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Assess AI’s impact task by task, in the actual workplace—not by treating an occupation-level exposure score as a forecast that a job will disappear. Before automating a role, employers should test what the system can reliably do, measure effects on output and working conditions, identify the human judgment and oversight that remain, and consult affected workers about redesign and training options.
What does AI exposure say about a job?
Exposure estimates indicate that some tasks in an occupation may overlap with generative AI capabilities. They do not show that a particular system can perform those tasks reliably in your workplace, or that the employer will automate them. The International Labour Organization’s 2025 update combines task-level data, expert input and AI predictions across nearly 30,000 tasks at six-digit occupational detail. It groups exposure into four gradients, accounting for average exposure and task variability. These are structured estimates of potential exposure, not forecasts for an individual employer.
The ILO reports that one in four workers worldwide is in an occupation with some degree of generative-AI exposure. Its conclusion is that most jobs are more likely to be transformed than made redundant, because human input remains necessary. The methodology’s mean automation score was 0.29 in 2025, compared with 0.30 in 2023; the standard deviation fell from 0.30 to 0.14. Those figures describe the ILO’s scoring method, not the share of jobs expected to disappear.
For an employer, the useful question is narrower: which tasks might a proposed system support or perform here, how well, and with what consequences for the work around them?
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How to assess a role before automating it
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Define the decision and document the baseline
State why the organization is considering AI and what decision it needs to make. Document how the work currently happens, including tasks, volume, cycle time, quality, errors, rework, service outcomes and existing human review. These are practical measures for a fair comparison, not a universal metric set prescribed by the OECD or ILO.
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Break the role into tasks
For each task, record how often it occurs, how much time it takes, how variable it is, what judgment or relationships it requires, how exceptions are handled and what happens when an error occurs. Map the proposed system to specific tasks rather than assigning a single AI-exposure label to the whole role. This reflects the ILO’s task-level approach and its attention to variability.
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Test the system in the real workflow
Run a bounded pilot with human review before making a workforce decision. Compare the AI-supported process with the documented baseline for speed, quality, errors, rework, service outcomes and the time people spend checking or correcting results. Record failure cases, escalation needs and work shifted to colleagues or other teams. These are useful comparison measures, not an official standard imposed by the sources.
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Separate exposure, capability and the employer’s choice
Keep three questions distinct: does the task overlap with AI capabilities; can this system do it reliably in this workplace; and does the organization want to automate, augment or retain human performance of it? The ILO’s framing identifies task centrality, integration into work processes and management’s choice to retain people for performance or oversight as factors shaping whether automation leads to job loss or augmentation. See the ILO’s artificial intelligence topic page.
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Assess job quality and worker rights
Examine how the change could affect workload and work intensity, monitoring and privacy, fairness, health and safety, transparency, accountability, and the ability to question or appeal consequential decisions. Check who receives access to the AI tools and the training needed to use or oversee them.
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Consult workers and plan transitions
Ask affected workers and their representatives what the task map misses, explain the system’s purpose and role in decisions, and identify complementary skills, training and realistic role-redesign options. In its 2024 workplace AI surveys, the OECD found that training and worker consultation were associated with better worker outcomes. The ILO’s workplace case-study reporting also emphasizes social dialogue and worker protection. ILO Senior Economist Uma Rani, a co-author of that report, said: “Social dialogue and strong industrial relations are key to ensure that employers and workers can mitigate the possible negative impact on job quality and that workers are protected.”
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Decide against context-specific thresholds, then monitor
Set decision thresholds for your own workplace before reviewing pilot results. Possible outcomes include not adopting the system, limiting its use, augmenting work, redesigning a role or automating selected tasks. The sources do not establish a universal threshold for eliminating or redesigning a role. Continue monitoring after deployment because system capabilities and work practices can change.
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How should employers compare the alternatives?
Compare the current process with AI-assisted work and, where relevant, selective task automation. Use the same criteria for each option so a speed gain does not obscure added checking work, poorer service or a change in working conditions.
| Comparison area | What to examine |
|---|---|
| Task coverage and reliability | Which tasks the system handles under real conditions, how performance varies and when it fails. |
| Quality and service | Accuracy, rework, completion time and the experience of customers or service users. |
| Human work remaining | Judgment, exception handling, relationship work and oversight—and how those duties shift across the team. |
| Job quality and rights | Changes in work intensity, monitoring, privacy, fairness, safety, transparency and accountability. |
| Skills and transition | Required training and complementary skills, and whether roles can be redesigned to retain valuable human contribution. |
| Context | How sector, geography, workplace institutions and applicable law affect likely impacts and obligations. |
What evidence exists about workers’ experience?
In an OECD survey covering 5,334 workers and 2,053 firms in manufacturing and finance across Austria, Canada, France, Germany, Ireland, the United Kingdom and the United States, four in five workers who used AI reported improved work performance, and three in five reported greater enjoyment at work. These are workers’ reported experiences in the surveyed sectors and countries, not a guarantee that AI will improve every role or workplace. The OECD’s 2024 report discusses the opportunities, risks and policy responses; its report PDF provides further detail on the surveys.
Context matters beyond survey results. ILO and Joint Research Centre case studies of algorithmic management in logistics and healthcare across France, Italy, India and South Africa found that effects on job quality and monitoring differed across countries and workplaces. A result from one sector or location should not be assumed to apply to another.
When do employment-related AI rules matter?
In the EU, the AI Act’s Recital 57 identifies specified employment and worker-management uses as high-risk. Examples include AI used for recruitment and selection, decisions affecting work relationships, task allocation based on personal characteristics or behavior, and monitoring or evaluation. The recital highlights potential effects on career prospects, livelihoods, discrimination, privacy and worker rights. This is not a blanket classification of every AI tool used to automate a work task. Employers should check the actual system, its intended use and the applicable jurisdiction. The European Commission’s AI Act Recital 57 page sets out the relevant recital.
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