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What changes when AI moves from answering to acting?
A generative AI tool may help draft a response or summarize information. An AI agent is generally intended to pursue a bounded goal through a sequence of steps, using available information or tools along the way. That can shift a worker’s role from performing every step to defining the task, checking the agent’s work and deciding what to do when the situation falls outside its instructions.
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This does not mean an agent can safely handle every task from beginning to end. Workflows vary in how clearly their goals can be specified, how costly an error would be, and how much context or judgment a task requires. Where a process is predictable and its output can be checked, an organization may be able to delegate more execution. Where stakes or ambiguity are higher, human review and decision-making remain central.
The distinction matters when interpreting evidence. Much of the current labor-market research covers generative AI or workplace AI broadly, not agents specifically. Those findings can help explain possible pressures on work, but they are not proof of what an agent deployment will do in every occupation.
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What the evidence says—and what it does not
The ILO’s June 2026 review finds evidence of productivity gains, but says they are often unverified and uneven. In the evidence it reviews, large-scale displacement is limited; that is an assessment of current evidence, not a guarantee about future employment. The review concerns generative AI broadly, not AI agents alone.
Several reported figures help show why measures and populations matter:
| Measure | What it covers |
|---|---|
| Task-level productivity gains of 10% to 70% | The ILO’s May 2026 brief on the AI aggregation paradox summarizes task-level evidence, with stronger effects for less experienced workers and well-defined, text-intensive tasks. This range is not a guaranteed gain for an organization or an agent-specific result. ILO, 6 May 2026 |
| Four in five workers surveyed said AI improved their performance; three in five said it increased their enjoyment of work | Self-reported responses in OECD AI surveys of employers and workers, not measured productivity or causal evidence, and not specific to agents. OECD, 15 March 2024 |
| About 27% of employment in OECD countries is in occupations at highest risk of automation when AI’s effect is considered | An occupational exposure and risk framing, not a forecast that this share of jobs will disappear. OECD, 15 March 2024 |
| AI adoption among firms in OECD countries rose from around 7% to 20% between 2021 and 2025 | The OECD attributes the rise partly to generative AI diffusion and identifies skills shortages as an adoption barrier. OECD, 2026 |
| 20,000 knowledge workers across 10 markets | Microsoft’s 2026 Work Trend Index surveyed full-time employed or self-employed knowledge workers who used AI at work. Fieldwork ran from 18 February to 7 April 2026; this was not a representative sample of all workers. Microsoft, 5 May 2026 |
| 49% of more than 100,000 Microsoft 365 Copilot conversations supported cognitive work | Microsoft’s privacy-preserving analysis of conversations on that product; it does not measure the share of all work performed by agents. Microsoft, 5 May 2026 |
| 19% of surveyed AI users were classed as “Frontier” and 16% as “stalled” | Categories defined by Microsoft’s report, not shares of the labor market. Microsoft, 5 May 2026 |
Even a real improvement on an individual task may not raise output at the scale of a company or economy. The ILO’s aggregation-paradox brief explains that adoption, organization and institutions can prevent task-level gains from scaling up. A faster step in a process may simply move the bottleneck elsewhere; broader gains depend on how the work is organized around it.
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How jobs and teams may be reorganized
When agents are introduced, change may appear first in the tasks that make up a job rather than in the job title itself. Workers may spend less time on some routine execution and more time framing requests, supplying context, reviewing outputs, coordinating handoffs or dealing with unusual cases. Which tasks shift—and how much—depends on the actual workflow and the system’s reliability, not simply on an occupation’s name.
That redistribution can affect how teams coordinate. If an agent performs a step that used to be a handoff between people, responsibilities for checking, correcting and escalating the result still need to be assigned. If several tasks become faster but require more review, the total workload may not fall. Clear ownership matters because automation does not transfer an employer’s or worker’s responsibility for consequential decisions to a software system.
Evidence on exposure should not be mistaken for a list of occupations that will vanish. The OECD’s estimate describes risk across occupations in its member countries; it does not identify a certain outcome for each worker. The ILO’s 2025 analysis also examines AI adoption and jobs without turning exposure into a prediction of net job losses. ILO, 31 May 2025
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Which skills become more important?
Using AI effectively is not just a matter of learning how to write a prompt. The ILO’s August 2026 review of skills in the age of AI identifies changing needs across cognitive, socioemotional, digital and AI skills, as well as adaptability, resilience and human agency. In practical terms, workers need to understand a task well enough to give useful direction, recognize a weak result, communicate with colleagues and adjust when a process changes.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What could go wrong for workers?
AI can make some work easier or more enjoyable, as workers reported in the OECD surveys, while also creating risks. The OECD and ILO raise concerns about work intensity, worker data, inequality, surveillance, autonomy and job quality. These are risks to manage, not inevitable effects of every AI system.
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Monitoring systems or data-driven management can narrow a worker’s discretion if decisions are made from incomplete measures or if people have little ability to question them. Unequal access to training and to better-supported roles could also distribute benefits unevenly. The ILO’s review of the changing psychosocial work environment discusses these concerns in the context of AI systems at work. ILO, 30 April 2026
The ILO’s 2026 review also identifies potential inequality, reduced opportunities for younger workers and changes to autonomy and job quality. These outcomes are not established as universal consequences; they are reasons to examine who gains access to learning opportunities, how work is monitored and whether workers retain meaningful control.
How to judge an AI-agent rollout at work
A vendor demonstration or a faster result on one task does not establish that a deployment improves the whole job. Workers and managers can use these questions to evaluate a rollout:
- What is being measured? Separate the speed or quality of a specific task from output, cost or service quality across the full workflow.
- Who and what were studied? Check whether evidence is agent-specific, which tasks and workers it covers, and whether the result comes from a survey, a task study or operational data.
- How are errors handled? Identify who reviews outputs, what requires escalation, and who is accountable for the final decision.
- What happens to job quality? Examine autonomy, workload, surveillance and data handling alongside any efficiency measures.
- Who receives support and benefits? Ask who gets training, whose work changes, whether workers were consulted and how productivity gains are distributed.
- Was the workflow redesigned? Determine whether roles, handoffs and review time were planned, rather than assuming a tool will improve a process on its own.
Microsoft’s 2026 survey reports an association between organizational factors such as culture and manager support and reported AI impact among its surveyed AI-using knowledge workers. Because the findings are survey evidence from a defined population, they do not establish that those factors caused the reported impact across workplaces generally.
Will AI agents take your job?
There is no single reliable global estimate in the available evidence for net job losses caused specifically by AI agents. Current findings are stronger on task exposure and uneven productivity effects than on universal job replacement. For an individual worker, the more useful question is which parts of the work are changing, what new review or coordination duties may appear, and whether the employer provides the skills, authority and support to handle them.
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