We Believe Measurement Protects People

Our Manifesto

The Measurement Gap

Last week, 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 PwC’s 2026 Global CEO Survey of 4,454 executives, 56% of CEOs report zero financial benefit from their AI investments. Not disappointing returns. Zero. Meanwhile, enterprise AI spending has crossed $37 billion 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 25 years 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 last week: here is our return on AI, by tool, by team, by business outcome.

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 three years is the anomaly. Three forces are converging to end it: enterprise AI budgets are moving from experimental line items to real P&L commitments, the EU AI Act enforcement deadline hits August 2026, and the workforce conversation has changed permanently.

The Stakes Just Changed

When Jack Dorsey cut nearly half of Block’s workforce citing AI productivity gains, the stock surged 23%. Wall Street rewarded the decision instantly. This will not be the last time. Every board in every industry is now asking whether a smaller team with AI can do more. Dorsey said he expects a majority of companies to reach the same conclusion.

Here is what concerns us. Dorsey made that decision based on internal data. He could see that smaller teams using AI tools were outperforming larger teams without them. He had the measurement to justify the move.

Most companies do not have that measurement. And when the pressure to follow Dorsey’s example reaches their boardroom, and it will, they will be making headcount decisions without the data to make them well. They will cut in the wrong places. They will keep the wrong tools. They will call it AI transformation when it is just cost reduction without a compass. Measurement is not a nice-to-have anymore. It is the difference between strategic workforce planning and blind cuts.

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 23% stock jump that somebody else got. Evidence. That is what we are building at Olakai. And it has never mattered more than it does right now.