Rising AI spending, elusive returns: what KPMG's data says about measurement

KPMG's latest Global AI Pulse names the problem we built The GAiGE to help with. Spending is holding, usage is climbing, and yet very few leaders can prove the return. The interesting part is what separates the few who can.

The headline numbers set the scene. AI remains a top investment priority for 79% of leaders, up from 74% the previous quarter, with average AI spending holding steady at US$188 million (Source: KPMG, Global AI Pulse, June 2026). Everyday use is rising fast too, with 22% of organisations now describing AI as part of everyday work, up from 13%, the largest single-quarter shift on KPMG's maturity curve (KPMG). But momentum is not the same as return. Only 7% of leaders report establishing ROI, even as nearly one in four face pressure to prove value to investors (KPMG).

The real predictor is cost visibility, not tool choice#Link to this section

Here is the finding that matters most for anyone signing off budget. Leaders with strong cost visibility are five times more likely to report established ROI, 15% against 3% (KPMG). Yet only about a third of respondents report full visibility into AI operating expenses, and 42% have only partial visibility into AI spending (KPMG).

The consequences are already showing up in cancelled work. Around 33% cite limited understanding of AI cost structures, including how token-based pricing works, as a major challenge, and nearly half have already scaled back AI agent deployments because costs outweighed the benefits (KPMG).

If you want to move from the 3% group to the 15% group, this is what to track. Start with cost per tool per team, not a single line item for AI. Track spending against actual usage, so a licence nobody opens is visible as waste rather than hidden in a subscription total. Watch the trend on usage-based charges, since token consumption moves with behaviour and can drift well past forecast. What good looks like is simple to state: you can name, for any AI tool, what it costs, who uses it, and what changed in their work because of it. Most organisations can't do that yet, and the data shows it.

Executive ownership is the other half#Link to this section

Cost visibility doesn't appear by accident. It follows from someone owning the outcome. KPMG found that only 24% of leaders say the CEO is ultimately accountable for AI-driven business outcomes, while 29% point to the broader C-suite, which in practice can mean accountability belongs to everyone and no one (KPMG).

Where ownership is clear, results follow. Organisations where the CEO is explicitly accountable report higher confidence in their AI strategy, 60% against 22%, are more likely to realise meaningful business value, 57% against 21%, and are nearly four times more likely to report established ROI, 14% against 4% (KPMG).

The measurement lesson is that ownership needs a number attached to it. An accountable executive without a live view of adoption and cost is accountable for a guess. So pair the name with a cadence. Decide who reviews AI cost and impact, how often, and against what threshold. More than half of leaders now report having AI cost monitoring dashboards in place, and a similar share have embedded cost reviews into their AI approval processes (KPMG). That is a discipline any organisation can repeat.

The missing layer between activity and value#Link to this section

What KPMG describes, and what we see, is a gap that spreadsheets alone don't close. Finance can see the invoice. IT can see the licence count. Whether a tool actually changed how work gets done, or whether people quietly went back to the old way, is the question around both.

That is the measurement layer we focus on. Short pulses, anonymous by default, ask the people using the tools whether they save time, where the tool helps, and where it doesn't. Read alongside spending and adoption, that turns AI from a line of cost into a line you can defend. When someone asks whether the AI budget is working, the answer becomes a number with evidence behind it.

In KPMG's data, the leaders most likely to report established ROI are those with clear sight of their AI costs and a named executive accountable for the outcome.