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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Before automating a role, assess the tasks within it—not an occupation-level AI exposure score. Map what the system can reliably do in your actual workflow, measure effects on output and working conditions, and identify the judgment, oversight, and complementary skills people still provide. The evidence may support limited use, augmentation, or role redesign rather than eliminating a job.
What does AI exposure say about a particular job?
Exposure estimates indicate that some tasks in an occupation may overlap with generative AI capabilities. They do not show that a specific system can perform those tasks reliably in your workplace, or predict that a particular role will disappear.
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The International Labour Organization’s 2025 analysis 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 based on average exposure and task variability. These are structured estimates, 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, and says most jobs are more likely to be transformed than made redundant because human input remains necessary. ILO, Generative AI and jobs: A 2025 update (20 May 2025).
The ILO’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 methodology. They are not the share of jobs expected to disappear, nor a company-level automation probability.
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How should employers assess a role before automating it?
Use a staged assessment that separates what the AI might do from what it actually does in context, and from the organization’s decision about how work should be arranged. The measures below are practical recommendations for comparison, not a universal metric standard prescribed by the cited sources.
- Define the decision and establish a baseline. State why AI is being considered and document how work is done now: tasks, volume, cycle time, quality, errors, rework, service outcomes, and existing human review. Without a baseline, a pilot cannot show what changed.
- Break the role into tasks. Record each task’s frequency and time, along with variability, judgment, relationship work, exceptions, and the consequences of error. Match proposed AI capabilities to particular tasks instead of assigning one exposure label to the whole occupation.
- Run a bounded pilot in the real workflow. Keep human review in place and compare the AI-supported process with the baseline. Track speed, quality, errors, rework, service outcomes, and the burden of reviewing AI output. Record failure cases, escalations, and work shifted to other employees.
- Separate exposure, capability, and business choice. Exposure describes potential task overlap; it does not prove the system works reliably in your environment. Consider how central the task is to the occupation, how AI would fit into the workflow, and which tasks people will continue to perform or oversee. The ILO identifies these factors as important to whether task automation results in job loss or augmentation. ILO, Artificial intelligence topic page.
- Assess job quality and rights. Examine changes in workload and work intensity, monitoring and privacy, bias, health and safety, transparency, accountability, and workers’ ability to question or appeal consequential decisions. Check who can access the tools and training, and who may be disadvantaged by their introduction.
- Consult workers and plan transitions. Ask affected workers and their representatives what the task map misses. Explain the system’s purpose, the data it uses, and its role in decisions. Identify complementary skills and realistic options for training or redesigning roles.
- Make a decision against local thresholds, then monitor. Possible outcomes include not adopting the tool, using it in a limited way, augmenting work, redesigning a role, or automating selected tasks. Set thresholds for your own context and continue checking results after deployment, since model capabilities and working practices can change.
What should a fair comparison between options include?
Compare the current process with AI-assisted work and, where relevant, selective task automation. A faster process is not necessarily a better one if it lowers quality, shifts hidden work to colleagues, or worsens job conditions.
| Assessment area | Questions to answer |
|---|---|
| Task coverage and reliability | Which tasks can the system perform under real conditions? How often does performance vary, fail, or require escalation? |
| Quality and service | What changes in 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 worker 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 and obligations? |
What do worker experience and workplace studies show?
In OECD surveys 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 reported experiences among the surveyed population, not a guarantee that AI will improve every job or workplace. The OECD also found that training and worker consultation were associated with better worker outcomes. OECD, Using AI in the workplace: Opportunities, risks and policy responses (15 March 2024); OECD report PDF.
ILO and Joint Research Centre case studies in logistics and healthcare across France, Italy, India, and South Africa found that effects on job quality and monitoring differed by country and context. A finding from one sector, country, or tool should not be treated as a universal result. Uma Rani, ILO Senior Economist and co-author of the case-study 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.” ILO, “Algorithmic management practices in regular workplaces are already a reality” (26 February 2024).
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.When do employment-related AI rules matter?
In the European Union, Recital 57 of the AI Act identifies specified employment and worker-management AI uses as high-risk. Examples include 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. It does not mean that every tool used to automate a work task has the same classification. Employers should assess the system’s actual use and the jurisdiction that applies. European Commission AI Act Service Desk, Recital 57.
Quick Recap
What the evidence cannot decide for an employer
- An occupation-level exposure estimate cannot substitute for evidence about particular tasks, a system’s reliability, or the employer’s workflow.
- Survey perceptions and case studies provide useful signals, not guaranteed results for a different employer, role, country, or AI system.
- The cited sources do not establish universal pilot metrics or a single threshold for eliminating or redesigning a role.
- No particular provider is required by the cited evidence; the assessment should focus on the system, tasks, and workplace outcomes.
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