AI Job Risk by Country
A country's exposure to AI is mostly a story about what its people actually do for work. Economies weighted toward office and back-office roles face faster automation pressure than those anchored in physical, field, or care work.
Nations with large clerical, financial, and business-process sectors, including outsourcing-heavy labor markets, tend to score higher, because that work is text- and rule-based. Economies built on manufacturing, resources, construction, hospitality, and hands-on services hold up better, since much of that work resists automation regardless of how capable models become.
The ranking below combines two ILO datasets: how each country's employment splits across occupation groups, and how exposed each group's work is to generative AI. Treat a single national number as a broad signal, not a full picture; open a country to see the occupation mix behind it.
Country Ranking
| Rank | Country | Exposure (ILO, 0–1) | Clerical support workers, share of employment |
|---|---|---|---|
| 1 | Singapore | 0.341 | 9.1% |
| 2 | Switzerland | 0.332 | 12.1% |
| 3 | Netherlands | 0.327 | 8.3% |
| 4 | United Kingdom | 0.325 | 8.8% |
| 5 | Germany | 0.324 | 12.8% |
| 6 | Sweden | 0.323 | 5.8% |
| 7 | Canada | 0.321 | 11.6% |
| 8 | Japan | 0.317 | 20.9% |
| 9 | United States | 0.316 | 8.3% |
| 10 | Australia | 0.315 | 8.7% |
| 11 | France | 0.312 | 8.0% |
| 12 | South Korea | 0.305 | 12.4% |
| 13 | Italy | 0.303 | 12.1% |
| 14 | Spain | 0.297 | 10.0% |
| 15 | Saudi Arabia | 0.286 | 5.1% |
| 16 | United Arab Emirates | 0.270 | 5.9% |
| 17 | Brazil | 0.270 | 8.6% |
| 18 | Mexico | 0.252 | 6.0% |
| 19 | China | 0.220 | 4.6% |
| 20 | India | 0.206 | 2.3% |
Method: each country's employment by ISCO-08 major occupation group (ILOSTAT, ILO modelled estimates) multiplied by the average ILO exposure score of the occupations in that group (ILO Working Paper 140), then added up. The ILO publishes skilled agricultural workers and elementary occupations as one group and leaves out the armed forces. The figure uses the ILO's 0–1 scale, not the 0–100 occupation index used elsewhere on this site, and it treats an occupation as equally exposed in every country.
Sources
Exposure index calculated by ai-job-risk.net from the datasets below (data as of October 15, 2026). US labour figures are from the BLS, unchanged.
- Eloundou, Manning, Mishkin & Rock, "GPTs are GPTs: Labor market impact potential of LLMs", Science (2024); data MIT licence
- Gmyrek, Berg, Kamiński, Konopczyński, Ładna, Nafradi, Rosłaniec, Troszyński. Generative AI and Jobs: A Refined Global Index of Occupational Exposure. ILO Working Paper 140. Geneva: ILO, 2025. © ILO, CC BY 4.0
- Tomlinson, Jaffe, Wang, Counts & Suri, "Working with AI: Measuring the Occupational Implications of Generative AI", Microsoft Research; CC BY 4.0
- Anthropic Economic Index (job_exposure); CC BY
- U.S. Bureau of Labor Statistics, Employment Projections 2025–35 and AI exposure categories
- ILOSTAT, ILO modelled estimates: employment by sex and occupation (ISCO-08), accessed 16 October 2026. © ILO, CC BY 4.0
The publishers did not prepare, review or endorse this site. Full citations, licences and method
Frequently asked questions
Q.Which countries are most exposed to AI?
The economies near the top of this ranking are those weighted toward office and back-office work: large clerical, financial and business-process sectors, including outsourcing-heavy labour markets. Economies anchored in manufacturing, resources, construction, hospitality and hands-on services sit lower, because much of that work resists automation regardless of how capable models become.
Q.Why would the same occupation get a different figure in another country?
It does not. Occupation figures are the same everywhere; what differs between countries is the occupation mix: how many people work as clerks, professionals, machine operators and so on. A national figure is a weighted view of that mix, which is why two countries can share every occupation figure and still land in different places.
Q.Does a low national figure mean my job is safe there?
No. A national figure averages across an entire labour market, and every country contains both highly exposed office work and durable physical or accountable work. Where you sit inside that range matters far more than which side of a border you are on. Use the country page for context and the occupation page for your own reading.
Q.How is a country's figure calculated?
Each country's employment in each ISCO-08 major occupation group (ILO modelled estimates) is multiplied by the average ILO exposure score of the occupations in that group, and the results are added up. The figure is on the ILO's 0–1 scale. It treats an occupation as equally exposed in every country, so it reflects differences in occupation mix only.