Clerical jobs remain among the most exposed to generative AI (GenAI), but exposure is not limited to office administration: some highly digitized professional and technical roles are also increasingly affected. The most useful way to assess your own situation is to examine the tasks you do, not treat an occupation-wide ranking as a prediction that you will lose your job.
Which jobs are most exposed to AI automation?
The International Labour Organization’s 2025 global index identifies clerical work as the occupational group with the highest exposure to GenAI. Examples include data-entry clerks, typists, accounting and bookkeeping clerks, and administrative secretaries. The ILO also reports increased exposure among some digitized professional and technical roles, including financial analysts, web and multimedia developers, application programmers, and investment advisers. ILO, 2025 global index; ILO, 2025 interview
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These categories point to tasks that current GenAI capabilities may be able to perform or assist with; they do not establish that every worker in those occupations faces the same risk. A job title can contain a mix of automatable digital tasks and work requiring judgment, accountability, physical presence, or interaction with people.
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What the global figures show
- One in four workers globally are in an occupation with some degree of GenAI exposure, according to the ILO’s 2025 estimate.
- 3.3% of global employment falls into the index’s highest exposure category.
- The ILO estimates exposure for 34% of employment in high-income countries and 11% in low-income countries. Differences in occupational mix and income group affect this comparison; it is not an individual worker’s probability of displacement.
The ILO/NASK index draws on nearly 30,000 tasks and combines worker input, expert review, and AI-assisted predictions. Its four exposure gradients describe potential task-level exposure aggregated to occupations—not observed job losses or a forecast for a particular person. ILO, 2025 global index
How to assess your own job’s exposure
Use this checklist to make a practical, task-based assessment. It is an editorial framework informed by task-level research, not a validated calculator or a probability of losing your job.
- List recurring tasks. Focus on work that takes a meaningful share of your week, rather than occasional duties or your job title alone.
- Identify digital inputs and outputs. Flag tasks centered on text, records, routine analysis, standard communications, or other information that can be handled digitally.
- Check what current tools can do. For each flagged task, ask whether an available GenAI system could complete it or materially speed it. Then note what checking, correction, or accountability a person must still provide.
- Mark the work that depends on people or the physical world. Separate tasks requiring physical presence, nuanced human interaction, context-sensitive judgment, or responsibility for consequential decisions.
- Estimate the share of your week affected. A few exposed tasks do not mean the whole job can be automated. Consider how much working time those tasks represent and whether your employer is adopting AI or redesigning workflows.
- Identify skills that support changing work. Domain knowledge, evaluating AI output, and handling human-facing or accountable responsibilities can matter as tasks change. Look for employer-supported development where available.
What AI exposure does—and does not—mean
Exposure measures the potential for technology to affect tasks. It is not the same as employer adoption, full automation, redundancy, or unemployment. The ILO’s 2025 summary says transformation is more likely than outright replacement for most jobs. ILO, 2025 brief
Whether technical potential changes a workforce depends in part on employer decisions to adopt AI and whether workers have opportunities to learn and adapt. ILO Senior Researcher Paweł Gmyrek explains: “Employment statistics usually react slowly, while exposure – measured through the automation potential of tasks across occupations – gives us a clearer sense of the transformations likely to occur in the mid-term.” ILO interview
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How to compare AI-exposure studies
Different studies may use “exposure” to mean different things. Before comparing rankings or figures, check what each study measures and whom it covers.
| What to check | Why it matters |
|---|---|
| Unit assessed | A study may assess individual tasks, an occupation’s average tasks, an industry, or regional employment. |
| Definition of exposure | It may measure task automation potential, possible time savings, or overlap between tasks and AI capabilities. |
| Scale and threshold | Categories and scores are not interchangeable. Check the underlying definition before comparing labels. |
| Geography and year | A global estimate, a country sample, and a regional estimate describe different populations and periods. |
| Potential versus outcome | Technical capability is different from observed employer use, workflow redesign, job loss, or employment change. |
| Who is affected | Exposure can vary by gender, income group, occupation, and local industry mix. |
For example, the OECD’s 2024 regional analysis estimates that 5% of workers in EU agriculture and 71% in information and communications are exposed to GenAI under that report’s definition and analytical scope. Those sector estimates are not universal probabilities for workers in those industries. OECD, 2024 regional analysis
A separate OECD 2024 policy brief uses online vacancy data and AI-exposure measures across ten OECD countries to examine changes in skill demand. Its approach and study population differ from the ILO index; do not combine their figures into a single ranking without aligning methods, thresholds, geography, and years. OECD, 2024 policy brief
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