AI can help with some information-heavy and clerical tasks, but that does not mean it can do an entire office job or that the job will disappear. Its usefulness depends on the task, the worker’s experience and how the tool fits into the workflow; people still need to apply expertise and review the work.
What AI can help with in office work
Generative AI is most usefully understood as assistance for particular tasks, not as a replacement label for whole occupations. The International Labour Organization’s 2025 assessment finds clerical occupations have the highest exposure to generative AI, but exposure describes tasks that may be affected; it does not establish that every task in those jobs can be automated. The ILO’s 2025 update and its refined global index both frame the issue at the task level.
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In practical terms, a worker or manager can assess whether a tool is useful for a defined task, then check the result before relying on it. The reviewed evidence does not support promising the same benefit for every tool, worker or workflow. The OECD’s review of experiments finds that outcomes depend on task and user experience, and identifies human-AI collaboration as central to realizing potential gains. The OECD review discusses effects on productivity, innovation and entrepreneurship.
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What AI cannot be assumed to do
Exposure is not proof that AI can reliably complete every task in a role, work independently without human expertise, or produce consistently accurate results. Trust and the need for expertise remain important challenges. A capability that works for a bounded task should not be treated as evidence that a full workflow is safe to delegate without review.
Nor does a potential capability or exposure score establish guaranteed productivity gains, higher earnings or job losses. The ILO’s June 2026 review of empirical evidence says reported time savings have not yet reliably translated into higher measured output, earnings or employment. Its synthesis finds productivity gains are uneven and often unverified; that does not mean no worker benefits. The ILO review examines evidence on jobs, productivity and work organization.
What the exposure and adoption figures mean
| Finding | What it does—and does not—show |
|---|---|
| One in four workers worldwide is in an occupation with some degree of generative AI exposure, according to the ILO’s 2025 global assessment. | This is an estimate of occupational exposure, not a forecast that one in four workers will lose a job. The ILO says transformation is more likely than redundancy because most jobs still require human input. Source: ILO, 20 May 2025. |
| 3.3% of global employment falls into the ILO’s highest exposure category. | This is the share in the highest exposure category, not the share of jobs certain to be automated. Source: ILO, 20 May 2025. |
| The ILO’s mean automation score was 0.29 in 2025, compared with 0.30 in 2023. | This is a measure in the ILO’s revised exposure methodology, not a percentage of jobs automated. Source: ILO, 20 May 2025. |
| 31% of SMEs reported using generative AI. | OECD survey evidence published in 2025, based on more than 5,000 SMEs in Austria, Canada, Germany, Ireland, Japan, Korea and the United Kingdom. The survey was conducted in 2024; the figure is not a global adoption rate. Source: OECD, 5 November 2025. |
| 6% of SMEs reported increased staff needs and 9% reported decreased staff needs. | These are staffing reports from the OECD survey conducted in late 2024, published in 2025. They do not establish that generative AI caused all reported staffing changes. Source: OECD, 2025. |
The ILO’s 2025 task-based assessment draws on a representative sample of 29,753 tasks in Poland’s occupational classification, survey inputs and international expert discussions. Its global estimates should be read as exposure analysis, not a count of workers already displaced.
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How to judge whether AI fits a task
For a particular office workflow, the evidence points to a practical set of questions rather than a universal promise of automation:
- Task fit: Is the task clearly bounded, and can a person check the result?
- Complexity: Does the task involve context or judgment that makes reliable output harder?
- Worker experience: Will the person using the tool know how to evaluate and correct its output?
- Trust and expertise: What decisions still require a knowledgeable person?
- Workflow integration: Does the tool support the way the work is actually done, with appropriate human review?
The OECD sources identify these as relevant dimensions for evaluating deployment; they do not provide product-by-product benchmarks establishing that a particular tool is superior.
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