The International Labour Organisation (ILO) has reported that one in 4 jobs globally is potentially exposed to generative artificial intelligence (GenAI), noting that transformation, not replacement, is the most likely outcome of the development..
The global labour organization, in a joint study conducted with Poland’s National Research Institute (NASK) and titled ‘Generative AI and Jobs: A Refined Index of Occupational Exposure’ launched today, highlighted the most detailed global assessment to date of how GenAI may reshape the world of work.
The index provides a unique and nuanced snapshot of how AI could transform occupations and employment across countries, by combining nearly 30,000 occupational tasks with expert validation, AI-assisted scoring, and ILO harmonized micro data.
Commenting on the study, ILO Senior Researcher and lead author of the study, Pawel Gmyrek, said: “We went beyond theory to build a tool grounded in real-world jobs. By combining human insight, expert review, and generative AI models, we’ve created a replicable method that helps countries assess risk and respond with precision.”
The report’s key findings include new “exposure gradients”, which cluster occupations according to their level of exposure to Generative AI, help policymakers distinguish between jobs at high risk of full automation and those more likely to evolve through task transformation; and 25 per cent of global employment falls within occupations potentially exposed to GenAI, with higher shares in high-income countries (34 per cent).
Others are, Exposure among women continues to be significantly higher. In high-income countries, jobs at the highest risk of automation make up 9.6 per cent of female employment – a stark contrast to 3.5 per cent of such jobs among men; and Clerical jobs face the highest exposure of all, due to GenAI’s theoretical ability to automate many of their tasks. However, the expanding abilities of GenAI result in an increased exposure of some highly digitized cognitive jobs in media-, software- and finance-related occupations.
The study’s report further showed that Full job automation, however, remains limited, since many tasks, though done more efficiently, continue to require human involvement. The study highlights the possibly divergent paths for occupations accustomed to rapid digital transformations – such as software developers – and those where limited digital skills might have more negative effects; and Policies guiding the digital transitions will be a leading factor in determining the extent to which workers may be retained in occupations that are transforming as a result of AI, and how such transformation affects job quality.
In his remarks, Senior Expert at NASK and one of co-authors of the new paper, Marek Troszyński, said: “This index helps identify where GenAI is likely to have the biggest impact, so countries can better prepare and protect workers. Our next step is to apply this new index to detailed labour force data from Poland.”
The ILO–NASK study emphasizes that the figures reflect potential exposure, not actual job losses. Technological constraints, infrastructure gaps, and skills shortages mean that implementation will differ widely by country and sector. Crucially, the authors stress that GenAI’s effect is more likely to transform jobs than eliminate them.
The report calls on governments, employers’, and workers’ organizations to engage in social dialogue and shape proactive, inclusive strategies that can enhance productivity and job quality, especially in exposed sectors.
Speaking on the report’s findings, Senior Economist at the ILO, Janine Berg, explained: “It’s easy to get lost in the AI hype. What we need is clarity and context. This tool helps countries across the world assess potential exposure and prepare their labour markets for a fairer digital future.”
This study marks the first in a series of ILO–NASK publications focused on GenAI and the future of work. Forthcoming reports will explore national labour market impacts and provide technical blueprints to support policy responses, particularly in emerging and developing economies.
