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The most reliable way to assess AI exposure is to examine a job’s tasks, not its title. List the work an occupation actually performs, compare each task with the capabilities of the AI system being considered, keep the range of task-level results visible, and then check whether adoption is technically, economically and institutionally feasible. Aggregate those findings carefully and compare them with employment, wage and job-transition data.
An exposure score describes susceptibility to assistance, task completion or possible substitution. It does not say that a job will disappear. As the International Labour Organization (ILO) put it on 20 May 2025, "As most occupations consist of tasks that require human input, transformation of jobs is the most likely impact of GenAI."
What “exposure to AI” actually measures
Exposure is a measure of how closely the requirements of work match what a specified AI system can do. The result depends on the technology, the outcome being measured and the evidence period. A language model, a computer-vision system and a warehouse robot expose different tasks.
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Before comparing occupations, define four terms:
- AI scope: generative language models, AI broadly, or embodied systems such as robots and autonomous machines.
- Outcome: assistance to a worker, completion of a task, substitution potential, or observed use at work.
- Unit of analysis: an individual task, an occupation’s average task score, or an observed labor-market outcome.
- Time and place: the model or capability evidence window, occupation taxonomy, country and edition of the data.
The U.S. Bureau of Labor Statistics (BLS) states that its exposure measure should not be read as a forecast of job loss: "Exposure does not imply job loss, productivity gains, automation probability, or wage effects." Treat that distinction as a rule for interpreting every ranking.
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A six-step method for assessing an occupation
1. State the technology and decision question
Write a one-sentence scope before collecting scores. For example: “How much of a U.S. payroll clerk’s information-processing work could current generative language models assist or complete?” is a different question from “How likely is this occupation to lose employment over the next decade?” The first is an exposure question; the second requires labor-market projections and adoption evidence.
Also specify whether you are assessing present capability or a forward-looking horizon. The OECD’s 2026 capability-gap measure is designed to look five to ten years ahead, whereas a workplace-use survey describes activity that has already occurred.
2. Decompose the occupation into real tasks
Start with a consistent occupational description and turn it into a task list. Use verbs and outputs rather than broad labels. “Prepare monthly variance reports,” “verify a customer’s identity,” and “de-escalate an upset patient” are assessable tasks; “do finance” and “work with people” are not.
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For each task, record frequency, time share, required inputs, output, quality threshold and who carries responsibility. Separate routine information processing and codifiable outputs from work that depends on context, interpersonal understanding, physical presence, complex decisions or accountability.
Do not assume that a highly paid or highly educated occupation is either safe or exposed as a whole. A single role can contain data preparation that an AI system handles well and judgment or relationship work that still requires a person.
3. Match each task to AI capabilities
Score the fit between the task and the defined technology. A useful worksheet asks:
- Are the inputs available in a machine-readable form?
- Can the system produce the required output with the necessary accuracy, consistency and speed?
- Does the task require tacit context, physical action, negotiation, empathy or an accountable decision?
- Can a human check errors at acceptable cost and within the workflow?
- Would the system need capabilities outside the chosen scope, such as vision, robotics or access to a proprietary database?
BLS distinguishes theoretical measures, which compare AI capabilities with task or ability requirements, from observed-use measures, which map actual AI interactions to tasks or activities. The latter show where use has been recorded; they do not establish that every worker in the occupation used AI.
4. Preserve the distribution, not just the average
Calculate an occupation-level mean only after retaining every task score. A high average can hide a small set of indispensable tasks. Report the spread, the share of time in high-exposure tasks and the share in low-exposure tasks.
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The ILO’s 2025 framework makes this explicit by combining mean exposure with task-score variability. It distinguishes occupations with high exposure and little variation from occupations with elevated exposure but wide variation. The second pattern often indicates transformation of selected activities rather than uniform replacement of the occupation.
5. Test adoption constraints
Capability is a prerequisite, not an outcome. Check whether the organization has the data, infrastructure, software integration, skills and governance needed to deploy the system. Then examine reliability, error costs, privacy, cybersecurity, procurement, regulation, collective agreements and customer acceptance.
Economic feasibility matters too. A task may be technically automatable but cheaper to leave with a worker, especially when exceptions are frequent or review takes nearly as long as doing the task manually. Conversely, a modest capability gain can be important where work is high-volume, standardized and easy to verify.
6. Compare exposure with observed outcomes and date the result
Pair the task assessment with employment, wages, vacancies, hours, occupational transitions and establishment-level adoption where available. Record the source edition, occupation classification, geography and AI evidence period. Static task descriptions and model capabilities age quickly, so a score without a date is incomplete.
How the main official frameworks differ
These frameworks answer related but non-interchangeable questions. Their rankings should not be merged into one universal list.
| Framework | Geography and coverage | What it measures | Important interpretation |
|---|---|---|---|
| BLS exposure categories | United States; detailed occupations linked to 2025–35 projections | Four relative exposure categories built from five external datasets: three theoretical and two based on observed AI use | Observed-use data map interactions to tasks but do not identify which workers in an occupation used AI. Exposure is not job-loss probability, productivity, wage or automation probability. |
| ILO 2025 Global GenAI Index | 436 detailed ISCO occupations; labor-force applications covering more than 140 countries | GenAI task ratings aggregated by occupation using mean exposure and task variability | Designed for global comparison of potential GenAI exposure. Infrastructure, digital skills, cost and operational difficulties constrain adoption. |
| ILO 2026 indicator review | Global evidence review | Compares concepts and methods used in exposure indicators | Measures differ in technology scope, assumptions and outcomes; they can omit feasibility, wages, demand, workflow changes and institutional barriers. |
| OECD 2026 capability-gap measure | Forward-looking occupational mapping | Maps nine cognitive, social and physical AI capability domains to occupational requirements | A smaller capability gap signals higher potential exposure over a five-to-ten-year horizon. It recognizes different patterns for language/reasoning AI and robotics, vision and embodied AI. |
What the ILO’s 2025 evidence says
The ILO’s revised index used a representative sample of 29,753 tasks in Poland’s occupational classification. It combined survey input from 1,640 employed people across ISCO-08 one-digit groups, 52,558 data points covering 2,861 tasks, international expert discussion and AI predictions for the remaining task documentation. The resulting scores were aggregated across 436 detailed ISCO occupations and applied to labor-force data from more than 140 countries.
Its headline estimates describe potential exposure, not predicted displacement:
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- About 3.3% of global employment is in the highest exposure category.
- The highest category covers 4.7% of female employment and 2.4% of male employment.
- Clerical occupations continue to have the highest exposure levels in the framework.
- Some strongly digitized professional and technical occupations also show elevated exposure.
The ILO uses four increasing exposure gradients based on an occupation’s mean task exposure and the variability of scores across its tasks. A gradient with high exposure and low variability suggests that many tasks resemble current GenAI capabilities. High exposure with high variability signals a mixed job: some activities are close to current capabilities while others remain difficult.
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The ILO’s 2026 review cautions that newer capability-based measures often find higher exposure in cognitive, analytical, administrative and managerial work. Earlier automation measures emphasized routine, lower-skilled tasks. Different rankings can therefore be methodologically consistent rather than contradictory: they may use different AI capabilities, task definitions and outcomes.
Why task mix matters more than a job title
Illustrative example: payroll administration
A payroll administrator might spend time importing attendance data, checking tax codes, answering routine employee questions, resolving an unusual deduction and approving a final payment. The first three activities may be candidates for AI assistance if records are structured and rules are explicit. The unusual deduction can require interpretation of policy and missing facts. Final approval carries accountability and may remain with a designated employee even when software prepares the recommendation.
An assessment should therefore report the percentage of work in each category, the error-review requirement and the decision rights attached to the output. Calling the whole occupation “highly exposed” hides these distinctions.
Illustrative example: software development
Code drafting, test generation and documentation can be compared with language-model capabilities. Understanding an undocumented legacy system, selecting an architecture under conflicting constraints, validating security and accepting liability are separate tasks. The occupation’s average exposure depends on the team’s actual task mix, not on the fact that it uses computers.
Illustrative example: nursing
Scheduling, record summarization and patient-education drafts may be assistive use cases. Physical examination, hands-on care, communication with families, triage under uncertainty and accountable clinical decisions involve capabilities and responsibilities that a text model does not cover. A language-only exposure score cannot stand in for a robotics or clinical-systems assessment.
These examples are assessment templates, not rankings of the named occupations.
How to build a transparent occupation scorecard
A scorecard makes assumptions visible and allows two occupations to be compared without pretending that one number is definitive. Use one row per task.
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|---|---|
| Task and time share | Plain-language task description, frequency and estimated percentage of working time |
| Required input | Structured records, free text, images, sensor data, physical materials or live interaction |
| AI capability match | None, partial or strong match to the defined model or system; note the evidence date |
| Human constraints | Context, empathy, negotiation, physical presence, complex judgment, safety or legal responsibility |
| Verification | Whether a human can detect errors quickly and what an error would cost |
| Adoption conditions | Data access, integration, skills, infrastructure, security, regulation and organizational approval |
| Observed outcome | Employment, wages, vacancies, hours or transitions for the same geography and period |
If a numerical summary is needed, calculate a weighted task exposure using time share, then publish the task distribution beside it. Do not let a weighted average erase rare but critical tasks. Label the number as an index or relative score, not as a probability that the occupation will be automated.
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How to compare two occupations without misusing rankings
- Align the geography. Compare U.S. occupations with U.S. data, or use the ILO’s international framework for a global comparison. Do not compare a U.S. BLS category directly with an ILO global occupation as if they used the same taxonomy.
- Align the technology. A language-model score is not a robotics score. State whether vision, speech, physical manipulation or autonomous control is included.
- Align the outcome. Put theoretical capability, observed use, assistance and substitution potential in separate columns.
- Align the date. Record the model evidence period, source edition and projection horizon.
- Compare averages and spread. Review mean exposure, task variability and the share of high-exposure work.
- Add adoption and labor-market evidence. A higher capability match does not establish that employers will deploy the system or reduce headcount.
When a comparable value is unavailable, mark it as “not stated” and preserve the source’s definition rather than filling the gap with a different metric.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Adoption constraints that can reverse a simple ranking
- Data quality: Missing, inconsistent or inaccessible records can prevent an otherwise capable model from working.
- Reliability and review: Hallucinations, edge cases and hidden errors may make human checking essential.
- Workflow integration: A standalone demonstration is different from a system connected to permissions, databases and audit trails.
- Skills and infrastructure: Employers need staff who can configure, supervise and maintain the system.
- Economics: Licensing, integration, cybersecurity, retraining and review costs can exceed the value of automation.
- Rules and responsibility: Privacy law, sector regulation, safety obligations, professional standards and collective bargaining can limit use.
- Organizational choice: Firms may use AI to raise output, improve quality or reduce routine work without reducing employment.
Because these constraints vary by employer and country, an occupation-level exposure score cannot predict an individual worker’s experience.
What evidence to monitor after estimating exposure
Exposure is a starting condition. To test whether it is changing work, track outcomes for the same occupation and geography over time:
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- Employment and hours, separated where possible by industry and establishment size
- Wages, vacancies and hiring rates
- Transitions into, within and out of the occupation
- Changes in task composition, job postings and required skills
- AI adoption, deployment scale and the proportion of work subject to review
Interpret these measures together. Employment can rise while routine tasks are automated if demand expands or workers shift to complementary activities. A fall in employment can have causes unrelated to AI, such as trade, business cycles or regulation. Neither outcome can be inferred from exposure alone.
Common mistakes and how to correct them
Treating exposure as a replacement forecast
Problem: A high score is presented as the percentage of jobs that will vanish.
Correction: Call it potential exposure or capability match, show task variability and state that transformation is the more typical near-term interpretation.
Combining incompatible rankings
Problem: A global GenAI index, a U.S. projection table and a robotics study are averaged into one league table.
Correction: Keep frameworks separate and explain their geography, taxonomy, AI scope and outcome concept.
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Ignoring within-job variation
Problem: The occupation average is used to describe every worker.
Correction: Publish the task distribution, time shares and critical human constraints.
Confusing observed use with capability
Problem: A small number of recorded interactions is interpreted as proof that the whole occupation is low exposure.
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Leaving the date and assumptions out
Problem: A score is quoted without the model period, occupation edition or country.
Correction: Attach those qualifiers to every number and schedule reassessment as capabilities and workflows change.
A practical checklist for readers and analysts
- Write the exact AI system and outcome being assessed.
- Choose one occupation taxonomy and geography.
- List the occupation’s actual tasks and estimate time shares.
- Separate codifiable information work from context, interpersonal, physical and accountable work.
- Rate each task against current capabilities and document the evidence date.
- Record uncertainty, error costs and the need for human review.
- Calculate an average only after retaining task-level scores.
- Report variability and identify tasks that remain essential even when the average is high.
- Check infrastructure, skills, integration, economics, regulation and organizational decisions.
- Compare the result with employment, wages, vacancies and transitions rather than inferring outcomes.
- Label the result as exposure, assistance potential or substitution potential—not a job-loss forecast.
- Revisit the assessment when the AI capability, workflow or institutional conditions change.
Bottom line
The jobs most exposed to AI are not identified reliably by a simple title-based ranking. They are occupations containing a large share of information-processing or other codifiable tasks that match the specified AI’s capabilities, especially when data, infrastructure, verification and incentives support deployment. The defensible assessment is task-level, distribution-aware, dated, geographically specific and paired with adoption constraints and observed labor-market outcomes.
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