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Careers built around physical presence, hands-on work, and tasks that are difficult to digitize tend to have lower exposure to current generative AI (GenAI) than careers dominated by language, information processing, and other digital tasks. In the OECD’s cross-country comparison using 2022 data, lower-exposure occupational groups include cleaners and helpers; agriculture, forestry and fishery workers; food preparation assistants; labourers; and refuse and other elementary workers. These are broad groups, not a ranked list of individual jobs or a guarantee that any career is protected from automation.
The key distinction is between exposure and job loss. Exposure estimates how much a job’s tasks overlap with what a technology could do or assist with. They do not establish whether employers will adopt that technology, whether jobs will disappear, or whether workers’ duties will instead change.
What “least exposed” means
In this context, “least exposed to AI automation” means that the tasks in an occupation have relatively little overlap with the capabilities of the AI technology being measured. For GenAI, that generally means less overlap with work that can be done through digital inputs and outputs, such as generating or processing text and other information. Work requiring someone to be physically present, handle materials, respond to changing conditions on site, or complete manual tasks is often less directly affected by today’s GenAI systems.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsThat is a relative comparison, not a promise of job security. An exposure score is not a forecast that a worker will be replaced, and a low score does not mean that a job cannot change. The International Labour Organization (ILO) says its index estimates potential effects on existing occupations; it does not predict the future or settle what employment outcomes will follow. Its methodology is explained in the ILO’s overview of occupational exposure.
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It is also important to specify which technology is being discussed. GenAI is one kind of automation. Robotics, specialized machinery, and other technologies can affect physical tasks that are less exposed to GenAI. A career can therefore have low exposure to current GenAI and still face change from other forms of automation.
Occupational groups the OECD identifies as lower exposure
The OECD’s comparison of average relative exposure across countries, based on 2022 data, lists the following broad groups among those least exposed to AI:
- Cleaners and helpers
- Agriculture, forestry and fishery workers
- Food preparation assistants
- Labourers
- Refuse and other elementary workers
These categories are useful starting points for understanding the kinds of work that may have less direct overlap with AI capabilities. They are not a definitive ranking of every job title, and they should not be read as career recommendations. A broad occupational group can contain varied duties; a particular role’s exposure depends on what the worker actually does and how that work is organized. The OECD’s comparison appears in its report component on AI, work and digital societies.
Nor should “least exposed to AI” be translated into “least exposed to automation overall.” The OECD’s broader assessment of automation includes technologies beyond AI and uses a different dataset and time frame. In that broader framing, some physical occupations—including roles in construction and extraction, farming, fishing and forestry, and some production and transportation work—can be more susceptible to automation. The apparent contrast is not a contradiction: GenAI and physical automation target different kinds of tasks.
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Why physical work often has lower GenAI exposure
GenAI systems are most directly relevant when a substantial part of a job can be represented and handled digitally. A system may assist with drafting, summarizing, classifying, or analyzing information, for example, if the relevant information is available in a form it can process. By comparison, cleaning a changing physical environment, handling refuse, tending crops, or preparing food in a real workplace involves tangible surroundings and actions that a text-generating system does not itself perform.
This distinction is about task overlap, not whether a job requires skill or judgment. Physical work can involve planning, communication, problem-solving, safety decisions, and recordkeeping. Some of those components may be more amenable to digital assistance than the hands-on work itself. A worker in a lower-exposure occupation may therefore encounter AI in a limited part of the role, even if the occupation’s overall task mix has little overlap with GenAI.
The ILO’s 2025 refined index makes this task-level variation explicit. It considers both average exposure across an occupation’s tasks and how much exposure varies from task to task. An occupation with a low average can still include particular tasks with higher exposure. That is why a job title alone is an imperfect guide: two roles with the same title may have different task mixes, digital tools, and proportions of administrative work.
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Several numbers describe how many workers or occupations may be exposed, but they measure different things. They should not be combined into a single ranking or treated as counts of jobs likely to disappear.
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| Source and measure | What it reports | How to interpret it |
|---|---|---|
| ILO, refined global index, 2025 | One in four workers globally are in an occupation with some degree of GenAI exposure; 3.3% of global employment is in the highest exposure category. The highest-category shares are 4.7% of female employment and 2.4% of male employment. | These are exposure classifications, not observed job losses. “Some degree” and “highest category” refer to different levels of exposure. |
| ILO, exposure gradients, 2025 | One of the four exposure gradients covers 34% of employment in high-income countries and 11% in low-income countries. | The geography and exposure categories matter; these percentages do not describe every country or predict adoption. |
| OECD, GenAI exposure, 2022–2024 | About one quarter of workers across OECD countries were exposed, using a definition of at least 20% of tasks amenable to AI assistance. | This is an OECD-country estimate with a specific task threshold, not the same measure as the ILO’s global estimate. |
The ILO’s figures come from its 2025 update and refined index publications: Generative AI and jobs: A 2025 update and the refined global index of occupational exposure. Its analysis covers 436 detailed ISCO occupations. In constructing the refined index, researchers used a Polish occupational classification as a sampling frame listing 29,753 tasks and collected worker input on 2,861 tasks.
The 2025 index reports a mean automation score of 0.29, compared with 0.30 in the 2023 index; its standard deviation is 0.14, compared with 0.30 in 2023. Those figures describe the index estimates and their distribution, not a measured change in job losses or a direct probability that an individual worker will be replaced. The updated methodology produced a more concentrated spread of estimates; a small change in the mean should not be mistaken for evidence that employment outcomes barely changed or that particular jobs are secure.
The different employment shares also illustrate why definitions matter. The ILO’s global estimate of workers with some degree of exposure is not interchangeable with the OECD’s estimate for workers meeting its threshold of at least 20% of tasks amenable to AI assistance. The organizations use different populations, classifications, periods, and methods. As the ILO’s 2026 discussion of what exposure indicators can and cannot tell us emphasizes, susceptibility indicators do not by themselves establish adoption or employment effects.
Low exposure does not mean low risk from every technology
For a person choosing a career, “Will GenAI do these tasks?” is only one part of the automation question. Physical tasks may be less suited to GenAI yet more susceptible to machinery, robotics, or other forms of automation. That broader risk can depend on whether a task is repetitive, whether it can be performed reliably by equipment, and whether adoption is practical in the workplace. The OECD’s wider automation assessment treats AI and other technologies together, but it differs from its GenAI comparison in both data and time frame; its results should be considered separately rather than merged into one score.
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There are also limits to adoption. The ILO notes that infrastructure, digital skills, costs, and operational difficulty can constrain the deployment of technology. Those conditions vary across countries and workplaces, so a task that is technically amenable to AI assistance may not be automated in practice. Conversely, a low current exposure estimate cannot rule out future changes in technology or how employers redesign work.
The ILO’s 2026 brief also finds that manual, care, and craft occupations may be less exposed directly and have fewer exposure-related spillovers through occupational networks. This is still a measure of susceptibility, not a guarantee. It does not mean that every care or craft role has the same exposure, or that wages, demand, working conditions, or other forms of technological change are settled by an exposure estimate.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess a career rather than just its title
If you are comparing possible careers, examine the work itself and the context in which it is done. A useful assessment asks four separate questions:
- Which technology is being measured? A GenAI estimate concerns overlap with generative AI capabilities. A broader automation estimate may include robotics and other technologies. Do not treat one as a proxy for the other.
- What tasks fill the working day? Look at the actual mix of hands-on duties, information processing, communication, recordkeeping, and other responsibilities. An occupation’s average score can conceal tasks with very different exposure.
- How current and comparable is the source? Check the estimate’s year, occupational classification, and method. The OECD’s named lower-exposure groups are from its cross-country comparison using 2022 data; the ILO’s refined global index was published in 2025 and classifies detailed occupations using task-level evidence.
- What is the local adoption context? Infrastructure, digital skills, cost, and the practical difficulty of deploying a technology affect whether potential assistance becomes workplace adoption. Country-level averages cannot tell you exactly what will happen at a particular employer.
Then consider questions an exposure index is not designed to answer: what qualifications you need, whether the day-to-day conditions suit you, whether employers in your area are hiring, and what training costs or time commitments are realistic. The OECD’s occupational groups identify lower relative AI exposure; they do not establish local demand, earnings, or personal fit.
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What exposure estimates can—and cannot—tell you
An exposure index is most useful as a way to compare the kinds of tasks that may be affected by a particular technology. It can help explain why a digital, text-heavy role may have more direct overlap with GenAI than work that depends on physical presence. It can also highlight variation inside occupations instead of treating every worker with the same job title as identical.
It cannot show, on its own, whether employers will adopt a system, whether workers will be displaced, how much a role will change, or whether employment will grow or shrink. Those outcomes depend on factors beyond task capability, including adoption barriers, institutions, demand, and how work is reorganized. The ILO distinguishes exposure from employment effects in its explanation of GenAI’s possible effects on employment.
The most defensible takeaway is therefore comparative: the broad OECD groups named above have lower relative exposure to AI in its 2022 comparison, especially when work is centered on physical tasks rather than digitized information processing. That does not make any occupation “AI-proof,” and it does not settle its vulnerability to other automation. For a career decision, use exposure as one input alongside the actual task mix, local conditions, training requirements, and employment prospects.
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