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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsKeep people in charge of tasks that require contextual judgment, accountability, relationship work, or meaningful oversight. Use AI first for bounded, repeatable tasks whose results a qualified person can check. Decide one task at a time, not by labeling an entire job “automatable” or “safe.” Most jobs combine work with different levels of AI exposure, reviewability, and risk.
What does AI exposure say about jobs?
Exposure means that generative AI could affect some tasks in an occupation. It does not mean those tasks will be adopted, that a worker will lose a job, or that an employer can remove human review. Adoption depends on factors such as infrastructure, digital skills, cost, and how difficult the technology is to integrate into a real workflow.
The International Labour Organization’s 2025 assessment estimates that one in four workers globally is in an occupation with some GenAI exposure, while 3.3% of global employment is in its highest exposure category. The ILO estimates some exposure for 34% of employment in high-income countries and 11% in low-income countries. These are measures of potential exposure, not forecasts of job losses.
The ILO’s index combines task-level data, worker input, expert review, and AI predictions, then maps task exposure to employment data. Its highest exposure gradient indicates high and consistent exposure across an occupation’s tasks. Lower gradients can still include highly exposed tasks, but show greater variation. That is why an occupation-level estimate is a reason to examine tasks, not a yes-or-no automation decision.
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Clerical occupations have the highest exposure in the ILO analysis. Exposure has also increased for some highly digitized work in media, software, and finance. The effect can vary substantially within each field: a job title does not tell you which tasks are suitable for automation or how much review they need.
Which tasks are good candidates for AI automation?
Consider automating a task when it has clear inputs, a defined and repeatable output, and an affordable way to check the result. Examples might include sorting incoming requests by category, extracting specified fields from documents, or drafting a routine first version for a person to review. These are candidate task types, not guarantees that a particular system will perform them reliably.
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The OECD’s 2025 review of experimental research finds that outcomes depend on both the task and the user’s experience. It also identifies human-AI collaboration as a way to make better use of AI’s potential. Productivity results are therefore not a sound reason to automate a workflow without checking how the tool performs in that workflow.
A 2025 ILO repository record summarizing a review reports productivity gains on the order of 20 to 60 percent in controlled randomized trials and 15 to 30 percent in field experiments. Those ranges summarize heterogeneous study results; they are not a prediction for a particular company, job, or task. The review and OECD assessment both point to variation across tasks and workers, including mixed findings on complex work.
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Which tasks should stay human-led?
Keep a person as the decision-maker when the task depends on context the system may not have, when a wrong answer could cause significant harm, or when the work requires responsibility that cannot be delegated to a model. Examples include deciding how to handle an unusual or consequential case, resolving a disagreement that requires understanding the people involved, and approving a decision that materially affects someone.
Human-led does not have to mean “no AI.” A tool might help gather information or draft options while a qualified person evaluates the context and makes the decision. The important distinction is who has authority to decide, whether the person can inspect the basis for the AI’s output, and whether there is a clear route to challenge or escalate it.
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Human review is not automatically effective just because a person is in the workflow. Reviewers need enough relevant expertise, time, and access to the underlying information to identify errors. If they cannot realistically check an output, labeling the process “human-reviewed” does not provide meaningful oversight.
How should a task be allocated?
Use the table as a starting point, not a universal ranking. The ILO and OECD evidence supports evaluating tasks and workflows; it does not supply a single scale that determines the right treatment for every task.
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| Work pattern | Possible approach | What to establish |
|---|---|---|
| Bounded, repetitive work with a checkable output | Test automation | Define acceptable output, sample errors, and set a route for uncertain or incorrect results. |
| Work where AI can assist but context or expertise matters | Augment a worker | Make the person’s review and decision role clear; ensure they can inspect the relevant information. |
| Variable, consequential, or relationship-dependent decisions | Keep the decision human-led | Preserve human authority, provide escalation, and avoid treating an AI recommendation as a decision. |
For each task, ask these questions before choosing:
- Can you bound the task? Specify its inputs, output, and exceptions. Open-ended work with hidden context is harder to assess than a repeatable task with observable results.
- Can a qualified person check it? Decide what counts as correct, who can assess it, and what happens when the system is uncertain or wrong.
- What is the consequence of an error? As consequences rise, preserve stronger human decision authority and clearer escalation. The sources do not provide a universal list of tasks that are safe or unsafe; the organization must assess its own use case.
- Does automation remove drudgery or necessary judgment? Automating an administrative subtask may leave more time for complex work. Automating a task central to a role can change that role more deeply. The ILO identifies task centrality, workflow integration, and decisions about who performs or oversees remaining work as factors shaping whether AI complements or displaces labor.
- What changes for workers? Track review time, work intensity, autonomy, data collection, and access to training as well as speed or cost. A faster process can still make work more demanding or reduce workers’ control over how it is done.
How can an organization test a task safely?
- Choose a specific task, not a whole job. Record who currently does it, the inputs it uses, how often exceptions occur, and what a correct result looks like.
- Set a baseline. Measure the current quality, completion time, and effort needed to handle errors. Include the time spent checking AI outputs; otherwise a tool may appear to save time while shifting work to reviewers.
- Run a limited pilot with review. Keep the person responsible for the workflow in the loop and define when the system must defer, when a reviewer must intervene, and how errors will be recorded.
- Evaluate more than speed. Compare output quality, correction and escalation rates, review burden, worker experience, and effects on people whose information or decisions are involved.
- Expand only if results hold in the real workflow. Adjust the task boundary, review process, or training when the pilot exposes problems. Stop or redesign the use if errors cannot be detected in time or review takes away the expected benefit.
This process is a practical application of the task-level approach in the ILO’s 2025 work and the OECD’s finding that results depend on task and user experience. Short experiments can inform a decision, but they do not establish long-term effects on employment, skills, or expertise.
What do workplace studies say about benefits and risks?
In an OECD 2024 survey, four in five surveyed workers said AI improved their performance at work, and three in five said it increased their enjoyment of work. These are worker self-reports, not proof that AI caused better outcomes for every respondent or that all workers benefit.
The same OECD paper identifies concerns about work intensity, collection and use of data, and inequality. Its estimate that about 27% of employment in OECD countries is in occupations at the highest risk of automation uses a different measure from the ILO’s 2025 GenAI exposure estimates; the two figures should not be compared as if they describe the same risk.
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Workplace decisions should therefore include workers’ understanding of AI limitations, access to relevant training, and a way to raise concerns about how a tool changes the job. The ILO emphasizes social dialogue in managing transitions. The applicable legal requirements for consultation, privacy, employment decisions, or sector-specific oversight depend on jurisdiction and context; the evidence summarized here does not establish which rules apply to a particular employer.
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