Assess the tasks in a role, not just an occupation-level AI exposure score. Before automating, test what the system can reliably do in the employer’s actual workflow, measure effects on output and working conditions, and identify the human judgment and oversight that remain. The result may support limited use, task redesign, augmentation or training—not necessarily job elimination.
What does AI exposure tell an employer about a job?
Exposure estimates indicate where AI may overlap with work tasks; they do not show that a particular system can perform those tasks reliably in your workplace, or predict that a role will disappear. The International Labour Organization’s 2025 update estimates that one in four workers worldwide is in an occupation with some degree of generative-AI exposure. It says most jobs are more likely to be transformed than made redundant because human input remains necessary.
The ILO methodology combines task-level data, expert input and AI predictions. It covers nearly 30,000 tasks at six-digit occupational detail and groups exposure into four gradients based on average exposure and task variability. Its mean automation score was 0.29 in 2025, compared with 0.30 in 2023; the standard deviation fell from 0.30 to 0.14. These are scores from the ILO methodology, not percentages of jobs expected to disappear.
That distinction matters because “How might generative AI impact different occupations?” and “What is the possible effect of generative AI on employment?” are broad questions. For an employer considering a specific role, the useful question is narrower: which tasks can this system perform in this workflow, with what results, and what work remains for people?
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How to assess a role before automating it
1. Define the decision and document the baseline
Write down why AI is being considered and what decision the assessment must inform. Record how the work currently gets done: tasks, volume, cycle time, quality, error and rework rates, service outcomes, and existing human review. These are practical measures for a transparent comparison, not a universal metric set prescribed by the OECD or ILO.
2. Map the role into tasks
For each task, note how often it occurs, how much time it takes, how variable it is, what judgment or relationship work it involves, how exceptions are handled, and the consequences of error. Map the proposed AI capability to specific tasks rather than treating the job title as the unit of analysis. The ILO’s task-level method and attention to task variability support this approach.
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3. Pilot 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 baseline for speed, quality, errors, rework, service outcomes and review burden. Record failures, escalations and work shifted to other employees. These comparison measures are practical recommendations, not an official standard set by the sources cited here.
4. Keep exposure, capability and business choice separate
An occupation-level exposure estimate describes potential task overlap. It does not establish reliability in your environment or determine what the organization should automate. The ILO’s framing also highlights three factors that affect whether automating tasks results in job loss or augmentation: how central the task is to the occupation, how AI is integrated into work processes, and whether management retains people to perform or oversee tasks. See the ILO’s artificial intelligence topic page.
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Measure more than output. Consider whether the system changes workload or work intensity; introduces surveillance or privacy concerns; affects bias, health and safety, transparency or accountability; or limits a worker’s ability to question or appeal consequential decisions. Check who gets access to the tools and training, and whose work bears the costs of implementation.
In the EU, the AI Act’s Recital 57 identifies specified employment and worker-management uses as high-risk. These 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 points to potential effects on career prospects, livelihoods, discrimination, privacy and worker rights. This does not mean every tool used to automate work tasks has the same classification; employers need to assess the system’s actual use and applicable jurisdiction. Read Recital 57.
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6. Consult workers and plan transitions
Ask affected workers and their representatives what the task map misses, including informal work, exceptions and customer or colleague interactions. Explain the system’s purpose, the data it uses and its role in decisions. Identify complementary skills, realistic retraining options and ways to redesign roles that preserve valuable human contributions.
An OECD survey of 5,334 workers and 2,053 firms in manufacturing and finance across Austria, Canada, France, Germany, Ireland, the United Kingdom and the United States found that four in five workers who used AI reported improved work performance, while three in five reported greater enjoyment at work. These are reported experiences in that survey population, not guaranteed results for other sectors, employers or workers. The OECD also found that training and worker consultation were associated with better worker outcomes. See its 2024 report and report PDF.
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ILO/JRC case studies of logistics and healthcare workplaces in France, Italy, India and South Africa found that effects on job quality and monitoring differed by country and context. The findings are a reason to examine local working conditions, not to assume one sector’s or country’s result applies everywhere. Uma Rani, ILO Senior Economist and co-author of the 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.” Read the ILO’s case-study summary.
7. Decide against context-specific thresholds and 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. There is no universal threshold in the sources cited here for eliminating or redesigning a role. Continue monitoring after deployment: model capabilities and work practices can change, and pilot results may not hold as the system scales.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to compare the alternatives
Compare the current process, AI-assisted work and selective task automation against the same criteria. A system that performs one task quickly may still create more review work, weaken service quality or shift risk elsewhere in the team.
Quick Recap
| What to compare | Questions to answer |
|---|---|
| Task coverage and reliability | Which tasks can the system perform under real conditions? How does reliability change with variability or exceptions? |
| Quality and service | What happens to accuracy, rework, completion time and the experience of customers or service users? |
| Human work remaining | What judgment, exception handling, relationship work and oversight remain? How does work shift across the team? |
| Job quality and rights | How do work intensity, surveillance, privacy, fairness, safety, transparency and accountability change? |
| Skills and transition | What training and complementary skills are needed? Can roles be redesigned to retain valuable human contribution? |
| Context | How do sector, geography, workplace institutions and applicable law affect likely outcomes or obligations? |
What an assessment cannot establish on its own
- Exposure is not realized automation. An occupation-level estimate cannot replace evidence about tasks and performance in the employer’s workflow.
- Survey perceptions and case studies offer useful signals, but do not guarantee results for a different employer, role, country or system.
- The EU AI Act discussion above concerns particular employment and worker-management uses. Classification depends on the actual system, use and applicable jurisdiction.
- The cited sources do not establish universal pilot metrics, a single threshold for automating a role or a provider employers must use.
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