing enterprises generate $10.30 in value for every dollar invested in AI, while the average is $3.70. The difference isn’t luck. It’s disciplined decision-making about which investments to scale and which to cut — and that discipline is only possible with measurement data.
Step 4: ACT — Scale With Confidence
The final step is where measurement pays off: scaling the AI investments that prove their value while governing the entire portfolio continuously.
Deloitte found that 25% of organizations now report AI having a “transformative” effect — up from just 12% a year ago. These are the enterprises that have moved through SEE, MEASURE, and DECIDE, and are now deploying AI at scale with the data to back every decision. They’re not guessing which use cases deserve investment. They know, because they measured.
But scaling introduces new challenges that require continuous measurement. An AI agent that performs well with 100 users may behave differently with 10,000. Cost structures change at scale. Risk profiles shift as AI touches more sensitive data and higher-stakes decisions. The ACT step isn’t a one-time event — it’s an ongoing cycle of deploying, measuring, governing, and optimizing.
This is where governance and measurement converge. The enterprises with the strongest ROI data are also the ones with the most rigorous governance frameworks. Not because governance is a checkbox exercise, but because governance forces the discipline that measurement requires: defining what AI is allowed to do, instrumenting how it performs, and maintaining the accountability structures that ensure continuous improvement.
BCG reports that 72% of CEOs are now the primary decision-makers on AI, double the share from a year ago. These executives don’t want dashboards full of technical metrics. They want a portfolio view: which AI investments are generating returns, which ones need intervention, and where the next opportunity lies. The SEE, MEASURE, DECIDE, ACT framework gives them exactly that.
Building Your Playbook
The 74-to-20 gap Deloitte identified isn’t permanent. But it won’t close on its own. It closes when enterprises stop treating AI measurement as an afterthought and start treating it as the foundation of every AI initiative.
Start with SEE: audit your AI ecosystem. You’ll likely find more than you expected. Move to MEASURE: define the business outcomes that matter and instrument your AI systems to capture them. Progress to DECIDE: use 30-day structured pilots to generate decision-quality data. And then ACT: scale what works, govern what runs, and keep measuring.
The enterprises in the 20% didn’t get there with better AI. They got there with better measurement. The playbook isn’t complicated. The hard part is committing to it before the CFO asks the question you can’t answer. Our AI ROI measurement framework breaks down the methodology step by step, and Future of Agentic’s KPI library offers specific metrics by use case to get you started.
Ready to build your AI ROI playbook? Schedule a demo and we’ll show you how enterprises are turning AI activity into measurable business outcomes.