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

Gmyrek, Berg, Kamiński, Konopczyński, Ładna, Nafradi, Rosłaniec, Troszyński

2025ILO Working Paper5 citations
Exposure / measurementInterdisciplinary
LLM / Generative AIGenderAugmentation vs. substitution
View RepositoryDOI: 10.54394/HETP0387
Abstract

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. Using a representative sample from the 29,753 tasks in the Polish occupational classification system and a survey of 1,640 people employed in each 1-digit ISCO-08 groups, we collect 52,558 data points regarding perceive potential of automation for 2,861 tasks. We then compare this input with a survey and several rounds of Delphi-style discussions among a smaller group of international experts. Based on this process, we create a repository of knowledge about task automation that goes beyond national specificities and use it to develop an AI assistant able to predict scores for tasks in the technical documentation of ISCO-08. Our 2025 scores are presented in a revised framework of four progressively increasing exposure gradients, with a new set of global estimates of employment shares exposed to GenAI. Clerical occupations continue to have the highest exposure levels. Additionally, some strongly digitized occupations have increased exposure, highlighting the expanding abilities of GenAI regarding specialized tasks in professional and technical roles. Globally, one in four workers are in an occupation with some GenAI exposure. 3.3% of global employment falls into the highest exposure category, albeit with significant differences between female (4.7%) and male employment (2.4%). These differences increase with countries' income (9.6% female vs 3.5% male in Gradient 4 in HICs), and so does the overall exposure (11% of total employment in LICs vs 34% in HICs). As most occupations consist of tasks that require human input, transformation of jobs is the most likely impact of GenAI. Linking our refined index with national micro data enables precise projections of such transformations, offering a foundation for social dialogue and targeted policy responses to manage the transition.

Summary

Gmyrek et al. use a survey of 1,640 Polish workers rating 2,861 occupational tasks, validated by international experts and combined with AI-assisted prediction, to construct a refined global index of GenAI exposure across ISCO-08 occupations and estimate global employment exposure by gender and income level

Main Finding

Globally, 24% of employment has some GenAI exposure, with 3.3% in highest exposure category (gradient 4); significant gender disparities exist, particularly in high-income countries where 9.6% of female vs 3.5% of male employment falls in gradient 4; clerical occupations remain most exposed, while some digitized professional/technical occupations show increased exposure due to expanding GenAI capabilities

Primary Datasets

Polish 6-digit occupational classification system (KZiS, 29,753 tasks); CAWI survey of 1,640 employed workers in Poland (52,558 task ratings); Expert validation survey (608 tasks); ISCO-08 task descriptions (3,265 tasks); ILO harmonized microdata for employment estimates

Secondary Datasets

Polish Labour Force Survey (LFS/BAEL) for demographic benchmarking; ILO global estimation model data; OpenAI GPT-4, GPT-4o, and Google Gemini Flash 1.5 APIs for synthetic scoring

Key Methods
Survey of 1,640 employed workers in Poland rating automation potential of 2,861 representative tasks (52,558 data points); expert validation survey with international specialists; AI-assisted score reconciliation using GPT-4o and Gemini; semantic clustering of tasks; construction of AI predictor trained on adjusted scores to generate synthetic scores for all ISCO-08 tasks; occupational classification into exposure gradients based on mean and SD of task scores; global employment estimates using ILO microdata
Sample Period
2024-2025
Geographic Coverage
International
Sample Size
1,640 survey respondents (after exclusions from 1,640 initial); 52,558 task-level ratings; 2,861 unique tasks scored in main survey; 608 tasks in expert validation; 29,753 tasks in Polish classification; 3,265 tasks in ISCO-08; Global employment estimates cover 186 countries
Level of Analysis
Task, Occupation, Country
Occupation Classification
ISCO-08 (4-digit level); Polish 6-digit classification system (2,541 occupations)
Industry Classification
None
Notes
ILO Working Paper 140 [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets. [Claude classification]: This is a methodological paper constructing an AI exposure index. Uses LLMs (GPT-4, GPT-4o, Gemini) as research tools for score prediction and arbitration, not as the subject of study. The paper explicitly distinguishes GenAI from broader AI/automation. Builds on ILO 2023 methodology (Gmyrek, Berg, Bescond) with significant refinements. Exploratory regressions on scoring behavior mentioned but full econometric analysis deferred. Expert group includes ILO, NASK-PIB (Poland), Polish Ministry officials, and international researchers. Poland chosen as representative upper-middle/high-income country between advanced economies and emerging markets.