The AI adoption gap was a survey-question artifact
Daily Briefing | June 24, 2026
For two years, one number has anchored the argument that companies are slow to adopt AI: the Census Bureau’s firm survey put business adoption at 5% to 7%, while worker surveys showed 35% to 40% of people using AI on the job. Economists treated that gap as a real puzzle about diffusion. A St. Louis Fed analysis by Alexander Bick, Adam Blandin, David Deming, and two co-authors says the puzzle was mostly an artifact of the question. The old survey asked only whether firms used AI to produce goods and services, which misses marketing, finance, and administration, where most AI actually shows up. When the Census Bureau widened the question in November 2025, reported firm adoption nearly doubled overnight, from about 10% to 17%, with no change in actual behavior.
By the Numbers
Reported U.S. firm AI adoption nearly doubled, from about 10% to 17%, the moment the Census Bureau changed its survey question in November 2025, with no change in underlying behavior (St. Louis Fed).
Worker surveys put on-the-job AI use at 35% to 40%, against the old firm-survey reading of 5% to 7%, a gap the analysis attributes mostly to measurement (St. Louis Fed).
Among European firms that use AI, production accounts for only about 21% of use cases; marketing and sales (35%) and business-process organization (31%) lead (St. Louis Fed, citing EU-ICT-Firm data).
Projected to the U.S. using the European pattern, true any-purpose firm adoption would sit near 34%, close to the worker-survey figure (St. Louis Fed).
Research and Data
The AI adoption gap that worried economists was mostly a survey-design problem
The St. Louis Fed’s “On the Economy” analysis takes apart a statistic that has shaped two years of AI-and-work commentary. The Census Bureau’s Business Trends and Outlook Survey, the main U.S. read on firm-level AI use, asked from 2023 through October 2025 whether a business used AI “in producing goods or services.” That phrasing quietly excludes the functions where AI lands most often. In European data, which asks about AI for any business purpose, production is only about a fifth of use cases; marketing, sales, and back-office work make up the rest. When the Census Bureau swapped “producing goods or services” for “any of its business functions” in November 2025, reported adoption jumped from roughly 10% to 17% in a single step. The authors are direct that the jump reflects firms finally being asked about marketing and finance use, not a surge in new behavior. Their projection from the European pattern puts genuine U.S. any-purpose adoption near 34%, which closes most of the famous gap with worker surveys. The worker side of this is the part HR leaders should sit with: employees were not quietly using tools their companies had not adopted. The companies had adopted, and the survey simply was not asking in a way that counted it. The measurement fix matters for anyone who built a workforce thesis on the idea that firm adoption was stuck in single digits.
Source: Federal Reserve Bank of St. Louis, “On the Economy,” June 1, 2026, “Measuring AI Adoption among Firms: How You Ask Matters.” Read it here.
Why it matters: The “firms are slow, workers are ahead” story was built on a number that the question itself depressed, and corrected adoption sits near 34%, not 7%. If you have been pacing your own AI rollout against benchmarks that said most companies had barely started, you have been racing a phantom. Check whether your internal adoption metrics measure use across all functions or only the narrow slice your survey happens to ask about.
What Workforce Leaders Are Watching
Corrected firm adoption near 34% means AI is already embedded in marketing, finance, and administration at most companies. Which functions in your own organization are using AI that your formal tracking does not yet count?
The worker-versus-firm gap was largely a measurement gap, not a shadow-IT problem. Are your AI usage numbers built on a question broad enough to capture every function, or are you underreporting your own adoption to leadership?
Several published workforce theses leaned on the old 5% to 7% figure as evidence of slow diffusion. Which assumptions in your strategy deck rest on that number, and do they survive a 34% reading?
The Census Bureau fixed its question and the picture changed immediately. When the Department of Labor’s AI workforce hub publishes its first private-sector data, how will you tell a real trend from a measurement artifact?
This briefing was prepared automatically by the Workforce Rewired research assistant. All stories include direct source links.