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Generative AI and Jobs: A Refined Global Index of Occupational Exposure

Gains and lossesindex of occupational exposureactive
Generative AI and Jobs: A Refined Global Index of Occupational Exposure: an index of occupational exposure, published as a working paper with a research brief. Operator: International Labour Organization (ILO), whose Research department partnered with NASK, the National Research Institute of the Ministry of Digital Affairs in Poland; the expert group includes the ILO, NASK and the Polish Ministry of Family, Labour and Social Policy. Authors: the paper's title block lists seven, Paweł Gmyrek, Janine Berg, Karol Kamiński, Filip Konopczyński, Agnieszka Ładna, Konrad Rosłaniec, Marek Troszyński; the ILO's publication and brief pages list eight, adding Balint Nafradi. Its unit: a task's automation score, aggregated to a 4-digit ISCO-08 occupation (mean and standard deviation) and placed in one of four exposure gradients; employment shares then estimated by country income group, region and sex. Whose labor: the tasks of occupations in ISCO-08 and in Poland's 6-digit classification (29,753 tasks in 2,541 occupations), and the global employment in the 112 occupations classed as exposed.
Visit Generative AI and Jobs: A Refined Global Index of Occupational Exposure ↗The /non rowThe DSL audit
This study updates the ILO’s 2023 Global Index of Occupational Exposure to Generative AI (GenAI), incorporating recent advances in the technology and increasing user familiarity with GenAI tools.

In its own words: https://webapps.ilo.org/static/english/intserv/working-papers/wp140/index.html, read 2026-10-29

Who supplies, who directs, who gains

Supplies the labornot recorded; the index scores tasks' technical feasibility for GenAI automation. Its scores were supplied by 1,640 people employed in Poland, each scoring tasks of their own 1-digit occupational group, and by an expert group
Directs itnot recorded
Receives the gains, bears the costsnot recorded as observed outcomes; it estimates employment shares exposed, by sex and income group, and states that the productivity benefits depend on complementing human expertise

Each as the resource itself records it. Where it records nothing, the census says so.

What it records

IntersectionsDistribution of gains and losses: wages, employment, workload, credit, ownership, and who captures productivity gains
Kind of evidenceexposure estimate
Unit of recorda task's automation score, aggregated to a 4-digit ISCO-08 occupation (mean and standard deviation) and placed in one of four exposure gradients; employment shares then estimated by country income group, region and sex op
Whose laborthe tasks of occupations in ISCO-08 and in Poland's 6-digit classification (29,753 tasks in 2,541 occupations), and the global employment in the 112 occupations classed as exposed
MethodLLM-generated task scores (GPT-4, GPT-4o, Gemini Flash 1.5), a survey of workers scoring a representative task sample (52,558 data points on 2,861 tasks), expert validation with Delphi-style rounds, then an AI predictor fed the human scores to score all tasks; employment shares from the ILO's global estimation model on harmonised labour force microdata op
Coveragegeography: global, with regional and income-group estimates; the task survey run in Poland; period: a static snapshot of exposure at the start of 2025; published 20 May 2025
What it findsone in four workers globally (24%) is in an occupation with some GenAI exposure; 3.3% of global employment falls in the highest exposure gradient (4.7% of female, 2.4% of male employment); in high-income countries 9.6% female and 3.5% male; exposed employment is 34% in high-income countries and 11% in low-income countries; clerical occupations continue to have the highest exposure; some digitised professional and technical occupations increased; mean automation score 0.29 in 2025 against 0.30 in 2023, with standard deviation 0.14 against 0.30; most jobs are more likely to be transformed than made redundant, because of the continued need for human input
Its stated limitsthe estimates measure exposure and give an upper threshold; they are not the actual impact on employment; they do not account for new jobs created or future technical advances: a static snapshot at the start of 2025; the survey sample may not represent the entire working population; opinions are comparable only within each 1-digit group; for 91.5% of tasks, respondents did not report performing a similar task in their work; the index is generic, because of the nature of ISCO-08; all tasks are weighted equally in the exposure scores, since neither ISCO-08 nor the Polish classification records the relative frequency or importance of tasks; Poland is assumed to represent the upper threshold of automation potential, for its digital equipment and internet access; the gradient classifications merely suggest the general trends, since any task scoring system carries a degree of subjectivity; the bundle of tasks buffers occupations against full automation, but the efficiencies gained may mean fewer workers are needed, and even partial automation could lower job demand in the higher gradients

op the resource's own statement · 3p a third-party account · obs our observation · rd our reading. Hover a tag for its quote; each opens below with its source.

What this evidence can settle

Can answer

  • which occupations have tasks scored as technically feasible for GenAI automation, and how consistently across their tasks
  • what share of employment sits in exposed occupations, by sex, region and country income group

Cannot answer

  • observed employment, wage or hours change in any occupation
  • whether exposure leads to job loss, transformation or new work
  • who directs adoption or receives the gains from it
  • how GenAI is in fact used at work

In the worked questions

Where it meets Labor Vector

rd The index estimates where task labor could pass to a GenAI system on grounds of technical feasibility, task by task; it meets task-origin only as a prior on which tasks are exposed. It observes no performed work, so it cannot speak to whether a system's labor advanced a commissioned task, or to user labor drag.

Entity

Canonical nameGenerative AI and Jobs: A Refined Global Index of Occupational Exposure
Also known asILO Working Paper 140, Global Index of Occupational Exposure to Generative AI, Generative AI and jobs: A 2025 update, AI impact on jobs, ILO–NASK Index
Addresshttps://webapps.ilo.org/static/english/intserv/working-papers/wp140/index.html
OperatorInternational Labour Organization (ILO), whose Research department partnered with NASK, the National Research Institute of the Ministry of Digital Affairs in Poland; the expert group includes the ILO, NASK and the Polish Ministry of Family, Labour and Social Policy. Authors: the paper's title block lists seven, Paweł Gmyrek, Janine Berg, Karol Kamiński, Filip Konopczyński, Agnieszka Ładna, Konrad Rosłaniec, Marek Troszyński; the ILO's publication and brief pages list eight, adding Balint Nafradi
Founded2023 (the first ILO index, Gmyrek, Berg and Bescond); this refinement 2025-05-20
Kindindex of occupational exposure, published as a working paper with a research brief
Lifecycleactive — published 20 May 2025, updating the ILO's 2023 index; the Observatory still links its data ('AI impact on jobs') on 2026-10-29
Count29,753 Polish 6-digit tasks scored; 3,265 ISCO-08 tasks rescored; 1,640 survey respondents; 52,558 data points; 112 occupations classed as exposed (as of 2025-05-20)
Data accessthe working paper and brief online; task-level scores published at pgmyrek.github.io/2025_GenAI_scores_ISCO08; interactive charts on the ILO site op
LicenceCC BY 4.0 (the working paper) op
Versions and datesfirst index: 2023; wp140: 2025-05-20; doi: 10.54394/HETP0387; isbn: 9789220421871; read: 2026-10-29
Observation2026-10-29: read

Relations

Neighbours

Captures: the composition layer's account

The /non row

Evidenceevery field's quote, its source and the hash of the saved copy44
Negative tagswhat it is not4
  • NOT an observed employment outcome
  • NOT a forecast of job loss: exposure is an upper threshold of potential
  • NOT a measure of AI use at work
  • the worker survey was run in Poland only
Sourcesevery source read, with its date and hash4
Not yet checkedwhat remains to read4
  • the task-level scores on GitHub Pages and the occupation table (Annex A1) as data
  • the interactive charts
  • the later ILO works listed on the Observatory (Working Paper 166, March 2026; the research brief on exposure indicators, April 2026) and whether they revise this index
  • the 2023 index itself

Cite this record

Lee Sharks, ed. "Generative AI and Jobs: A Refined Global Index of Occupational Exposure." Labor Vector meta-aggregator, laborvector.org, compiled 2026-10-29. https://www.laborvector.org/aggregators/ilo-genai-exposure-index

Record https://www.laborvector.org/aggregators/ilo-genai-exposure-index · entity https://www.laborvector.org/aggregators/ilo-genai-exposure-index#entity · the census as data

@misc{lv-ilogenaiexposureindex2026,
  editor = {Sharks, Lee},
  title = {Generative AI and Jobs: A Refined Global Index of Occupational Exposure},
  howpublished = {Labor Vector meta-aggregator, laborvector.org},
  year = {2026},
  note = {Compiled 2026-10-29; every field sourced and dated},
  url = {https://www.laborvector.org/aggregators/ilo-genai-exposure-index}
}
Editor: Lee Sharks (ORCID 0009-0000-1599-0703). This page is the Labor Vector meta-aggregator's record of the resource above: its reading and fields are ours, and each field carries its source, its basis and the hash of the copy read. The resource's own terms and findings are kept as its own. Compiled 2026-10-29. CC BY 4.0.