CEOs are budgeting for AI. Who is measuring the return?

Only 37% of respondents to McKinsey's State of AI: Global Survey 2026, published on August 25, 2026, can point to any effect of AI on their earnings (EBIT), about the same share as last year. Yet the spending keeps rising. In the US findings of KPMG's 2026 CEO Outlook, published on October 15, 65% of CEOs at large companies will put at least 10% of their capital budget into AI next year, and 84% call AI a top investment priority for the next three years.

The spending is decided. The return is a forecast.

Most of these CEOs expect the money to pay back within three years: 68% expect returns in one to three years. Only 9% say agents are already transformational for their organization.

The cost side worries them more than the return side. Half say managing the cost of implementing AI is mostly or very challenging. And 81% believe rising AI costs will reshape their margins more than labor costs over the next five years.

So AI has moved into the capital budget, with a payback date one to three years out and a cost line that is growing. That is a normal shape for an investment. What is not normal is how rarely the return is defined before the money is spent.

If you cannot say what an AI budget should return, you cannot check whether it did.

Return on intelligence is measured in the work

A license count or a token bill tells you what you spent. It does not tell you what changed. The return shows up in the work itself: hours a team no longer spends on a task, decisions made faster or with fewer errors, work that used to wait and now does not. And in what the business can now do for its customers: serve more of them, answer them sooner, and offer services that were too costly to run before.

Those numbers can only be read by the people who run the process. They know what the work took before, and they can see what it takes now. So the measure has to be agreed with them, process by process, before the spending starts, and read again on a fixed date.

KPMG's survey has a hint that CEOs see this too: 88% say they put realized AI value ahead of sticking to token budgets. Realized value needs a definition, an owner and a baseline.

Three questions for your leadership team

1. For each AI investment, what expected result did we write down before we spent the money?
2. Who owns that number, and do they run the work it measures?
3. Does every review end with a decision: spend more, change course or stop?

At Wyrework, this is the work we do with leadership teams. We design the measurement with them, not for them, so the business can see where it really stands with AI: what it costs to run, model by model, and what it returns, use case by use case. Leaders then decide what to scale and what to stop, and their own team keeps measuring after we leave.