We Believe Measurement Protects People

Our Manifesto

The Measurement Gap

A Fortune 500 CIO told me her board asked a simple question: “What is our return on AI?” She had spent $4.2 million on AI tools in the past year. She had adoption dashboards from four different vendors. She had employee surveys showing high satisfaction. What she did not have was an answer.

She is not unusual. According to a PwC global survey of more than 4,000 CEOs, 56% report zero financial benefit from their AI investments. Not disappointing returns. Zero. Meanwhile, enterprise AI spending is now measured in the hundreds of billions annually and is accelerating. Something does not add up. Either AI does not work, or we are not measuring it correctly. We believe it is the second one.

Flying Blind at Scale

Every AI vendor gives you a dashboard. OpenAI shows you token usage. Microsoft shows you Copilot adoption rates. Salesforce shows you Einstein interactions. Each dashboard answers a narrow question about its own tool. None of them answer the question that actually matters: is AI making this company more valuable? This is not a technology failure. It is a measurement failure. And it is structural.

No AI vendor has an incentive to give you a cross-platform view of your AI spending and its outcomes. Their dashboards are designed to show you that their tool is being used, not whether your overall AI strategy is working. The result is that enterprises have more AI analytics than ever and less clarity than ever.

We have spent decades building enterprise software. We have seen this pattern before. In the early days of digital marketing, every platform had its own metrics. Click-through rates here. Impressions there. Engagement scores somewhere else. None of it connected to revenue. It took a generation of analytics companies to close that gap and give marketers a unified view of what was actually driving results. AI is in that same moment. The tools are proliferating faster than the ability to measure them. And the stakes are significantly higher than marketing budgets.

Why We Started Olakai

We started Olakai because we saw this measurement gap widening and nobody building the infrastructure to close it. The problem is not that measurement is impossible. It is that it requires a layer that sits across every AI tool an enterprise uses, regardless of vendor. A layer that connects usage data, cost data, and business outcomes into a single view. No AI vendor will ever build this. It would require them to show you how their tool compares to their competitor’s tool. The incentive does not exist.

So we built it. Olakai is a vendor-neutral analytics and governance platform that works across your entire AI stack. Copilot, ChatGPT, Gemini, Claude, custom agents, AI features buried inside your SaaS applications. All of it. One view.

What does that look like in practice? It means a CFO can see that Copilot is delivering $14 per interaction in finance and $2 in marketing. It means a CISO can see which teams are using unsanctioned AI tools and what data they are exposing. It means a CIO can walk into a board meeting and answer the question that CIO could not answer: here is our return on AI, by tool, by team, by business outcome. And it means a CTO can see which AI coding tools are generating velocity gains versus burning budget — and forecast next month’s token bill before the invoice arrives.

These are not exotic questions. They are the same questions enterprises ask about every other category of technology spending. The fact that AI has operated without this level of accountability for years is the anomaly. Three forces are converging to end it: enterprise AI budgets have moved from experimental line items to real P&L commitments, the EU AI Act is now in enforcement, and the workforce conversation has changed permanently.

What Happens Without Measurement

Every board in every major enterprise is now asking the same question: can a smaller team with AI outperform a larger team without it? Can we restructure around this technology with confidence? These are not unreasonable questions. The evidence that AI delivers real productivity gains, in the right context with the right implementation, is real.

But the companies asking these questions are largely doing so without the measurement infrastructure to answer them. They restructure around tools they have not measured. They keep tools that do not perform because the data to prove underperformance does not exist. They cut in the wrong places because they cannot see where AI is adding value versus where it is just being used. And when those decisions turn out to be wrong, they cannot explain why — because they never had the data to explain why they made them in the first place.

The cost of guessing is not just wasted AI investment. It is the organizational decisions that get made on top of it. Measurement is not a nice-to-have anymore. It is the prerequisite for every other AI decision your company will make.

What We Believe

AI is going to reshape how companies operate. That is not hype. It is already happening. The question is not whether it will happen but whether companies will navigate the transition with real data or with guesswork.

We believe measurement protects people. When you can prove which AI initiatives are delivering value, you can invest in them with confidence. When you can prove which roles are being augmented rather than replaced, you can make workforce decisions that are honest and defensible. When you cannot prove any of that, every decision is political.

That is the mission. Give enterprises the clarity to make AI decisions based on evidence. Not vendor promises. Not board pressure. Not a competitor’s results they cannot replicate. Evidence.

We also believe governance cannot be separate from measurement. As AI moves deeper into operational workflows — autonomous agents making decisions, coding tools generating production code, AI tools touching regulated data — visibility alone is not enough. You need controls, audit trails, and the ability to enforce policy before a bad action happens. Olakai is built for both: the measurement layer and the governance layer, in one platform, across every AI tool and agent your organization runs.