The most reliable way to assess AI exposure is to analyze a job’s tasks, not its title. Define the AI system and outcome you mean, map the occupation’s actual tasks, compare each task with documented AI capabilities, preserve the spread between easy and difficult tasks, and then test whether adoption is technically, economically and institutionally feasible. Exposure indicates that work could change; it does not predict that the occupation will disappear.
What “AI exposure” actually measures
AI exposure is a measure of how much of an occupation’s work could be affected by a specified AI capability. Depending on the method, “affected” can mean that AI assists a worker, completes a task, creates a possibility for substitution, or is already being used in workplaces. Those are different questions and must not be treated as one ranking.
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An exposure score is therefore not a job-loss forecast. The U.S. Bureau of Labor Statistics (BLS) states: “Exposure does not imply job loss, productivity gains, automation probability, or wage effects.” A highly exposed occupation can retain substantial employment if AI is unreliable, expensive to implement, restricted by regulation, or used to augment people rather than replace them.
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Start by defining the question
Before assigning a score, write down four choices. Without them, two apparently conflicting studies may simply be measuring different things.
1. Specify the technology
- Generative language or reasoning systems: relevant to drafting, summarizing, coding, classification, analysis and other digital information work.
- AI broadly: may include prediction, recommendation, computer vision and speech systems.
- Robotics and embodied AI: relevant when a system can perceive and act in a physical environment.
A language model and a warehouse robot do not expose the same tasks. A report that combines them without saying so is difficult to interpret.
2. Define the outcome
- Assistance: AI helps a worker complete a task faster or with fewer errors.
- Task completion: AI performs the output without continuous human execution.
- Substitution potential: the task could, in principle, be done with less human labor.
- Observed use: workers or organizations are already using AI for the activity.
Capability-based exposure answers a “could this be affected?” question. Observed-use data answer a “where is this happening?” question. Neither alone establishes future employment change.
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3. Set the geography, date and taxonomy
State whether the analysis concerns the United States, a global workforce or another labor market; identify the occupation classification; and record the source edition and evidence period. Occupational titles and task mixes differ by country. A result based on U.S. detailed occupations cannot automatically be applied to an equivalent-sounding role elsewhere.
4. Decide whether you need an average or a distribution
An occupation-wide mean is useful for comparison, but it can conceal a split between highly exposed administrative tasks and low-exposure duties involving responsibility, physical presence or interpersonal judgment. Report both the average and the variation whenever the data permit.
Step 1: Decompose the occupation into real tasks
Use a consistent occupation description and replace the title with an inventory of work. Describe what the worker produces, decides, communicates and physically does. Separate core duties from occasional activities and identify where another person must approve, verify or take responsibility.
Classify each task by its work requirements
- Routine information processing: entering, transforming, sorting or retrieving structured information.
- Codifiable outputs: text, standard code, summaries, forms, classifications or other outputs with clear specifications.
- Contextual judgment: interpreting incomplete facts in a changing or high-stakes situation.
- Interpersonal understanding: negotiation, coaching, care, conflict management or trust-building.
- Complex decisions and responsibility: choosing among uncertain options and accepting legal, ethical or organizational accountability.
- Physical presence and manipulation: movement, inspection, dexterity, equipment handling or work in an uncontrolled environment.
This classification is not a claim that one category is always safe or always automatable. It creates a transparent basis for comparing task requirements with a particular system’s capabilities.
Record constraints, not just outputs
For every task, note whether inputs are digital and available, whether the result can be checked objectively, how costly an error would be, whether privacy or security rules apply, and whether a human must be present. A model may produce plausible language while still lacking the authority, context or reliability needed to perform the task independently.
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Use the same level of detail for every occupation
Comparing one occupation through ten detailed tasks with another through a single broad description creates a misleading result. Use a repeatable template, such as task name, frequency, required inputs, output, decision authority, human interaction, physical setting and consequences of error.
Step 2: Compare task requirements with AI capability
Once the task list is stable, evaluate the match between each requirement and the capability evidence. Do not ask whether AI is “good at the job” in general. Ask which parts it can perform, under what conditions and with what level of supervision.
Capability dimensions to examine
- Reading and generating language, including domain-specific terminology.
- Reasoning over structured and unstructured information.
- Planning, sequencing and adapting actions.
- Perception of images, speech or physical surroundings.
- Social interaction and interpretation of human intent.
- Fine or gross physical action in the relevant environment.
- Reliability, consistency and error detection.
- Ability to operate within access controls, privacy rules and audit requirements.
- Capacity to explain decisions and accept or support accountability.
The OECD’s 2026 capability-gap approach maps nine cognitive, social and physical AI capability domains to occupational requirements. A smaller capability gap indicates higher potential exposure. Its mapping highlights routine information processing, administrative work and codifiable tasks as closer to current capabilities, while contextual judgment, interpersonal understanding, complex decision-making and responsibility are generally farther away.
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Distinguish theoretical and observed measures
BLS combines five external datasets for its 2025–35 U.S. occupation projections: three theoretical measures and two measures based on observed AI use. The theoretical measures compare capabilities with tasks or abilities. The observed measures map actual AI interactions to tasks or activities, but BLS cautions that they do not directly establish which workers in an occupation used AI at work.
Use theoretical measures to assess technical possibility and observed-use measures to assess adoption signals. A high theoretical score with little observed use may indicate barriers, early-stage technology or a mismatch between a benchmark and real workflows. A high observed-use signal does not prove that the task is fully automated; it may represent assistance.
Step 3: Preserve variation inside the occupation
Aggregate scores only after rating individual tasks. The ILO’s 2025 GenAI framework reports both mean exposure and task-score dispersion, producing four increasing exposure gradients. This prevents a high average from being read as uniform exposure.
| Pattern | What it means | How to report it |
|---|---|---|
| Low average, low variation | Tasks are generally distant from the measured capability and relatively similar. | Describe exposure as broadly limited under the stated technology and date. |
| Low average, high variation | A minority of tasks may be affected even though the occupation-wide mean is low. | Identify the exposed task cluster instead of calling the occupation “safe.” |
| High average, low variation | Many tasks resemble the measured capability. | Discuss broad potential for workflow change, then check feasibility and human controls. |
| High average, high variation | Some tasks are highly exposed while others remain difficult. | Expect task redesign and role differentiation rather than a single occupation-wide outcome. |
When a source supplies only an average, say that the distribution is unknown. Do not infer that every worker has the same exposure.
Step 4: Test whether adoption is feasible
Technical capability is only the first gate. Assess the conditions that determine whether an organization can and will deploy the system.
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Infrastructure and data
Check connectivity, computing access, software integration, data quality, permissions and the availability of representative examples. A task may be theoretically automatable but impractical when records are fragmented, sensitive or not digitized.
Skills and workflow design
Workers need the skills to supervise outputs, detect errors and escalate unusual cases. The organization must also redesign handoffs, approvals, documentation and training. A tool that saves time on drafting can add review work if every output requires extensive checking.
Reliability and operating cost
Estimate error rates that are acceptable for the task, the cost of verification, downtime, integration and maintenance. Include the cost of changing processes, not just the price of a model or device.
Rules and institutional choices
Privacy, employment, safety, professional licensing, procurement and sector-specific regulation can restrict use. Employers may choose human review for accountability, quality or customer trust even when automation is technically possible.
Physical and environmental constraints
For embodied systems, examine workspace variation, safety, dexterity, navigation, maintenance and the consequences of failure. A capability demonstrated in a controlled setting may not transfer to a busy, changing workplace.
Step 5: Check labor-market outcomes separately
Exposure should be paired with evidence about employment, wages and job transitions, not substituted for it. Track whether employment is growing or shrinking, how pay changes, which tasks are being added or removed, and whether workers move into related occupations.
Do not infer a wage effect from an exposure score. Do not infer displacement from a capability gap. Demand for a service, productivity changes, new tasks, organizational strategy and labor supply can push outcomes in different directions.
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How the major frameworks differ
The following measures are complementary, not interchangeable rankings. They use different geographies, taxonomies, technology scopes, inputs and outcome concepts.
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| Framework | Coverage and date | What it measures | Important qualification |
|---|---|---|---|
| BLS exposure categories | United States; occupations used in 2025–35 projections | Four relative exposure categories built from five external datasets, including theoretical capability measures and observed-use measures | Observed interactions do not establish which workers used AI at work; exposure is not job loss, productivity, automation probability or wage effect. |
| ILO 2025 GenAI index | Global; 436 detailed ISCO occupations; labor-force survey application in more than 140 countries | Task exposure aggregated with mean scores and task variability | Focused on generative AI; infrastructure, digital skills, cost and operational difficulties limit adoption. |
| ILO 2026 indicator review | Review of exposure-indicator approaches | Explains why measures differ and what they omit | Indicators may omit wages, feasibility, workflow changes, institutional barriers, demand effects and reskilling needs. |
| OECD 2026 capability-gap measure | Forward-looking; intended for potential effects over five to ten years | Maps nine cognitive, social and physical capability domains to occupational requirements | A lower capability gap means higher potential exposure; it is not a forecast of employment change. |
What current evidence says about exposed work
The ILO’s 2025 index estimates that one in four workers are in an occupation with some GenAI exposure, while 3.3% of global employment falls in its highest exposure category. The highest category includes 4.7% of female employment and 2.4% of male employment. These are exposure estimates, not counts of jobs expected to disappear.
Clerical occupations continue to show the highest exposure in the ILO’s 2025 framework. Some strongly digitized professional and technical occupations also show elevated exposure. The ILO’s 2026 review notes that recent capability-based measures often identify cognitive, analytical, administrative and managerial work as highly exposed, whereas earlier automation measures emphasized routine, lower-skilled tasks. The difference reflects method and technology scope, not necessarily a contradiction.
The 2025 ILO index used a representative sample of 29,753 tasks in Poland’s occupational classification, 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 remaining task documentation. Its public explainer aggregates scores across occupations and applies them to labor-force data. Those methodological details matter when comparing the result with a U.S.-specific or forward-looking capability measure.
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Consider a hypothetical administrative coordinator. Do not score the title as a whole; split it into tasks:
- Extract fields from incoming forms.
- Draft routine email responses from approved templates.
- Schedule appointments across shared calendars.
- Resolve an unusual request involving incomplete records.
- Explain a policy to a frustrated person and negotiate a workable option.
- Verify that a completed transaction meets internal and legal requirements.
- Maintain access controls and escalate suspected misuse.
The first three tasks are information processing or codifiable outputs and may have a closer capability match for current language systems. The fourth and fifth require context and interpersonal judgment. The sixth and seventh add responsibility, risk and governance constraints. An occupation-level average would conceal this spread.
Next, record whether the organization has clean digital records, approved templates, calendar integration, review capacity, privacy controls and a rule requiring a human decision on exceptions. Finally, compare the assessment with observed use, employment trends, pay and transitions. The result should describe which tasks are likely to change first, what new review or escalation work appears, and what remains dependent on human authority.
How to write a defensible exposure report
Use a fixed report structure so readers can audit the conclusion:
- Question: name the technology, outcome and decision the report supports.
- Scope: state geography, occupation taxonomy, source date and evidence window.
- Task inventory: list the actual tasks and their frequency, inputs, outputs and constraints.
- Capability match: explain why each task is close to or far from the relevant AI capabilities.
- Distribution: show the average and variation, or state when variation is unavailable.
- Adoption screen: document infrastructure, skills, reliability, cost, regulation and organizational choices.
- Outcomes: add employment, wage and transition evidence without treating it as a consequence of exposure alone.
- Uncertainty: identify assumptions, missing data, taxonomy differences and the date after which the assessment should be updated.
Use language such as “higher potential exposure under this capability measure” rather than “will be automated.” State whether a result describes assistance, possible substitution or observed use in the same sentence as the score.
Common mistakes and how to correct them
Ranking titles without examining tasks
Problem: A broad title may cover very different workplaces and specialties.
Correction: use a consistent task inventory and report within-occupation variation.
Mixing incompatible rankings
Problem: A global GenAI index, a U.S. observed-use dataset and a robotics capability measure are presented as one league table.
Correction: label geography, taxonomy, technology, date and metric before comparing.
Calling exposure a probability of replacement
Problem: Technical possibility is converted into a forecast of job loss.
Correction: separate capability, adoption and labor-market outcomes.
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Problem: A plausible output is treated as an authorized decision.
Correction: record who verifies, approves, explains and bears responsibility.
Using old task descriptions as current facts
Problem: Static occupational data may not reflect new workflows or capabilities.
Correction: date every assessment, identify the source edition and schedule updates.
Assuming adoption is frictionless
Problem: Infrastructure, data, skills, cost and regulation are omitted.
Correction: make each a separate adoption gate and document which one limits deployment.
How workers and employers should use the result
For workers, an exposure assessment is most useful as a task-transition map. Identify duties likely to be assisted, duties that require stronger verification or domain judgment, and adjacent responsibilities that become more valuable when routine work is automated. Do not treat an occupation-wide score as an individual prediction; your employer’s systems, policies and client base may produce a different task mix.
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Bottom line
Assess AI exposure from the bottom up: define the technology and outcome, map tasks, compare requirements with capabilities, show the distribution, test adoption constraints and then examine employment, wages and transitions. The strongest conclusion is usually about which tasks are likely to change first and what human work remains—not a claim that an entire occupation will vanish.
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