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To tell which work is most exposed to AI automation, assess the tasks in a job against a clearly defined AI capability, then state how task scores are combined. Treat the result as a measure of potential exposure—not a prediction that the job will disappear. Adoption, workplace changes, regulation and demand all affect what happens in practice.
Start with tasks, not job titles
A job title bundles together different kinds of work. A role may include drafting, data handling and summarizing alongside judgment, customer interaction, care, physical tasks and responsibility for decisions. AI may be able to perform or speed up some of those tasks without replacing the whole role.
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Write down what workers actually do, including routine information processing, communication, analysis, judgment, care, physical handling and accountability where relevant. Then assess each task separately. This makes it possible to distinguish work that a system can complete from work where it can only help with one step.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallDefine what “AI” and “exposure” mean
There is no single AI exposure score that answers every question. Language models, broader AI systems and physical robots have different capabilities and affect different task bundles. Before comparing results, specify the technology and whether the estimate concerns current tools or a plausible near-future scenario.
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- Technology: Is the assessment about generative AI or language models, broader AI capabilities, physical robotics, or combined systems?
- Capability: Does a task count as exposed if AI can complete it, assist with it, or merely speed up part of it?
- Human review: Does the estimate assume a person checks the work or takes responsibility for the result?
- Time horizon: Is the measure about what tools can do now or what they might do as capabilities develop?
Definitions can materially change the result. For example, an OECD 2024 framework defines exposure using tasks an LLM could complete in half the time. That is a specific threshold, not a universal definition of exposure. The OECD explains its framework in Beyond automation: Decoding the impact of Generative AI on regional labour markets.
Turn task assessments into an occupation measure
After evaluating tasks, ask how a study aggregates them into an occupation-level result. A score might represent the share of tasks above a threshold, an average exposure score or a gap between AI capabilities and occupational requirements. These measures are not interchangeable.
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Aggregation can hide important differences inside an occupation: workers with the same title may spend different amounts of time on different tasks, and an average can obscure both highly exposed activities and work that still depends on human input. Look for a method that preserves task variability rather than relying on an unexplained single “risk score.”
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Check the data and assumptions before comparing rankings
Two studies can rank the same occupations differently without either being wrong: they may assess different systems, tasks, time horizons or geographies. Before putting results side by side, check the following:
- Technology and capability: What system is assessed, and what does the study count as exposed?
- Task and occupation data: Where do task descriptions come from, how recent are they, and how are occupations classified?
- Geography: Is the estimate global or tied to one country’s workforce and job taxonomy? A result based on U.S. O*NET tasks, for example, should not automatically be treated as a universal ranking.
- Aggregation: Is the occupation score a task share, average, capability gap or another measure? Does it account for variation within occupations?
- Time horizon: Does the measure describe current capability or include near-future systems?
- Outcomes: Does the study measure technical potential only, or does it also examine employment, wages, hiring or job transitions?
Do not compare headline percentages as though they measure the same thing when these choices differ. The ILO’s 2026 brief notes that exposure results depend on the measure; it also cautions that indices use static task descriptions, embed subjective assumptions and omit adoption constraints. Its explanation, New ILO brief explains what AI exposure indicators reveal about jobs, offers guidance on interpreting these measures.
What current exposure figures do—and do not—show
The ILO’s 2025 global assessment estimates that one in four workers worldwide are in an occupation with some degree of generative AI exposure. It places 3.3% of global employment in its highest exposure gradient. Those are exposure estimates, not projected displacement rates. The same paper reports that the highest gradient covers 4.7% of female employment and 2.4% of male employment; the differences vary by country income. These figures reflect the ILO’s global index and occupational classification, not a forecast for every country or workplace. The paper is Generative AI and Jobs: A Refined Global Index of Occupational Exposure.
The ILO also reports that its index’s mean automation score was 0.29 in 2025, compared with 0.30 in 2023, while the standard deviation was 0.14 compared with 0.30. These are changes in the score distribution under an updated methodology—not changes in realized employment. A shift in an index is not evidence by itself that workers gained or lost jobs.
High exposure is not limited to work traditionally described as low-skill or routine. The ILO identifies clerical work as highly exposed, while newer capability-based measures also find exposure in professional and cognitive fields such as business, finance, computing and education. Which occupations rank highest depends on the measure used; historical rankings focused on routine-task automation should not be carried over automatically to generative AI.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Keep technical exposure separate from job loss
An AI system’s ability to perform a task does not establish that an employer will adopt it or eliminate a position. Economic feasibility, organizational change, workflow redesign, regulation, responsibility for errors and changes in demand can all affect whether technical capability changes hiring or employment.
The ILO’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.” For claims about jobs changing or disappearing, pair capability indicators with observed evidence on employment, wages, hiring and job transitions. The ILO’s 2025 working paper also notes: “As most occupations consist of tasks that require human input, transformation of jobs is the most likely impact of GenAI.”
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A practical way to assess a role
- Set the scope. Name the geography, occupation classification, AI technology and time horizon. Make clear whether physical robotics is included.
- List the work. Break the role into tasks rather than inferring exposure from its title. Include both information-based and physical or interpersonal duties.
- Assess each task. Record whether the specified AI can complete it, accelerate it or assist only one step. State assumptions about human review and responsibility.
- Choose and explain an aggregation. Say how task results become an occupation-level measure, and preserve variation where the underlying method allows it.
- Assess adoption separately. Consider whether use is economically feasible and organizationally possible, along with relevant regulatory or institutional barriers.
- Verify real-world effects. If the question is about job loss or change, look for observed labor-market evidence rather than treating an exposure score as the outcome.
Include robotics only when it fits the question
Physical robots and language models should not be treated as the same technology. A robotics measure considers physical tasks and robot capabilities; an LLM-focused index may center on information or cognitive work. If “AI automation” is intended to cover both, state that scope and keep their task profiles distinct. Anthropic describes a physical-task approach in Can we predict the jobs robots will do?.
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