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The best way to identify which parts of your job are most exposed to AI automation is to assess your recurring tasks one by one, not to rely on your job title or an occupation-level score. For each task, ask whether current AI can produce an acceptable result from the available inputs, how much human checking or judgment remains, and whether the work requires relationships, physical action, or accountability. This can help you spot where AI might change your work; it cannot tell you whether your employer will adopt AI or whether your job will disappear.
What does it mean for a task to be exposed to AI?
A task is exposed when AI capabilities appear to match some or all of what that task requires. That is a measure of possible susceptibility, not a prediction that the task—or the person who performs it—will be replaced. A task could be exposed because AI can draft, sort, summarize, classify, or otherwise help produce a work output, yet still require a person to verify it, handle exceptions, explain it to someone, or take responsibility for the result.
Exposure and automation are not the same thing. Technical capability is only one condition: an employer also needs usable data, appropriate permissions, reliable tools, workable integration, a reason to pay for adoption, and a workflow that makes sense. Regulation, organizational choices, and economic conditions can affect what happens next. The International Labour Organization (ILO) cautions that exposure measures describe what AI could do under a static view of tasks, not whether firms will find automation profitable or how work and employment will change. The ILO explains what these indicators can—and cannot—tell us.
That distinction matters in practical terms. A tool may help produce a first draft but not decide what to say, check whether the claims are correct, adapt the message to a sensitive situation, or answer for its consequences. The ILO’s 2025 update concludes that most jobs are more likely to be transformed than made redundant because human input remains necessary. That is a broad global finding, not a guarantee about any particular role or workplace. Read the ILO’s 2025 analysis of generative AI and jobs.
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Think of exposure as a question about fit: how closely do a task’s requirements match what an AI system can do, given the information it can access? It does not, by itself, measure whether a system is accurate enough for your workplace, how much review it needs, or whether an organization has chosen to use it. Those are separate questions to record rather than assume from a label.
How do you assess which tasks in your job are most exposed?
Start with a task inventory, then evaluate capability match, remaining human work, and adoption conditions for each item. The goal is a reasoned map of your work—not a personal automation-risk score.
- List the work you actually do. Write down recurring activities and deliverables rather than relying on your job title or a generic job description. Include work that happens daily, weekly, monthly, or only when a particular problem arises. For each task, note what triggers it, the information or materials it uses, the output it produces, how often it occurs, and roughly how much time it takes. Roles with the same title can have different task mixes, and occupational exposure measures are built from task descriptions.
- Describe the inputs and the acceptable result. Be specific about what a person needs to begin the task and what counts as a good outcome. Inputs might include records, instructions, examples, a customer’s request, or information held in someone’s memory. A task is not a good capability match just because an AI tool can produce a plausible-looking output: the output must meet the actual quality standard, and the necessary inputs must be accessible in a usable form.
- Ask what current AI could do with those inputs. For each task, consider whether available AI could assist with part of it or complete it, and whether it could do so to the required standard. Record the distinction between assistance and completion. For example, a task may be technically amenable to AI assistance while still requiring a person to interpret the result, check it, or finish a separate part of the work. Avoid turning a general impression about AI into a categorical yes or no when the answer depends on the tool, the inputs, or the quality threshold.
- Write down the human work that remains. Identify verification, exception handling, local or tacit knowledge, contextual judgment, communication, trust, physical presence, complex decisions, and responsibility. Some of these may be the core of the task; others may be necessary checks around an AI-generated output. Make the remaining work visible rather than treating it as negligible just because an AI system can produce a draft or recommendation.
- Consider whether your workplace could adopt it. Ask whether the relevant information is available and permitted for use, whether tools can fit into existing systems, and whether the employer has a practical reason and budget to change the workflow. A capability match alone does not show that adoption will happen. These conditions help explain why the same task may be affected differently across organizations.
- Record confidence and evidence. Note what supports your assessment: a clearly defined task and output, a known tool capability, or only a general assumption. Mark uncertain judgments as uncertain. An inventory is more useful when a reader can tell which conclusions are grounded in the task’s inputs and standards and which need further checking.
- Review the inventory as capabilities and work change. AI tools and measurement approaches change, as do job duties and workplace processes. Treat your assessment as a snapshot. Revisit it when your tasks, available tools, policies, or workflow change rather than treating an exposure label as a permanent property of your job.
Keep each task narrow enough that you can assess it meaningfully. If a single line in your list bundles several different activities, separate them when their inputs, outputs, standards, or human responsibilities differ. One part may be routine and codifiable, while another relies on context or a decision that carries responsibility. A broad task label can conceal that difference just as a job title can.
When you are unsure whether a task is assistable or completable, state the condition rather than forcing a binary answer. For instance, note that a capability match depends on information being available in a usable form, or that a draft would still need fact-checking. This makes it easier to revise the assessment if the inputs, tool, or quality requirement changes.
What should you put in a task-by-task worksheet?
A compact worksheet makes assumptions explicit and helps distinguish possible AI assistance from work that still depends on people. Use one row per meaningful task; split a broad activity into smaller parts when the inputs, output, or human responsibilities differ.
| Column | What to record |
|---|---|
| Task | A specific recurring activity or deliverable, not a broad job-title phrase. |
| Frequency and time | How often it happens and roughly how much time it takes. These details describe the task’s place in your work; they are not an automation score. |
| Inputs and output | What starts the work, what information or materials it uses, and what result it must produce. |
| Possible AI role | Whether current AI might assist with part of the task or complete it, and what the capability match depends on. |
| Quality and review | The standard the output must meet, what a person needs to verify, and how exceptions are handled. |
| Human requirements | Judgment, relationships, context, communication, physical presence, complex decisions, and accountability that remain necessary. |
| Adoption barriers | Data access, permissions, reliability, integration, budget, workflow changes, or other conditions that may affect workplace use. |
| Confidence and evidence | What supports the assessment, what is uncertain, and what would change your view. |
This is a practical synthesis of factors used in occupational exposure studies, not a validated scoring system. You do not need to assign numbers to make it useful. A clear note such as “AI could assist with the first draft; a person must verify the facts and tailor the explanation” is more informative than an unsupported percentage.
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Frequency and time are useful for understanding where a task sits in your workload, but they do not determine its exposure. A task that takes little time could still have an important quality or accountability requirement; a time-consuming task could include only a small portion that AI can help with. Keep the task description and capability assessment separate from any judgment about how significant a possible change would be.
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Which tasks may have a closer match with current AI capabilities?
The OECD’s AI exposure measure, published on 26 May 2026, maps capabilities across nine cognitive, social, and physical domains to occupational requirements. It finds closer matches between current AI capabilities and routine information processing and codifiable tasks. It finds larger gaps for work requiring contextual judgment, interpersonal understanding, complex decision-making, and responsibility. These are broad capability patterns, not a universal ranking of every task within a job. The OECD describes its AI exposure measure and capability mapping.
Use those patterns as prompts for your inventory, not as a shortcut around it. If a task mainly involves processing information that is available in a defined form and producing a repeatable output, consider whether AI could help with that portion of the work. Then check what makes the task harder than its surface description: Does it require context that is not in the input? Is the output consequential? Must someone understand a person’s needs or take responsibility for a decision? Those questions help identify the review and judgment that can remain even when parts of a task are more codifiable.
Do not assume that a task involving communication is automatically protected, or that an information-processing task is automatically replaceable. The source measures describe patterns across capabilities and occupational requirements; they do not decide what a particular organization will automate. Your task description, quality standard, human responsibilities, and workplace conditions still matter.
The OECD’s capability-gap approach is one way to organize the question, not a universal definition of exposure. Its mapping compares capability indicators with occupational requirements; other measures use different task descriptions, predictions, or evidence of use. A capability match can therefore look different depending on what abilities and work requirements a method includes. Read the measure’s scope before applying its broad pattern to an individual duty.
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Use an occupation-level measure as context for comparison, not as a verdict about your individual job. These measures can show how an occupation’s described tasks relate to particular AI capabilities or exposure definitions, but a job title does not reveal your exact mix of duties or the conditions in your workplace.
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The ILO’s Generative AI and jobs: A 2025 update, published 20 May 2025, assesses almost 30,000 tasks using an approach that combines human input and AI predictions. It groups occupations into four exposure gradients using both mean exposure and variation in task scores. That variation matters: a high-exposure occupation label does not mean that every task in the occupation is equally exposed. The ILO’s 2025 update sets out the task-based assessment.
For a broad sense of scale, the ILO reports that one in four workers worldwide are in an occupation with some degree of generative AI exposure. It says most jobs are more likely to be transformed than made redundant because human input remains necessary. The figure is a global occupational estimate; it is not a measure of an individual worker’s probability of job loss or a forecast for a particular employer.
The ILO’s 2025 update reports a mean automation score of 0.29, compared with 0.30 in its 2023 assessment. It also reports lower score variability in 2025: a standard deviation of 0.14, versus 0.30 in 2023. These numbers describe the study’s scoring results. They are not a probability that a worker, task, or occupation will be automated, and they should not be read as a percentage chance of job loss.
The U.S. Bureau of Labor Statistics (BLS) provides relative exposure categories across detailed occupations in its employment projections materials, combining five external data sources. It distinguishes theoretical exposure from measures based on current evidence and warns that exposure does not imply job loss, productivity gains, automation probability, or wage effects. The categories can help compare occupations, but they do not establish whether a particular worker uses AI at work. See the BLS explanation of its AI exposure categories.
Before comparing any two measures, check what each one describes:
- Unit of analysis: Is the measure about tasks, occupations, or observed activity?
- Capability and task definition: What kinds of AI capability are matched to what kinds of work?
- Human input and task variation: Does the method account for task variability or the human contribution that remains?
- Coverage: What geography and occupations does it cover?
- Evidence basis: Is it theoretical, based on observed use, or a combination?
- Claim being measured: Does it describe exposure, or an employment outcome? Exposure is not itself an outcome forecast.
ILO, OECD, and BLS labels and rankings should not be treated as interchangeable. They use different approaches and scopes. A score or category can help you ask better questions about your work, but it cannot answer whether your particular employer will adopt AI or whether your position will be eliminated.
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That caution applies even when a measure uses evidence of current AI use. Observed use and theoretical capability are different kinds of information: one concerns activity seen in a defined setting, while the other concerns a possible match between capabilities and work. The BLS materials combine several external sources and distinguish theoretical exposure from current-evidence measures. Check which basis is relevant to the comparison you want to make, and do not infer that an occupation-wide category describes your own workplace.
The ILO’s April 2026 brief explains another limitation: exposure indicators rely on methods and static descriptions of tasks. A measure may say something about susceptibility, but it does not establish whether an organization can profitably automate a task, how it will redesign workflows, or what the eventual effects on employment, wages, and demand will be. Those outcomes also depend on adoption choices and conditions beyond technical capability. Read the ILO’s April 2026 explanation of exposure indicators.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you conclude from your assessment?
Look for differences between tasks, not a single risk label for your whole job. A useful assessment identifies where AI may assist or complete a defined part of the work, what human contribution remains, and which adoption conditions could affect real use. It should also make uncertainty visible.
If your inventory shows a possible capability match, that is a reason to examine the task’s quality requirements, review burden, and workflow—not proof that the work will be automated. If a task depends heavily on context, interpersonal understanding, complex judgment, physical presence, or responsibility, record those requirements precisely rather than assuming that an AI system can take them on. And if an occupation-level measure looks concerning, compare it with your actual duties: occupational averages can hide substantial variation between tasks and between workers.
Keep the categories separate in your notes: possible AI assistance, possible task completion, remaining human contribution, and practical adoption conditions. This gives you a more grounded picture of how the work could change without turning exposure into a personal prediction.
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Frequently Asked Questions
What was the ILO’s average automation score in its 2025 update?
The ILO reported a mean automation score of 0.29 in its 2025 assessment, compared with 0.30 in 2023. It also reported lower score variability in 2025: a standard deviation of 0.14, versus 0.30 in 2023. These are study-level scoring results, not an individual worker’s automation probability.
Does a job exposure category show whether I use AI at work?
No. The BLS categories support relative comparisons across detailed occupations; they do not observe whether a specific worker uses AI in their workplace. Your own task inventory and workplace conditions are needed to describe that situation.
Can an AI exposure measure tell me whether my wages will change?
Exposure measures do not establish wage effects. The BLS explicitly cautions that its categories do not imply wage effects, while the ILO says exposure indicators do not determine how wages, employment, or demand will adjust.
Does a high-exposure occupation score mean every task in my job is exposed?
No. The ILO’s 2025 gradients use both average exposure and variability across task scores, so an occupation can include tasks with different exposure levels. Compare the measure with your actual task mix rather than treating an occupation label as a verdict on every duty.
Are ILO, OECD, and BLS exposure measures directly comparable?
Not as if they were one scale. They differ in unit of analysis, capability and task definitions, coverage, human-input treatment, and whether they use theoretical or observed-use evidence. Check each measure’s scope and what it claims to describe before comparing its labels or rankings.
The Bottom Line
Bottom line: Assess your work task by task. Note what AI could assist with or complete, the quality checks and human responsibilities that remain, and the practical conditions required for adoption. Use occupation-level scores only as context: exposure describes possible susceptibility, not a forecast that your job will disappear.
