A new research brief from the International Labour Organization (ILO) examines how artificial intelligence (AI) exposure indicators are used to assess potential impacts on jobs, highlighting both their value and their limitations.
As interest in generative AI (GenAI) grows, exposure indicators are increasingly used to estimate which tasks and occupations could be automated or transformed. However, the ILO cautions that these measures should not be interpreted, on their own, as predictions of job losses or labour market outcomes.
The brief shows that results vary depending on how exposure is measured. Earlier automation-based approaches pointed to lower-skilled, routine jobs as most at risk. More recent AI capability-based measures instead identify higher-skilled, cognitive occupations - including roles in business, finance, computing and education - as among the most exposed.
It also highlights that AI exposure extends beyond directly affected jobs. Highly exposed occupations tend to be closely connected to others through shared skills and career pathways, meaning that changes in these roles can have wider ripple effects across the labour market.
At the same time, all exposure measures share important limitations. They rely on static descriptions of current tasks, do not account for economic feasibility or adoption constraints, and reflect subjective assumptions. Most importantly, they capture what AI could do, as a first step in the analysis, not what will happen in practice.
The ILO stresses that exposure indicators are best understood as early signals of where work may change. To inform policy effectively, exposure indicators should be treated as early warning signals and be combined with evidence on actual labour market developments, including employment, wages and job transitions, as well as broader economic and institutional factors shaping AI adoption.
By clarifying both the strengths and limits of these tools, the ILO aims to support policymakers in using AI exposure measures responsibly and in designing policies that promote inclusive and sustainable labour market outcomes.
