Thirteen source groups, kept distinct by population, time and method. These static pages work without scripts.
UK business AI · six qualified observations · CSV · UK evidence and methods · JSON
Reuters Institute · three qualified observations · CSV · Reuters evidence and methods · JSON
| Source | Published | Observed | Coverage | Limit |
|---|---|---|---|---|
| Microsoft AI Economy Institute | 2025–2026 report series | H1 2025–Q2 2026; US Q1 2026 | 147 economies; 50 states + DC, 35 metros, 3,143 counties | Adjusted population telemetry and modeled local estimates. Usage is not proficiency or productivity. |
| Anthropic | 26 June 2026 | Geography: May 2026; output analysis: 10 Apr–10 Jun 2026 | Claude chat and Cowork; country publication thresholds apply | Provider-specific conversations; classifier-inferred purpose. The per-capita index is not a user percentage. |
| European Commission JRC / Eurostat | JRC: 14 October 2026 | 2021–2025; Eurostat snapshot updated 15 June 2026 | EU27; eligible enterprises with 10+ persons employed | Broad AI, not only GenAI. Strategic-sector coverage varies; the country map uses the broader enterprise series. |
| McKinsey | 25 August 2026 | 4 May–8 June 2026 | 1,719 respondents across 97 countries | Self-reports, GDP weighting, non-census sample. Reported financial effects are not causal estimates. |
| Gallup / JFF / Families & Workers Fund | 6 October 2026 | 26 January–24 March 2026 | 15,482 US employees; AI-effect questions: 7,845 AI users | Employee assessments; frequency and benefit questions have different bases. Job quality association is not causation. |
| HEPI / Kortext | 12 March 2026 | December 2025 for the 2026 edition | 1,054 full-time UK undergraduates | Weighted self-report survey. Earlier waves differ; 2026 adds wellbeing uses. Tool provision is student-reported. |
| National Literacy Trust | 18 August 2026 | Early 2026; pupils and teachers January–March | 40,543 pupils aged 13–18; 2,567 teachers; 3,073 parents | School recruitment; pupil sample largely England. Questions use different routed bases; findings are not national prevalence estimates. |
| Dartmouth College | 30 October 2026 · working paper | 10–23 September 2026 | 1,003 US four-year college students; College Pulse weighted online panel | Separate, overlapping agreement measures, not complements or an adoption rate. Weighted online-panel self-reports; working paper, peer review not verified. Design effects were not verified. No causal learning outcomes or direct comparison with HEPI. Table 12, PDF p. 22. |
| UK Government · AI Adoption Survey | 29 October 2026 | 7 January–21 April 2026 | UK private-sector businesses with at least five employees in SIC A–N and R–S. 3,503 telephone interviews with technology-responsible respondents; Market Location commercial sampling frame; weighted by business size and sector; 5% response rate. | Broad AI, including pilots, not only generative AI. Selected sectors and a five-employee threshold differ from Eurostat; no direct UK–EU ranking. Self-reports, not a census or causal productivity evidence. Low response creates nonresponse risk; weighting does not establish its absence. The report compares 32% with 16% in the prior wave. These are repeated cross-sections, not a panel; questionnaire and achieved samples changed. Tables 27 and 61. |
| Reuters Institute / Oxford | 29 October 2026 | 2025 and 2026 waves; 2026: 14 August–28 September | Six-country weekly use; nine-country search exposure. YouGov online questionnaire; nonprobability samples with quotas and weighting for age, gender, region, education and political leaning. Combined-respondent estimates use YouGov weights, not an unweighted average of country percentages. | Self-reported use and exposure, subject to recall and interpretation; online samples underrepresent people offline. No conventional sampling margin of error; not a worldwide estimate or directly comparable with Microsoft usage estimates. Different country sets and questions mean the weekly-use and search-exposure figures must not be subtracted as an exposure gap. Weekly-use comparison covers the same six countries, not a longitudinal panel. The prompted standalone-tool list changed from 13 tools in 2025 to 14 in 2026; this qualification accompanies the trend. |
| OECD technology co-use | 5 November 2026 | Mostly 2025; selected older periods | 28 member countries; enterprises with 10+ employees | Enterprises with at least 10 employees; covered sectors and national definitions vary. Mostly 2025; Korea 2024, Canada and Japan 2023, Australia financial year 2021–2022. Co-use does not establish integration within a workflow or causal returns. OECD aggregate is an unweighted mean of 28 covered member-country rates, not a firm-weighted OECD population estimate. |
| US Census BTOS | 5 November 2026 | 5–18 October 2026; collected 19 Oct–1 Nov | Covered nonfarm employer businesses | Both estimates are shares of covered nonfarm employer businesses, not employees. Voluntary probability survey; business weights with nonresponse adjustments. Reference period 5–18 October 2026; collection 19 October–1 November; released 5 November. Collection wording changed to Super Intelligence (SI); effect on comparability is not established. No trend is inferred and no direct UK/EU ranking is made. The two questions overlap; their difference is not an employee-use gap. |
335 observations across 13 source groups; all 304 prior observations retained.
In the selected 2025 data, the simple average regional share of enterprises using AI was 11.43% across 43 Central and Eastern European regions, compared with 26.10% across 48 other covered regions. These are unweighted means of regional rates across 12 countries, not enterprise-weighted adoption rates for Europe.
OECD analysis of existing Eurostat isoc_r_eb_ain2 data, not an independent survey. Selected 2025 regional coverage in 12 countries; not Europe-wide. Regional achieved sample counts and confidence intervals are not supplied in the workbook. Employment exposure in column D is excluded: it has a different denominator.
The workbook’s OECD mean is 12.2162086899778%. Recomputing the simple mean of the 28 covered member-country rates reproduces it. Brazil, Croatia, Romania, Bulgaria and the EU aggregate are excluded from that calculation. This is not an enterprise-weighted rate for the whole OECD.
Reference years are mainly 2025: Korea uses 2024, Canada 2023, Japan 2021–2023, and Australia financial year 2021–2022. The population is enterprises with at least ten employees in covered sectors. National survey coverage varies; the workbook supplies no achieved sample counts or confidence intervals.
The source labels analytics “big data analytics”; European Statistical System countries and Canada’s 2023 data use the broader “data analytics” measure. European cloud data concern purchased intermediate or sophisticated services, including business software, database hosting and development platforms. Canada covers only the development-platform use case among sophisticated services. Co-use establishes neither workflow integration nor causal returns.
OECD chapter / Figure 3.4 ↗ · Original workbook ↗ · Workbook snapshot · 29 observations · CSV · Data & provenance · JSON
Reference period: 5–18 October 2026. Responses were collected 19 October–1 November and published 5 November (cycle 202622). National.xlsx records 24.2% at Response Estimates E16 (business-function use) and 26.8% at E19 (employee generative-AI use reported by the business). Corresponding standard errors are 0.61 and 0.56 percentage points, not confidence intervals. Both denominators are businesses, not employees; the questions can overlap.
BTOS is a voluntary probability survey covering nonfarm employer businesses in the United States, DC and Puerto Rico. Exclusions include agricultural production, railroads, the Postal Service, central banking, funds/trusts, NAICS 813 organisations, private households and public administration. Approximately 1.2 million firms are sampled annually across six panels of about 200,000; each firm is asked every twelve weeks. Those are sampled invitations, not achieved responses. Business weights adjust for nonresponse by state/size and sector/size. Sampling error, measurement error and residual nonresponse bias remain possible.
The 19 October collection changed the instrument to “Super Intelligence or SI”, including the generative-use question. Its effect on comparability has not been established. An earlier November 2025 change broadened business use from producing goods or services to any business function. No trend is inferred here; populations, size thresholds, reference windows and questions differ from the UK and EU surveys. These two US percentages are not subtracted as an employee-use gap.
5 November release ↗ · Survey notes ↗ · Methodology ↗ · National workbook snapshot · Two observations · CSV · Data & provenance · JSON