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Clerical and administrative jobs are the clearest high-exposure group for generative AI, especially work involving data entry, typing, bookkeeping and routine document handling. Digitized professional and technical work—including financial analysis, programming, and web and multimedia development—is also increasingly exposed. Exposure means that AI may be able to assist with or perform some tasks; it is not a prediction that a worker will lose a job. For most people, the practical response is to identify which parts of their work may change, learn relevant tools, and strengthen skills that help them apply, check and complement AI.
What does “exposed to AI” mean?
An exposure measure asks how closely AI capabilities match tasks associated with an occupation, or whether observed AI interactions correspond to those tasks. It does not tell you whether an employer will adopt a tool, whether using it will be economical, or what the net effect on hiring and employment will be.
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The International Labour Organization’s 2025 index assesses tasks rather than declaring whole occupations replaceable. It covers 436 detailed occupations and applies scores to labor-force survey data for more than 140 countries. The ILO concludes that transformation is more likely than wholesale redundancy because most occupations include tasks requiring human input.
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Which jobs are most exposed?
Clerical and administrative work
Clerical occupations remain the clearest high-exposure cluster in the ILO’s 2025 analysis. Examples include data entry clerks, typists, accounting and bookkeeping clerks, and administrative secretaries. These roles can include repeatable, text- or data-heavy tasks that generative AI may assist with, such as drafting, sorting, summarizing or processing information. That does not mean every task in these jobs can be automated, or that every employer will use AI to do it.
Digitized professional and technical work
The ILO also identifies increased exposure in some professional and technical roles as generative AI capabilities expand. Examples include financial analysts, investment advisers, application programmers, and web and multimedia developers. In these occupations, AI may affect parts of analysis, coding or content production while leaving important work in problem definition, verification, client communication, judgment and accountability.
These examples describe task exposure, not a ranking of who is most likely to be unemployed. A role’s actual mix of responsibilities and its employer’s choices matter more to an individual than its occupation label alone.
What do the available figures say—and what do they not say?
| Finding | What it measures | How to interpret it |
|---|---|---|
| One in four workers worldwide | ILO 2025 estimate of workers in occupations with some degree of generative-AI exposure | Potential exposure, not the share expected to lose jobs. |
| 3.3% of global employment | ILO 2025 share in the highest exposure gradient | A small highest-gradient category does not mean the remaining jobs are unaffected; it is not a job-loss rate. |
| 34% in high-income countries; 11% in low-income countries | ILO 2025 share of total employment potentially exposed | Global exposure varies with country context; these group figures are not estimates for a particular country or worker. |
| 4.7% of female employment and 2.4% of male employment globally | ILO 2025 share in the highest exposure category | The difference is an aggregate distribution, not an individual risk estimate. In high-income countries, the corresponding shares are 9.6% for women and 3.5% for men. |
| About one-third of vacancies | OECD 2024 analysis of online vacancies in ten countries, in occupations its framework classified as highly AI-exposed; country shares ranged from 31% in Austria to 45% in the United Kingdom | This is a vacancy-data finding, not a global census or a measure of jobs lost. |
The ILO’s 2025 index also reports an average automation score of 0.29, compared with 0.30 in its 2023 index, and a standard deviation of 0.14, compared with 0.30. These are index scores, not percentages of jobs. The revised index uses a four-gradient framework and updated task-level evidence, so the figures should not be read as a simple count of jobs becoming safer or more threatened.
Evidence on employment trajectories is a separate question. For the United States, the BLS projects employment from 2023 to 2033 to grow 17.9% for software developers and 17.1% for personal financial advisors, while projecting a 4.4% decline for claims adjusters, examiners and investigators. These are projections for selected occupations susceptible to potential AI impacts, not estimates of changes caused by AI. Exposure can coexist with projected growth or decline for many reasons.
Which skills can help workers adapt?
There is no universally “AI-proof” credential or occupation. A more useful aim is to combine field knowledge with skills that help you use tools thoughtfully, assess their output and handle the work they cannot reliably do alone.
An OECD analysis of online vacancies across Austria, Belgium, Canada, Czechia, France, Germany, the Netherlands, Sweden, the United Kingdom and the United States found that management skills appeared in 72% of vacancies in highly exposed occupations, and business skills in 67%, in 2021–22. The figures are vacancy shares, not the percentage of workers who need a particular qualification. The analysis also found demand for emotional, digital and social skills increased by approximately 15% in highly exposed occupations over its study period. The OECD notes that broader digitalization and structural shifts may also contribute; the trend cannot be attributed to AI alone.
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- Critical evaluation: Check AI-generated information, calculations, code or recommendations against reliable evidence and domain knowledge.
- Communication and social skills: Clarifying needs, working with colleagues and customers, and building trust remain valuable alongside automated workflows.
- Business and management skills: Project coordination, process improvement and people management can complement technical expertise.
- Problem-solving and domain judgment: Use experience to decide what question to ask, whether an output makes sense and when a human decision is required.
Prioritize skills that fit your existing expertise and appear in vacancies in your area. The OECD findings describe patterns in online postings across ten countries, not a guaranteed hiring outcome or one prescribed course of study.
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How can you make a practical adaptation plan?
- Map your tasks. Write down what you do in a typical week. Mark work that is repetitive, text- or data-heavy, already handled with software, or dependent on physical presence, interpersonal service, changing conditions, judgment or accountability.
- Separate assistance from substitution. For each task that might be AI-assisted, ask what the tool could draft, summarize, sort or analyze—and what still requires you to verify, decide, communicate or take responsibility.
- Practice with a relevant tool in a low-risk setting. Start with non-sensitive material and tasks where mistakes are recoverable. Check outputs rather than treating them as authoritative, and follow workplace rules for confidential information and consequential decisions.
- Build complementary skills deliberately. Pair digital capability with your subject knowledge, critical evaluation, communication, customer interaction, collaboration or project and people management, choosing based on actual job requirements rather than a generic “AI-proof” label.
- Check local evidence and revisit your plan. Review job openings, wage information, hiring patterns and training options in your region, and watch for concrete changes in your employer’s tools and workflows. Update your task map as work changes; an exposure score alone cannot show whether demand for your role is rising or falling.
- Ask for a supported transition. If AI is changing your role, discuss training time, tool access, review responsibilities and how workers will be involved in implementation. The ILO emphasizes social dialogue and targeted transition support rather than treating adaptation as an individual burden alone.
How should you compare career options?
Do not choose or leave a career based only on a global exposure ranking. Compare the work and the local opportunity, using the same questions for each option:
- Task mix: How much of the work is repeatable digital processing, and how much depends on human interaction, physical activity, judgment or accountability?
- Kind of evidence: Is a claim based on theoretical task capability, observed AI interactions, or employment outcomes? Those measures answer different questions.
- Human contribution: Which tasks could AI assist, and where do verification, domain judgment, trust, communication or responsibility remain central?
- Local trajectory: What do current vacancies, wages, hiring and occupational transitions show where you plan to work?
- Transition support: Is there employer-provided training, time to learn and a way for affected workers to influence implementation?
For a U.S. career decision, BLS employment projections can provide one view of occupational trajectories, but they are not AI-causation estimates. For other countries, use local labor-market and training information; global ILO figures and the OECD’s ten-country vacancy study cannot substitute for occupation-specific local evidence.
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