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To judge which work is most exposed to AI automation, assess the tasks in a job against a clearly defined AI capability—not the job title alone. Then state how task-level results are combined, and keep technical exposure separate from adoption and actual employment outcomes. An exposure score describes what AI may be able to do; it does not predict that a job will disappear.
What “AI exposure” means
Exposure is the degree to which a specified AI system could perform or speed up particular work tasks. It is not a single, universal property of an occupation: a role can include automatable information processing alongside tasks requiring human judgment, interaction, accountability, care, or physical presence.
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Define the technology before interpreting a ranking. A generative AI system, broader AI capabilities, and physical robotics have different abilities and task profiles. A measure focused on language-model tasks should not be presented as a measure of all automation, and robotics should be included only when that is part of the question.
Exposure is also distinct from adoption. Whether an employer implements a system depends on economic feasibility, workflow changes, regulation, responsibility, and organizational constraints. The International Labour Organization’s 2026 brief puts the distinction plainly: “They capture what AI could do, as a first step in the analysis, not what will happen in practice.”
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How to assess a job’s tasks
- Set the scope. Specify the geography, occupation classification, AI technology, and time horizon. A result based on U.S. O*NET task descriptions is not automatically a global ranking. The ILO’s global analysis uses ISCO-08, while OECD work also includes examples based on O*NET.
- Describe the work actually done. Break the role into tasks such as routine information processing, communication, analysis, judgment, care, physical handling, and accountability. Job titles can cover very different task mixes, even within the same occupation.
- Evaluate capability task by task. Ask whether the specified AI could complete the task, accelerate it, or assist with only one step. Make the human-review assumption explicit. For example, one OECD framework defines exposure using tasks an LLM could complete in half the time; that threshold is a methodological choice, not a universal definition.
- Aggregate transparently. Explain how task assessments become an occupation-level result. Possible approaches include counting the share of tasks that meet a threshold, calculating a mean exposure score, or measuring a gap between AI capabilities and occupational requirements. Preserve task variation where the measure allows it rather than presenting an average as though every worker does identical work.
- Assess adoption separately. Consider whether implementation is economically and organizationally feasible, and whether regulation or responsibility limits it. An exposure index by itself does not answer these questions.
- Check labor-market outcomes. If the claim concerns jobs changing or disappearing, examine employment, wages, hiring, and job transitions. Technical capability is not a substitute for evidence of those outcomes.
Why published rankings can disagree
Different studies may rank occupations differently because they define exposure differently. Before comparing scores or headline percentages, check the method behind each one.
| Comparison point | What to check |
|---|---|
| Technology | Whether the measure covers LLMs or generative AI, broader AI capabilities, robotics, or combined systems. |
| Exposure definition | Whether it counts task completion, time saved, capability overlap, or another measure. |
| Time horizon | Whether the estimate concerns current capability or near-future systems and tools. |
| Task and occupation data | The source and date of task descriptions, country, occupation classification, and level of detail. |
| Aggregation | Whether the result is a task share, threshold, mean score, or capability gap—and whether variation within an occupation is represented. |
| Outcomes | Whether the study measures technical possibilities only or also employment, wages, and transitions. |
The ILO’s 2026 brief notes that exposure results vary with the measure used. It also cautions that indices rely on static task descriptions, embed subjective assumptions, and omit adoption constraints. Two headline percentages should not be compared as if they measured the same thing unless their definitions and scope align.
Rank #2
What current estimates do—and do not—show
The ILO’s 2025 global index estimates that one in four workers worldwide are in an occupation with some degree of generative-AI exposure. That is an exposure estimate, not a forecast that one in four workers will lose their jobs.
In the same 2025 index, 3.3% of global employment falls into the highest exposure gradient. The ILO reports 4.7% of female employment and 2.4% of male employment in that gradient; the differences vary by country income. The gradient describes the index’s exposure classification, not a predicted redundancy rate.
The ILO also reports a mean automation score of 0.29 in 2025, compared with 0.30 in 2023, and a standard deviation of 0.14, compared with 0.30. These figures describe the score distribution under an updated methodology; they are not changes in realized employment.
Measures also differ in what capabilities they consider. The OECD’s 2026 measure maps AI capabilities across nine cognitive, social, and physical domains to occupational requirements. It is a forward-looking exposure measure, not an observed job-loss estimate.
Which kinds of work appear exposed?
Clerical work features prominently in the ILO’s 2025 analysis. Newer capability-based measures also identify exposure in professional and cognitive fields, including business, finance, computing, and education. The precise ranking depends on the technology, task data, and method, so these examples should not be read as a universal list of jobs at risk.
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This differs from a simple assumption that automation mainly affects lower-skilled routine work. Generative AI’s ability to handle language and other cognitive tasks means that some professional activities can also overlap with AI capabilities. That overlap still says nothing by itself about whether an employer will adopt the technology or reduce staffing.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Exposure is not a job-loss forecast
An occupation can contain tasks that AI handles well and other work that remains difficult to automate or requires a person to review, decide, interact, or take responsibility. The ILO’s 2025 working paper says: “As most occupations consist of tasks that require human input, transformation of jobs is the most likely impact of GenAI.” This is a broad assessment, not a guarantee about what will happen in any one workplace.
To evaluate likely effects, look beyond exposure rankings for evidence of actual changes in employment, wages, hiring, and worker transitions. Treat an index as an initial description of technical possibility, not a verdict about an occupation’s future.
Quick Recap
Sources
- ILO, “Generative AI and jobs: A 2025 update” (20 May 2025)
- ILO, “Generative AI and Jobs: A Refined Global Index of Occupational Exposure” (20 May 2025)
- ILO, “New ILO brief explains what AI exposure indicators reveal about jobs” (17 April 2026)
- OECD, “The OECD AI exposure measure: Mapping the OECD AI Capability Indicators to occupations” (2026)
- OECD, “Beyond automation: Decoding the impact of Generative AI on regional labour markets” (2024)
- Anthropic, “Can we predict the jobs robots will do?” (2026)
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