OIT - Generative AI and Jobs - A Refined Global Index of Occupational Exposure

X Generative AI and Jobs A Refined Global Index of Occupational Exposure Authors / Pawel Gmyrek, Janine Berg, Karol Kamiński, Filip Konopczyński, Agnieszka Ładna, Balint Nafradi, Konrad Rosłaniec, Marek Troszyński

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Authorization for publication: Caroline Fredrickson, Director, Research Department ILO Working Papers can be found at: www.ilo.org/global/publications/working-papers Suggested citation: Gmyrek, P ., Berg, J., Kamiński, K., Konopczyński, F., Ładna, A., Nafradi, B., Rosłaniec, K., Troszyński, M. 2025. Generative AI and Jobs: A Refined Global Index of Occupational Exposure, ILO Working Paper 140 (Geneva, ILO). https://doi.org/10.54394/HETP038701 ILO Working Paper 140

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 internatem 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. About the authors Paweł Gmyrek is a Senior Researcher in the Research Department of the ILO.

Janine Berg is a Senior Economist in the Research Department of the ILO. Karol Kamiński is a Senior Researcher in the Analysis and Research Department of the NASK-PIB. Filip Konopczyński is the Director of the Analysis and Research Department of the NASK-PIB. Agnieszka Ładna is a Manager in the Analysis and Research Department of the NASK-PIB. Konrad Rosłaniec is a Researcher in the Analysis and Research Department of the NASK-PIB. Marek Troszyński is an Expert in the Analysis and Research Department of the NASK-PIB and Assistant Professor at the Civitas University, Warsaw.02 ILO Working Paper 140 Abstract 01 About the authors 01 Acronyms 07 X Introduction 09 X 1 Task-based approaches to automation in the literature 11 X 2 Tasks and occupations in the 6-digit system in Poland 12 X 3 Assessment of tasks’ automation potential 14 3.1. Initial Algorithmic predictions 14 3.2. Selection of occupational tasks for human assessment 15 3.3. Survey design and recruitment 17 3.4. Survey design limitations 18 3.5. Survey of Task Automation Potential 19 3.5.1. Sample properties 19 3.5.2. Participants’ Exposure to GenAI: Screening Questions 22 3.5.3. Scoring of task automation potential 23 Table of contents03 ILO Working Paper 140 X 4 Expert validation survey 27 X 5 Adjustment of survey sub-sample 30 X 6 Adjustment of all survey scores 34 X 7 Prediction of synthetic task-level scores for all ISCO-08 and 6-digit occupations 35 X 8 Adjusted Global Index of GenAI Exposure 37 X 9 Changes to occupational classifications 40 X 10 Revised global employment estimates 43 X Conclusion 46 Annex 48

1. Exposure by 4-digit ISCO-08 occupation 48

2. Sampling Formula for section 4.2. 61

X 9 Changes to occupational classifications 40 X 10 Revised global employment estimates 43 X Conclusion 46 Annex 48

1. Exposure by 4-digit ISCO-08 occupation 48

2. Sampling Formula for section 4.2. 61

3. Survey Questionnaire 62

References 68 Acknowledgements 7104 ILO Working Paper 140 List of Figures Figure 1. Incomeand population-based similarities (A) and access to the internet (B) across countries 12 Figure 2. Distribution of synthetic automation scores from 3 LLMs, by ISCO-08 1-digit 14 Figure 3. Age and sex distribution in the survey compared to Labour Force Survey (LFS) data in Poland (employed individuals) 20 Figure 4. Distribution of occupational groups in the survey, compared to LFS in Poland (employed individuals) and to the desired sample (Table 2) 21 Figure 5. Occupation and sex distribution among survey participants compared to LFS in Poland (employed individuals) 21 Figure 6. Frequency of use of GenAI, by 1-digit ISCO-08 22 Figure 7. Expectations of impact on the work area 23 Figure 8. Expectations of impact on individuals’ current job – distribution of individual scores 23 Figure 9. Distribution of task-level scores by sex and occupational group (1-digit ISCO-08) 24 Figure 10. Scoring as a function of familiarity with GenAI and the scored task – distribution of individual scores 25 Figure 11. Schema of the scoring stages 27 Figure 12. Example of a dendogram used for the review of tasks’ semantic clustering 28 Figure 13. Task-level scores from the survey and experts, compared to AI-arbitrated scores 30 Figure 14. Comparison of Adjusted Scores: GPT-4o vs Gemini by Occupational Group 31 Figure 15. Survey scores, expert evaluation and final adjustments for 2,861 tasks in the main survey 34

Figure 14. Comparison of Adjusted Scores: GPT-4o vs Gemini by Occupational Group 31 Figure 15. Survey scores, expert evaluation and final adjustments for 2,861 tasks in the main survey 34 Figure 16. AI Exposure Gradients: ISCO-08 4-digit level occupations 37 Figure 17. AI Exposure: Comparison of 2025 with 2023, ISCO -08 4-digit level occupations 40 Figure 18. Changes to occupational exposure between 2023 and 2025 within Gradient 4 41 Figure 19. 4-digit level occupations (ISCO-08) with largest changes in mean scores between 2023 and 2024 42 Figure 20. Global estimates of occupations potentially exposed to GenAI (% of employment by sex) 4305 ILO Working Paper 140 List of Tables Table 1. Composition of occupational tasks in the Polish classification system and in ISCO-08 13 Table 2. Survey sample selection 16 Table 3. Core survey questions 17 Table 4. Sample of adjustments and justifications between the survey and expert scores (examples of largest upward and downward revisions) 31 Table 5. GenAI Exposure Gradients: Definition and Interpretation 38 Table A1. ISCO-08 occupations by exposure gradient 4806 ILO Working Paper 140 List of Boxes Text Box 1. Sample Question for Task Automation Assessment 18 Text Box 2. Introductory prompt for survey respondents 19 Text Box 3. The conceptual structure of the LLM prompt (Python code for GPT-4o) 3507 ILO Working Paper 140 Acronyms AI Artificial Intelligence API Application Programming Interface ATMs Automatic Teller Machines CAWI Computer-Assisted Web Interview

CEDLAS Center for Distributive, Labor and Social Studies CEE Central and Eastern European CEPS Centre for European Policy Studies GBB Gmyrek, Berg and Bescond, 2023. ILO Working Paper 96 GPT-4 Generative Pre-trained Transformer 4 GPT-4o Generative Pre-trained Transformer 4 Omni GUS Główny Urząd Statystyczny (Central Statistical Office, Poland) HIC High-Income Countries ICT Information and Communication Technology ILO International Labour Organization IMF International Monetary Fund ISCO-08 International Standard Classification of Occupations (2008 version) IT Information Technology ITC-ILO International Training Centre of the ILO (Turin) LFS Labour Force Survey (BAEL in Poland) LICs Low-Income Countries LLM Large Language Model ML Machine Learning MOL Ministry of Family, Labour and Social Policy in Poland NASK-PIB National Research Institute08 ILO Working Paper 140 ONET Occupational Information Network (U.S. Department of Labor) PIE Polski Instytut Ekonomiczny (Polish Economic Institute) SD Standard Deviation SGH Szkoła Główna Handlowa (Warsaw School of Economics) U.S. / US / USA United States of America UK United Kingdom UNLP National University of La Plata09 ILO Working Paper 140 X Introduction In August 2023, the ILO published the first global employment estimates of potential occupational exposure to generative artificial intelligence (GenAI) (Gmyrek et al., 2023). This research was prompted by concerns – that have featured prominently in the media – of the potential for the technology to replace large shares of knowledge work. Since the release of the 2023 estimates, there have been further technological advances in GenAI, including the introduction of Large Language Models (LLMs) with expanded capabilities in image recognition and audio and video production. Moreover, many new GenAI applications have been developed as a result of the possibility of linking GenAI models with Application Programming Interface (API) tools. Indeed, GenAI has been likened to a general-purpose technology, meaning

recognition and audio and video production. Moreover, many new GenAI applications have been developed as a result of the possibility of linking GenAI models with Application Programming Interface (API) tools. Indeed, GenAI has been likened to a general-purpose technology, meaning that it may continue generating new applications and innovations across various sectors, driving long-term economic and societal transformations. Despite this rapidly developing landscape, it is important for policy makers to develop deeper understanding of how technological advances might impact existing jobs. To address this need, the Research department of the ILO has partnered with NASK, the National Research Institute of the Ministry of Digital Affairs in Poland, to develop a more precise assessment of potential effects of GenAI on employment across countries. This study builds on the method developed by the ILO (Gmyrek, Berg and Bescond, 2023 - GBB hereafter), which used task descriptions from the 4-digit International Standard Classification of Occupations (ISCO-08) and the GPT-4 model to estimate task-level and occupational scores of exposure to GenAI technology and then derives global employment estimates based on official ILO labour force data. But the study includes several key refinements. The first step in our analysis is based on Poland’s national 6-digit classification of occupations that includes nearly 30,000 tasks. This expands tenfold the number of tasks given in the ISCO08 structure and enables a fine-grained assessment of tasks’ automation potential, based on a more up-to-date set of occupations and tasks than those captured in the ISCO-08 system. As Poland’s 6-digit classification is aligned with the 4-digit ISCO-08, the occupational scores can be compiled at the 4-digit level allowing a straightforward comparison and updating of the 2023 estimates of the ILO (GBB). In addition, the study improves the methodological framework of GBB, by drawing on the combined strengths of human and AI abilities, with an additional layer of expert verification. Specifically, we surveyed 1,640 people currently employed in Poland in each ISCO-08 1-digit

estimates of the ILO (GBB). In addition, the study improves the methodological framework of GBB, by drawing on the combined strengths of human and AI abilities, with an additional layer of expert verification. Specifically, we surveyed 1,640 people currently employed in Poland in each ISCO-08 1-digit group to rank the automation potential of a representative sample of tasks that pertain to their occupational group. As the second step, we asked a mix of national and international experts to conduct a detailed review of a large sample of tasks previously assessed in the survey to validate or adjust the level of automation scores through an iterative process. Since we maintain the hierarchical link to ISCO-08 and focus on technological feasibility rather than specific country context, by combining opinions of local and international experts, we create a system that enables more precise predictions of the potential of automation of individual occupational tasks with GenAI technology, including at the international level. To achieve that, we input the human scores into an AI model, to generate scores reflecting human judgement for nearly 30,000 individual tasks pertaining to 2,500 occupations at 6-digit level in Poland. We then use the same AI predictor to re-generate scores for the tasks in ISCO-08, previously provided by GBB (2023), thereby adjusting the 2023 index of the ILO, which now benefits from both the input of people10 ILO Working Paper 140 employed in each ISCO-08 1-digit group and from expert opinions. As the final step, we provide updated global, regional and income-based estimates of employment that has the potential to be transformed by Gen AI technology. Our study specifically targets GenAI tools, isolating the effects of this recent family of AI from broader AI applications, such as the use of Machine Learning (ML) algorithms or image recognition in industrial production. There are several considerations that justify such selective focus. First, we respond to the growing need for a research tool that could provide a better picture of GenAI’s potential impact on national labour markets, given the growing societal angst concernnition in industrial production. There are several considerations that justify such selective focus. First, we respond to the growing need for a research tool that could provide a better picture of GenAI’s potential impact on national labour markets, given the growing societal angst concerning this technology and the recent reports about increasing levels of adoption of GenAI at work (Bick et al., 2024; Faverio and Tyson, 2022; Grampp et al., 2023; Maison & Partners and ThinkTank, 2024; Pew Research Center, 2023; Rutgers, 2024). Second, while several indicators of occupational exposure to broader AI technologies are available to researchers (Nurski and Vansteenkiste, 2024), very few tools allow for a more detailed focus on GenAI (Nurski and Ruer, 2024). A new, more precise exposure index enables a closer alignment of the academic work on digital economy and labour with the significant interest in the GenAI technology in the media and in the public debate.1 Third, the methodological blueprint provided by our research can be quite flexibly expanded to other types of AI in the future or focused more narrowly on specific subsets of digital technologies and sectors of their application. This paper is the first in a series of upcoming publications based on ILO and NASK cooperation that strive to improve the methodology for assessing employment effects of AI, but also to document in detail the methods used, including the survey, expert verification process and the construction of the AI model used for predictions. Subsequent papers will present an assessment of the potential impact of GenAI on the Polish labour market, based on more detailed occupation and task-level data and using a mix of quantitative and qualitative methods. All publications will include the methodological blueprints and technical details of both the quantitative and qualitative components, including survey questionnaires and interview guidelines. By making these tools openly accessible, we hope to contribute to the development of an improved method for assessing potential impact of GenAI on jobs and to stimulate more detailed national-level studies, including in lowerand middle-income countries.

tative components, including survey questionnaires and interview guidelines. By making these tools openly accessible, we hope to contribute to the development of an improved method for assessing potential impact of GenAI on jobs and to stimulate more detailed national-level studies, including in lowerand middle-income countries. 1 For example, Felten et al. (2023) provided an updated version of their original exposure index from 2021, adjusting for the abilities of GenAI tools. As shown in their paper, the adjustment does not result in a major shift in occupations exposure and still makes it impossible to assess the abilities of this new technology in a selective manner. See Gmyrek et al. (2024) for a more detailed comparison of these scores.11 ILO Working Paper 140 X 1 Task-based approaches to automation in the literature

Central to the study of technology on work is the insight that jobs are a “bundle of tasks” (Autor, 2015). As such, task automation might, or might not lead to job automation, depending on the importance of a particular task to an occupation. Analyses of employment effects of automation that use the task-based approach, including this study, attempt to capture two central considerations: (i) the ability of a specific technology to automate a given task and (ii) the character of existing occupations in the labour market, based on the detailed tasks performed by these occupations. By modelling the intersection of these two dimensions, one arrives at a range of possible projections of how such technology could interact with occupational tasks, leading either to their full automation through technology-driven substitution, or to a transformation, through partial automation and human-machine complementarity. While the task-based framework certainly has limitations, recent academic literature shows the analytical advantages of this method for modelling employment and labour market impacts (Acemoglu et al., 2024; Nurski and Ruer, 2024). Research on AI and tasks has shown important improvements in methods and scope in recent years, which includes using AI tools for research purposes. In one of the early papers on Machine

elling employment and labour market impacts (Acemoglu et al., 2024; Nurski and Ruer, 2024). Research on AI and tasks has shown important improvements in methods and scope in recent years, which includes using AI tools for research purposes. In one of the early papers on Machine Learning (ML) and jobs, Frey and Osborne, (2013) fed the opinions of a group of IT technology experts into an ML algorithm to develop occupational-level projections of automation potential for the US job market. The emergence of GenAI APIs has simplified the possibility of combining human expertise and algorithmic capacities for research purposes. Relying on this idea, Eloundou et al. (2023) demonstrated a close alignment of GPT-4 predictions with a survey of 70 AI experts on the potential of automating occupational tasks with LLMs, and subsequently built on this feature to develop synthetic automation scores for the US market. Most task-based studies are focussed on the US, due to the very detailed nature of the ONET database, as well as its public accessibility. This typically forces researchers interested in the global picture or other countries and regions to base their analysis on the strong assumption of similarity of local economic and labour effects to those projected in the US context. In reality, occupational tasks can vary significantly among individual countries, which poses a particular challenge in developing countries. As such, working with tasks in the ISCO-08 system offers a useful and simple common denominator for projections across different countries, regions and income groups. Our method combines the strengths of this global framework with a significantly more detailed national classification system in Poland, which provides a detailed list of occupations and tasks, based on the latest labour market data. This helps us establish a starting point for modelling interaction of GenAI’s abilities with up-to-date tasks and occupations in a setting located between the countries with highest incomes and the emerging economies (Figure 1). Using a combination of GenAI tools, we subsequently transfer this knowledge back to the tasks in ISCO-08 system.12 ILO Working Paper 140 X 2 Tasks and occupations in the 6-digit system in Poland

located between the countries with highest incomes and the emerging economies (Figure 1). Using a combination of GenAI tools, we subsequently transfer this knowledge back to the tasks in ISCO-08 system.12 ILO Working Paper 140 X 2 Tasks and occupations in the 6-digit system in Poland

Within Europe, Poland is quite representative of the larger group of Central and Eastern European (CEE) countries, albeit with a significantly larger population (38 million) than most of its peers. Globally, the CEE ranks between the high-income advanced economies and the emerging economies of the Global South. With respect to access to the internet, Poland ranks below UK and Germany, but higher than Japan and France. Thus, for assessing the potential of task automation, it seems reasonable to assume that Poland is representative of the upper threshold of automation potential, due to high availability of digital equipment and widespread internet access. X Figure 1. Incomeand population-based similarities (A) and access to the internet (B) across countries The Ministry of Family, Labour and Social Policy (MOL) in Poland maintains a system of occupational classifications, with task and job descriptions that are regularly updated.2 The system is used by Statistics Poland (GUS) and other state institutions for labor market analysis. At the most detailed, 6-digit level, this framework contains 2541 occupations and 29,753 corresponding tasks (Table 1). Since the system is publicly available online, we initially designed a scraping bot that crawled through individual occupational pages and downloaded job descriptions and corresponding tasks into a structured data frame.3 The extracted content was subsequently cross verified against a tabular occupational classification provided by the Ministry. The Polish classification system is compatible with the hierarchical structure of ISCO-08, which means that we could easily append an additional mapping of occupations from 1 to 4-digit level (Table 1). 2 The occupational classification database (KZiS) was first published in 2014 and has been regularly updated, with the latest validation on 1 January 2025. The classification was introduced by the Regulation of the Minister of Labour and Social Policy on the classifica2 The occupational classification database (KZiS) was first published in 2014 and has been regularly updated, with the latest validation on 1 January 2025. The classification was introduced by the Regulation of the Minister of Labour and Social Policy on the classification of occupations and specializations for labor market purposes and its scope of application, dated 7 August 2014 (Dz.U. z 2018 r. poz. 227, z 2021 r. poz. 2285, z 2022 r. poz. 853, Dz.U. z 2024 r. poz. 1372). Additionally, the Ministry publishes a mapping to ISCO-08.

See: https://psz.praca.gov.pl/rynek-pracy/bazy-danych/klasyfikacja-zawodow-i-specjalnosci. 3 Web scraping was primarily conducted to demonstrate the potential of acquiring non-traditional data from publicly accessible sources, in contexts where such activity is not restricted by server settings or legal regulations. The process adhered to responsible data collection practices, including appropriate frequency settings to avoid overloading servers.13 ILO Working Paper 140

X Table 1. Composition of occupational tasks in the Polish classification system and in ISCO-08 ISCO-08 occupational classification Polish 6-digit occupational classification ISCO-08 1-digit Group Name Count of 2-digits Share in 2-digits Count of 3-digits Share in 3-digits Count of 4-digits Share in 4-digits Count of 6-digits Share in 6-digits Count of 6-digit tasks Share in 6-digit tasks 1 Managers 4 10.0% 11 8.7% 30 7.1% 173 6.8% 2565 8.6% 2 Professionals 6 15.0% 27 21.4% 92 21.7% 721 28.4% 8715 29.3% 3 Technicians and associate professionals

2 Professionals 6 15.0% 27 21.4% 92 21.7% 721 28.4% 8715 29.3% 3 Technicians and associate professionals 5 12.5% 19 15.1% 80 18.9% 507 20.0% 5956 20.0% 4 Clerical support workers 4 10.0% 8 6.3% 27 6.4% 71 2.8% 768 2.6% 5 Service and sales workers 4 10.0% 13 10.3% 40 9.5% 149 5.9% 1730 5.8% 6 Skilled agricultural, forestry and fishery workers 3 7.5% 9 7.1% 17 4.0% 53 2.1% 684 2.3% 7 Craft and related trades workers 5 12.5% 14 11.1% 65 15.4% 407 16.0% 4554 15.3% 8 Plant and machine operators, and assemblers 3 7.5% 14 11.1% 40 9.5% 348 13.7% 3769 12.7% 9 Elementary occupations 6 15.0% 11 8.7% 32 7.6%