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Assess your work task by task, not by job title. List what you do, how often you do it, and how much time it takes; then check whether AI can handle part of each task, how easily you can verify its output, and whether your workplace could realistically use it. This can help you identify where your work may change. It cannot predict whether you personally will lose your job.
Why assess tasks instead of your job title?
A job is a bundle of tasks, and people with the same title can spend their time very differently. An occupational exposure score is useful background, but it does not tell you exactly what will happen in your role or workplace. The International Labour Organization (ILO) assesses potential automation across detailed tasks and aggregates those assessments for occupations; its framework is a reason to look inside the job, rather than treat a title as a verdict. The ILO explains its task-level approach, and the OECD discusses the difference between AI exposure and automation in Skills in the AI Age.
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How to audit your own tasks
- Write down the work you actually do. Include recurring activities such as drafting, summarizing, searching, classifying or routing information, producing routine content, and responding to customer or coworker requests. Also include less frequent work if it takes substantial time or has serious consequences. OECD analysis and the ILO’s task-level explanation both emphasize that exposure can vary among tasks within an occupation.
- Record frequency and time. Estimate how often you do each task and roughly how much time it takes over a typical week or month. This helps you focus on work that matters to your day: a task may be technically exposed but rare, while a frequent task may deserve attention even if AI could handle only part of it. This is a practical prioritization method, not an official ILO or OECD score.
- Check whether current AI can do part of it. Ask whether the task uses information that can be provided to a system and produces digital material—such as text, a summary, a classification, or a response—that AI can generate or transform. Keep the question specific: could a system perform a defined part of this task using the information actually available to it? The ILO’s assessment evaluates potential at task level before aggregating results across occupations. Read the ILO overview.
- Judge how much human checking and context the task needs. Consider whether the inputs and success criteria are clear, whether you can check the output against a reliable source or rule, and what happens if it is wrong. Note whether the task involves exceptions, judgment, live interaction, physical action, trust, or accountability for a consequential decision. These considerations can help distinguish a task AI might assist with from one an employer might try to automate; they are not a validated prediction formula. The ILO and OECD describe broader human, social, legal, technical, and economic constraints on automation. See the ILO index study and OECD task analysis.
- Consider what your employer can actually adopt. A system may be technically capable of doing a task, yet not be usable in your workplace. Ask whether it can access needed information, fit existing systems and processes, produce reliable results, and meet the organization’s cost and risk requirements. Technical exposure does not establish that an employer has deployed AI or will do so. The ILO’s discussion of exposure indicators and the OECD analysis explain why capability and real-world outcomes are different questions.
- Describe the likely change at task level. If AI could take on part of a task, identify what a person might still need to do: review the result, handle unusual cases, apply judgment, coordinate with others, or take responsibility. “This part of my work may change” is a more grounded conclusion than assuming an entire occupation will disappear. The ILO’s 2025 update and OECD analysis address task change and automation.
A worksheet for comparing tasks
Use one row for each task. The worksheet organizes questions; it does not calculate a published or validated risk score.
| Task | Frequency and time | Inputs and output | Repeatability and success criteria | Review and human role | Workplace adoption |
|---|---|---|---|---|---|
| What activity do you perform? | How often, and approximately how long does it take? | Are the needed inputs and expected output digital? | Are the steps repeatable and is a good result clearly defined? | How easy is checking? How much judgment, exception handling, interaction, physical action, trust, or accountability is involved? | Could your employer connect AI to the necessary information and workflow at acceptable cost and risk? |
After filling it out, prioritize tasks that take meaningful time and recur often, then look closely at capability, reviewability, and adoption. Keep those dimensions separate: frequency and time help you decide what matters to your work, while capability and workplace feasibility address different reasons a task may or may not change.
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What occupation-level evidence can—and cannot—tell you
The ILO’s 2025 update assesses exposure using nearly 30,000 tasks, human and expert input, and AI-assisted prediction. It groups occupations into four exposure gradients based on average exposure and variation among tasks. That variation matters: an occupation can include tasks with higher potential for automation alongside others that remain less exposed. The ILO’s 2025 update, its working paper, and its explainer describe the method and gradients.
The ILO reports a mean automation score of 0.29 in 2025, compared with 0.30 in 2023. It estimates that one in four workers worldwide are in an occupation with some degree of generative AI exposure, while 3.3% of global employment falls in the highest exposure category. These are modeled exposure estimates, not observed job losses or forecasts for an individual worker. The ILO identifies clerical occupations as having the highest exposure in its assessment; some highly digitized professional work, including media-, software-, and finance-related roles, has also seen increased exposure. The ILO’s 2025 update and its working paper provide the estimates and occupational findings.
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Separately, OECD analysis found that about a quarter of workers across OECD countries were exposed to generative AI under that analysis’s definition: at least 20% of their tasks were amenable to AI assistance in 2022–2024. The OECD and ILO figures use different definitions, scopes, and methods; they should not be treated as readings from one common scale. OECD, Skills in the AI Age.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Does AI exposure mean my job will disappear?
No. Exposure describes potential susceptibility under a particular method; it is not evidence that an employer has adopted AI, that a worker has been displaced, or that a job will disappear. Indicators can rely on static task lists and judgments from experts, workers, or AI systems, and may not capture economic feasibility or institutional barriers. They do not, by themselves, establish what will happen to employment, wages, or demand. The meaning of a score also depends on the method, geography, occupational classification, and AI systems assessed. The ILO’s limits on exposure indicators and the OECD analysis explain these distinctions.
Even when a task is exposed, a role may still involve checking AI output, handling unusual cases, exercising judgment, coordinating people, or taking responsibility. The useful question is therefore not only whether AI can perform a task, but how that task and the surrounding work might be reorganized in your workplace.
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