A new research brief from the International Labor Organization (ILO) explores how artificial intelligence (AI) exposure indicators are being used to assess potential employment impacts, highlighting both their value and limitations.
As interest in generative AI (GenAI) grows, exposure metrics are increasingly being used to estimate which tasks and occupations have the potential to be automated or transformed. However, the ILO cautions that these measures should not be interpreted in isolation as predicting job losses or labor market outcomes.
The summary shows that results vary depending on how exposure is measured. Previous automation-based approaches identified low-skill, routine tasks as being most at risk. Instead, more recent AI competency-based measurements have identified higher-skilled cognitive occupations as the most at-risk occupations, including roles in business, finance, computing, and education.
It also highlights that exposure to AI extends beyond the jobs directly affected. Highly exposed occupations tend to be closely linked to other occupations through shared skills and career paths, meaning that changes in these roles can have far-reaching ramifications across the labor market.
At the same time, all exposure measures have important limitations. These rely on static descriptions of the current task, do not take into account economic feasibility or adoption constraints, and reflect subjective assumptions. The most important thing is knowing what AI can do as a first step in analysis, not what actually happens.
The ILO emphasizes that exposure indicators are best understood as early signals that work may change. To effectively inform policy, exposure indicators must be treated as early warning signals and combined with evidence about actual labor market trends, including employment, wages, and job mobility, as well as broader economic and institutional factors shaping AI adoption.
By highlighting both the strengths and limitations of these tools, the ILO aims to help policymakers use AI exposure controls responsibly and develop policies that promote inclusive and sustainable labor market outcomes.
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