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To identify which tasks in your job are most likely to be automated by AI, assess the work you actually do—not just your job title. List each task, how often it happens, and how much time it takes. Then check whether AI can handle its digital inputs and outputs, how easily you can verify the result, what human judgment or interaction it requires, and whether your employer could adopt the technology in practice.
This is a way to assess which parts of your work may change. It is not a personal job-loss prediction: a task’s technical exposure to AI does not mean an employer will automate it or that an entire role will disappear.
Which tasks in my job are most likely to be automated by AI?
Tasks are more exposed when they involve digital information, repeatable steps, and outputs that current AI systems can generate or transform. But capability alone is not enough to determine whether a task will be automated. Verification, judgment, physical work, human relationships, accountability, and workplace constraints also matter.
Start with a task-by-task audit. The questions below are a practical framework, not a validated prediction formula or official risk score.
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1. List the work you actually do
Write down recurring tasks such as drafting, summarizing, searching for information, classifying or routing requests, producing routine content, and responding to customers or coworkers. Include less frequent duties too if they take substantial time or carry serious consequences. Jobs are bundles of tasks, and people with the same title may do different work.
2. Record frequency and time spent
For each task, note how often it occurs and roughly how much time it takes. This helps you prioritize: a task that is technically exposed but takes only a few minutes each month may have little effect on your workweek, while a frequent task that consumes hours may be worth examining even if AI can handle only part of it. This is a practical prioritization method, not an official ILO or OECD score.
3. Check whether AI can do the task’s core work
Ask whether current systems can perform any part of the task using the information available to them, and whether the inputs and expected outputs are digital. Consider how repeatable the steps are and whether the success criteria are clear. A task involving a standard digital document and predictable output may be easier to assist with than one that depends on incomplete information or changing circumstances.
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The International Labour Organization (ILO) assesses potential automation at the task level and aggregates those assessments across occupations. That approach is one reason to examine your actual duties rather than infer your exposure from a job title. Read the ILO’s explanation of how generative AI may affect occupations.
4. Ask what a person must still contribute
Consider whether the task requires live interaction, physical action, contextual judgment, exception handling, trust, or someone to take responsibility for the decision. Also ask how easily a person can verify the output against a reliable source, rule, or standard. These factors can help distinguish a task AI might assist with from one an employer could fully automate.
5. Check whether your workplace could adopt AI for it
A system may be technically capable of a task yet impractical to use at your workplace. It may not be reliable enough, may not have access to necessary data, may be costly to integrate, or may create unacceptable risks. Technical exposure measures do not establish whether an employer has deployed a system or will do so. The OECD likewise distinguishes exposure from actual automation and employment outcomes. See the OECD’s discussion of skills and AI exposure.
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Use a worksheet to compare tasks
Record the following for each task. Use the notes to identify work worth investigating, not to calculate a definitive probability of automation.
| What to record | Question to ask |
|---|---|
| Frequency and time | How often do I do this, and how much time does it take? |
| Inputs and outputs | Are the information and expected result digital? |
| Repeatability and criteria | Are the steps repeatable, and is a good result clearly defined? |
| Verification | Can a person check the result easily against a reliable source or rule? |
| Human contribution | How much judgment, exception handling, interaction, physical action, trust, or accountability does it require? |
| Workplace adoption | Could the employer integrate AI for this task reliably, affordably, and acceptably? |
What does current evidence say about AI exposure?
The ILO’s 2025 update assessed exposure using nearly 30,000 tasks, with human and expert input and AI-assisted prediction. It groups occupations into four exposure gradients based on average exposure and how much exposure varies across their tasks. The gradients describe different occupational profiles; a mixed-exposure occupation can include tasks with high potential alongside duties that are less exposed. Read the ILO’s 2025 update and its detailed index paper.
In that 2025 assessment, the ILO reported a mean automation score of 0.29, compared with 0.30 in 2023. It estimated that one in four workers worldwide were in occupations with some degree of generative AI exposure, and that 3.3% of global employment fell into the highest exposure category. These are modeled estimates of exposure, not observed job losses or predictions about an individual worker. The ILO paper describes its estimates and method.
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The ILO assessment found the highest exposure among clerical occupations, with increased exposure also seen in some highly digitized professional work, including media-, software-, and finance-related roles. That does not mean every person in those fields performs the same tasks or will experience the same workplace outcome. The ILO’s announcement summarizes the index.
In a separate OECD analysis, about a quarter of workers across OECD countries were exposed to generative AI under that study’s definition: at least 20% of their tasks were amenable to AI assistance in 2022–2024. The ILO and OECD figures use different definitions and scopes, so they should not be treated as readings from a single shared scale. See the OECD analysis.
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Does AI exposure mean my job will disappear?
No. Exposure means that some tasks may be technically susceptible to AI assistance or automation; it does not show that an employer will adopt the technology or eliminate a role. A worker may continue to handle review, unusual cases, judgment, coordination, relationships, or responsibility even when AI changes part of the workflow.
Occupational estimates are also not individual forecasts. The result for a broad job category may not reflect your task mix or your employer’s systems. The ILO notes that exposure indicators have limits: they may rely on static task lists and judgments from experts, workers, or AI, and do not necessarily account for economic feasibility or institutional barriers. Such indicators do not by themselves establish whether workers will be displaced, wages will change, or demand will expand. Read the ILO’s explanation of what exposure indicators can and cannot tell us.
A more useful conclusion from your audit may be that a particular part of your work could change. You can then identify which human tasks remain and how your workflow might be reorganized. The ILO’s task-level framework and the OECD’s discussion of task automation both support distinguishing changes to tasks from claims about whole occupations disappearing. See the OECD’s task-level discussion.
