Workers’ exposure to AI: What indicators tell us – and what they don’t

• AI exposure indicators estimate the extent to which AI systems can substitute for humans in specific tasks. Available exposure indices vary widely depending on the specific method used. Earlier computerization and automation measures suggested lower paid-workers in repetitive, routine manual or routine cognitive jobs to be more at risk, including some engineering-related occupations. In contrast, more recent AI capability–based indicators point to jobs with more “brain work” with higher exposure scores among cognitive, analytical, administrative and managerial occupations.
• Exposure patterns confirm substantial heterogeneity within occupational groups. Across different exposure measures, higher-skill and higher-wage occupations emerge as the most exposed. Occupations in business, finance, computing, mathematics, and education consistently show the highest exposure scores. Lower-skilled groups such as office and administrative support, and sales, also appear vulnerable, though with greater within-category variation.
• AI exposure extends beyond directly affected jobs via career paths and occupational transitions. Highly exposed jobs tend to occupy central positions in occupational networks—particularly in analytical, administrative, legal, financial and other professional fields. Because these jobs are closely connected to many others through shared skills and career transitions, shocks affecting them can spill over to related roles, indirectly affecting workers whose own jobs do not appear directly automatable. By contrast, manual, care, and craft occupations lie on the periphery of the network and experience fewer spillovers.
• Limitations affect all exposure measures. They rely on static task lists of existing jobs, omit other adoption constraints, such as economic conditions and institutional barriers, embed subjective judgements (expert, worker, AI-based) and differ conceptually in how “exposure” is defined. As they lack any references to relative wages, economic feasibility and exposure might diverge significantly.
• Exposure indicators reveal technological susceptibility, not labour market outcomes. They capture only what AI could do—under a static view of tasks—not whether firms find it profitable to automate, how workflows will change, or how employment, wages, and demand will adjust. In particular, they do not account for productivity gains that may lower costs, expand demand and, historically, have contributed to net job growth despite task automation. Therefore, exposure measures offer risk assessments about potential job transformations but cannot be interpreted as predictions of job displacement, productivity gains or reskilling needs.

*The authors, Rossana Merola (ILO), Ekkehard Ernst (ILO), Daniel Samaan (ILO), Maria del Rio-Chanona (University College London), Ole Teutloff (Oxford University) thank Caroline Fredrickson and Sher Verick for constructive comments. We gratefully acknowledge Uma Rani and Morgan Williams for preparing Table A1 in the Annex.