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Work built around physical presence and manual tasks tends to have less overlap with today’s generative AI than digitized, language-heavy, administrative, analytical, and professional work. In an OECD comparison using 2022 data, lower-exposure occupational groups included cleaners and helpers; agriculture, forestry and fishery workers; food preparation assistants; labourers; and refuse and other elementary workers. These are broad categories, not a definitive ranking of individual jobs—and lower exposure is not a promise that a career is safe from automation or job change.
Which occupational groups have lower exposure to generative AI?
The clearest named examples in the available cross-country comparison are broad occupational groups rather than precise job titles. The OECD’s 2022 comparison places the following among the groups with the lowest average relative exposure to AI:
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- Cleaners and helpers
- Agriculture, forestry and fishery workers
- Food preparation assistants
- Labourers
- Refuse and other elementary workers
The comparison is useful for spotting a general pattern: work that depends on being physically present and carrying out manual tasks has less direct overlap with current generative AI capabilities than work centered on producing or processing digital information. It does not establish a precise order among these groups, or show that every job inside one group has the same exposure. The categories and data are described in the OECD report component on AI, work and digital societies.
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Think of these as examples for understanding task exposure, not as career recommendations. The comparison does not assess local hiring demand, earnings, training requirements, working conditions, or whether a particular person is suited to the work.
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What “least exposed” means—and what it does not
Exposure is about tasks, not a prediction of job losses
An exposure estimate asks how much a job’s tasks overlap with what a technology could potentially assist with or perform. It does not say that an employer will adopt the technology, that a worker will be replaced, or that employment in the occupation will shrink. Adoption depends on practical and economic conditions as well as technical capability; employers may use AI to change how work is done rather than eliminate a role.
The International Labour Organization (ILO) emphasizes this distinction: its exposure analysis estimates potential effects on occupations and employment, but employment outcomes cannot be read directly from an exposure score. Its explanation of the method says, “While it is not possible to predict the future – particularly as the technology is still evolving,” researchers developed and refined a methodology to estimate potential effects on existing occupations and, in a separate step, on employment. Read the ILO’s explanation of how generative AI may affect different occupations for the methodology and its limits.
Low GenAI exposure is not low risk from every kind of automation
Generative AI and broader automation are different subjects. Generative AI can affect tasks such as drafting, summarizing, or handling information. Robotics and other automation technologies can affect physical tasks. The OECD’s broader assessment of automation risk uses a different dataset, time frame, and technology scope from its 2022 comparison of AI exposure. Some physical occupations—including work in construction and extraction, farming, fishing and forestry, and some production and transportation roles—can be more susceptible under broader automation measures.
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That is not a contradiction. A job can have little overlap with current generative AI while still facing potential change from machinery, robotics, or other technologies. Do not combine the two kinds of measure into one ranking or treat a low GenAI exposure estimate as evidence that all automation risks are low.
How the main exposure studies differ
Different studies answer different questions. Their figures are not interchangeable: the year, definition of exposure, occupations covered, and technology being assessed all matter.
| Source and scope | What it measures | Useful figures and qualifications |
|---|---|---|
| ILO refined global index, published 2025 | Potential exposure to generative AI, estimated from occupational tasks. The analysis covers 436 detailed ISCO occupations and classifies exposure using average task exposure and variation across tasks. | One in four workers globally are in an occupation with some degree of GenAI exposure. The highest exposure category covers 3.3% of global employment: 4.7% of female employment and 2.4% of male employment. These are exposure estimates, not observed job losses. See the ILO’s 2025 update and Working Paper 140. |
| ILO exposure estimates by national income group, published 2025 | Share of employment falling into one of the index’s four exposure gradients. | The reported share is 34% in high-income countries and 11% in low-income countries. These figures describe the ILO’s exposure gradients, not the share of jobs expected to disappear. See the ILO methodology explanation and its 2025 update. |
| OECD cross-country comparison, 2022 exposure data | Average relative exposure to AI across countries; its broader automation-risk assessment is a separate measure. | In a related OECD estimate, about one quarter of workers across OECD countries were exposed to GenAI in 2022–2024, defined as having at least 20% of tasks amenable to AI assistance. That estimate uses a stated threshold and period; it is not a count of jobs lost. See the OECD report component. |
The ILO’s global results are based on a task-level approach, not a simple label assigned to a job title. Its 2025 update reports that researchers used the Polish occupational classification system as a sampling frame containing 29,753 tasks and collected worker input on 2,861 tasks. The resulting index combines task-level data, expert input, and AI predictions. The Polish task list is part of the method; it does not mean the headline global employment estimates describe only Poland.
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Why a single occupation can contain very different levels of exposure
An occupational average can hide substantial variation. A role may include tasks that could be assisted by AI alongside tasks that require physical presence, judgment in a changing environment, or interaction that is not captured by a task-level exposure score. The ILO framework therefore considers both average exposure and how much exposure varies across the tasks in an occupation. It uses four exposure gradients, from low to highest, as well as categories for minimal or no exposure.
The 2025 index’s mean automation score was 0.29, compared with 0.30 in the 2023 index; its standard deviation was 0.14, compared with 0.30 in 2023. These are index-level statistics reported by the ILO, not the probability that a worker or job will be automated. The lower spread in the refined index reflects a more concentrated distribution of scores under the updated method; it should not be interpreted as proof that employment has become safer.
This task variation matters when comparing two jobs with similar titles or when judging a particular position. A job description may include writing, recordkeeping, scheduling, customer communication, or analysis alongside hands-on work. Those information-processing tasks may have different exposure from the physical work around them. Conversely, a low average for an occupation does not mean every task in it has low exposure.
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Why exposure estimates differ by country
The ILO’s 2025 estimates report a higher share of employment in exposure gradients in high-income countries than in low-income countries. One reason is that economies differ in how much work involves digital and information-processing tasks. Infrastructure, digital skills, cost, and operational difficulty can also constrain whether a technically possible application is practical to adopt. Consequently, a global occupational estimate does not tell an individual worker how quickly a local employer will use AI.
Country, employer, and job design can all affect what changes first. Two workers with the same occupational label may use different tools, divide tasks differently, or work under different adoption conditions. Exposure figures are best used as broad signals for comparison—not as a personal forecast or a substitute for local labor-market information.
How to use exposure information when choosing or changing careers
If AI exposure is one factor in a career decision, use it as a screening question rather than a verdict. The studies reviewed here do not determine whether a career is a good personal choice, and a low exposure estimate does not establish strong demand or good working conditions.
- Identify the technology being measured. Check whether a figure concerns generative AI specifically or broader automation that includes robotics and other technologies. Do not use one as a proxy for the other.
- Compare the actual tasks. Look beyond the job title to the work performed day to day. Separate information-processing tasks from tasks requiring physical presence or manual activity, and note where duties vary among employers.
- Check the study’s date and occupational classification. The OECD’s named low-exposure examples come from a 2022 comparison. The ILO’s refined global index was published in 2025 and uses a task-level methodology. Different years and classification systems can produce results that are not directly comparable.
- Consider the place and likely adoption context. National income, infrastructure, digital skills, costs, and operating conditions can shape whether employers deploy a tool, even when some tasks are technically exposed.
- Evaluate career fit separately. Check the qualifications and training required, working conditions, local employment prospects, and whether the work suits your interests and circumstances. Exposure studies do not answer those questions.
For a current worker, the same process can help identify which duties may change and which parts of the role are not captured by a general occupational average. It can inform a conversation about training or workflow changes, but it cannot predict an employer’s plans. The ILO’s 2026 discussion of what exposure indicators can and cannot tell us also notes that manual, care, and craft occupations may be less exposed directly and have fewer exposure-related spillovers through occupational networks. That remains a measure of susceptibility, not a guarantee about future employment; see the ILO’s 17 April 2026 brief.
What the evidence can support
The strongest defensible conclusion is comparative: in the cited OECD analysis, several broad manual and elementary occupational groups had lower average AI exposure than more digitized, information-heavy work. The evidence does not support calling any career “AI-proof,” promising guaranteed earnings, or predicting that low-exposure jobs will avoid all forms of automation. It also does not establish a precise, current ranking of every job title.
Exposure indices are indicators of potential technological susceptibility. They do not fully capture adoption decisions, economic feasibility, institutions, or how employers and workers adjust. A practical career decision therefore needs both a careful reading of the exposure measure and separate evidence about the specific work and labor market a person is considering.
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