Measure what AI earns, not just how much it's used
In Writer's 2026 study, 59% of organisations spend US$1 million or more a year on AI, and 29% report significant returns.
Colin Cardwell
The numbers this year draw a hard line between activity and value. Writer's 2026 Enterprise AI Study found that 59% of organisations now spend an average of US$1 million or more each year on AI, while only 29% report significant returns (Source: Writer, 2026 Enterprise AI Study). Sit with that gap for a moment. Most companies are writing seven-figure cheques, and fewer than a third can point to a return worth naming.
The view from the top is starker. In PwC's 2026 Global CEO Survey of 4,454 chief executives across 95 countries, only 12% of CEOs saw both higher revenue and lower costs from AI, and 56% saw neither (Source: PwC, 29th Global CEO Survey, 2026). Only about one in eight can draw a straight line from AI to both outcomes.
What usage tells youLink to this section
Token counts, seat licences, active users and adoption rates all feel like progress. They are easy to pull, and they tell you how busy your AI is, not what it earned.
OpenAI's CFO Sarah Friar made this point plainly in July, arguing that for years software success was measured through adoption, seats, active users and renewals, and that AI has to be measured by the work it actually accomplishes instead (Source: Fortune, OpenAI's CFO: 4 questions that reveal if your AI spend is paying off, July 2026). Her proposed metric, useful intelligence per dollar, asks four questions: is AI completing work that matters, what does each successful task cost, can people depend on the result, and does each dollar produce more value as usage grows (Source: Axios, OpenAI's CFO pitches a new way to measure AI's value, July 2026). Notice that not one of those questions is about how much you used.
The cost point matters more than it looks. Friar argues that the full cost of a successful task includes AI usage, retries and the cost of human review, not the token price alone (Axios). A cheap model that needs three attempts and a person to clean it up is not cheap. That hidden human clean-up is exactly what a usage dashboard can't count.
Judge AI as a capital allocation, not a technologyLink to this section
The leaders who get real returns treat AI spending the way they treat any other claim on capital. It has to compete. It has to earn its place against every other use of the same dollar.
The evidence that scale alone doesn't buy returns keeps stacking up. Domino Data Lab's Fifth Annual Enterprise AI Report, based on 639 senior enterprise AI leaders, found that the share whose AI returns fail to outpace their investment has held at 57% since 2025, even as 93% now report improved production capability, up from 88% (Source: Domino Data Lab, Fifth Annual Enterprise AI Report, 2026). Companies are getting better at shipping AI and no better at profiting from it. Getting a model into production used to be the milestone that mattered, and it isn't enough any more.
McKinsey arrives at the same place. Its research found that 64% of companies say AI is driving innovation, but just 39% report a measurable impact on earnings (Source: McKinsey, The state of AI in 2025). The gap between those two figures is the gap between a story and a defensible case.
What good actually looks likeLink to this section
Here is what we would track for a return we could defend, and what each signal tells you.
The job. Every tool gets one line describing the work it does: issues resolved, contracts reviewed, code shipped, hours returned. If you can't write that line, the tool doesn't have a job yet.
Cost per successful outcome. Not the licence, not the tokens. The all-in cost of getting the work done right, including the human time spent fixing, checking and re-prompting. Good looks like this number falling over time.
Dependability. What share of the output is ready to use, against what needs correction or escalation to a person. A tool that is used constantly but corrected constantly is a cost, not a return.
Value at scale. Whether each additional dollar buys more finished work or just more activity. If usage climbs and outcomes flatten, you have found waste.
The reliable way to read these signals is to ask the people doing the work, quietly and often, because they know where the clean-up hides. Short pulses are how The GAiGE asks the people doing the work, and they are anonymous by default. Dashboards show the tokens. The people doing the work can tell you what they produced.
Colin Cardwell
Founder of AiGILE and The GAiGE. Colin has spent thirty years designing custom software for tough business problems.
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