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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 errorsGenerative AI can help with some office tasks, especially clerical and information-heavy work, but exposure to AI does not mean an entire job will disappear. The evidence points to task-by-task change: results depend on the work, the worker’s experience, and how AI is integrated and checked.
What AI can help with at work
AI is best understood as assistance for particular tasks, not as a replacement label for a whole occupation. The International Labour Organization’s task-based analysis finds that clerical occupations have the highest exposure to generative AI. That describes potential effects on tasks within those jobs; it does not establish that every clerical task can be automated.
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Information-heavy activities are among the tasks where generative AI may be useful. The OECD’s review of experiments finds that outcomes vary with the task and the user’s experience. Human-AI collaboration is important to realizing potential gains, rather than assuming that handing work to a tool will produce the same result for every person or workflow. OECD, The effects of generative AI on productivity, innovation and entrepreneurship
What AI cannot be assumed to do
Exposure is not a forecast of jobs lost. The ILO’s 2025 assessment says transformation is more likely than redundancy because most jobs still require human input. Its estimate that one in four workers worldwide is in an occupation with some degree of exposure measures potential task effects, not the number of people whose jobs will be eliminated. ILO, Generative AI and jobs: A 2025 update
Nor does a potential capability establish dependable accuracy, productivity, or independent performance across an entire job. The evidence reviewed by the OECD identifies trust and human expertise as ongoing challenges. The ILO’s June 2026 review finds that reported time savings have not yet reliably translated into higher measured output, earnings, or employment. This does not mean no worker benefits; it means those benefits are not guaranteed or uniform. ILO, The impact of GenAI on jobs, productivity and work organization: a review of the empirical evidence
How to interpret the ILO exposure figures
The ILO’s 2025 refined global index places 3.3% of global employment in its highest exposure category. This is a category in a task-based exposure assessment, not a prediction that 3.3% of jobs will be automated. The underlying working paper draws on a representative sample of 29,753 tasks in Poland’s occupational classification, survey inputs, and international expert discussions. ILO, Generative AI and Jobs: A Refined Global Index of Occupational Exposure
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The ILO also reports a mean automation score of 0.29 in 2025, compared with 0.30 in 2023. That score reflects the revised exposure methodology; it is not a percentage of jobs automated. Read both measures as ways to assess which work may be affected, not as counts of jobs already changed or forecasts of future layoffs.
What SME adoption and staffing figures show
An OECD survey published in 2025 found that 31% of surveyed SMEs reported using generative AI. The 2024 survey covered more than 5,000 SMEs in Austria, Canada, Germany, Ireland, Japan, Korea, and the United Kingdom. It is evidence about those businesses and countries, not a universal adoption rate for all employers. OECD, Generative AI and the SME Workforce: New Survey Evidence
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In the OECD’s late-2024 survey, 6% of SMEs reported increased staff needs and 9% reported decreased staff needs. These responses do not establish that generative AI caused every staffing change, and they should not be treated as a general forecast of whether a company will hire or cut jobs. OECD, Executive summary: Generative AI and the SME Workforce
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to assess AI for a particular office task
Evaluate the task and workflow rather than asking whether AI can “do the job.” The evidence supports looking at these factors:
- Task fit: Is the work information-heavy or clerical, and does the tool suit that specific activity?
- Complexity: How much judgment, context, or adaptation does the task require?
- Worker experience: Does the person using the system know how to guide it and judge its output?
- Trust and review: Who checks the result, and what happens when it is wrong or incomplete?
- Human expertise: Which parts still depend on a person’s knowledge, judgment, or responsibility?
- Workflow integration: Does the tool fit into how the work is actually done, rather than merely generating output?
The OECD’s reviewed evidence does not provide a product-by-product benchmark, so it does not establish that a particular assistant is more productive than another. A tool’s usefulness depends on the task and the way people use and verify it.
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