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Not yet demonstrably. Current evidence does not establish that AI adoption has increased total employment worldwide. AI may create jobs through productivity gains, new tasks and greater demand, while reducing hiring or eliminating roles in other settings. The latest International Labour Organization (ILO) review finds limited evidence of large-scale displacement so far, but it does not report a single global employment effect attributable to AI. Much-cited exposure estimates and job forecasts answer different questions from whether employment has already risen.
What does the evidence say about global employment?
The most accurate answer to “Is AI adoption increasing employment globally?” is that the outcome is not established. AI adoption can change how much labor employers need, which tasks workers perform and what kinds of jobs are available. Those changes can raise employment in some places and reduce it in others. The net result depends on adoption, business demand, worker skills, sector and time period.
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The ILO’s review of empirical generative AI (GenAI) evidence, published 1 June 2026, examines experiments, firm-level evidence, platform studies, and worker and employer surveys from Australia, Denmark, Germany, Korea, Kuwait, the United Kingdom and the United States. It finds that large-scale displacement remains limited in the evidence it reviews. It also cautions that reported time savings have not yet translated into higher measured output, earnings or employment. The studies cover multiple settings and methods, not every country or sector, and the review does not provide a pooled global estimate of AI’s effect on employment.
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Why “jobs exposed to AI” does not mean jobs created or lost
An exposure estimate identifies jobs or tasks that AI might affect. It does not count people who have been hired, laid off or moved into new roles. Exposure can lead to different outcomes: a tool may help a worker complete tasks, change the mix of work in a job, reduce the need for some tasks, or make a role more valuable as demand grows.
The ILO’s 2025 GenAI index estimates that one in four workers globally is in an occupation with some exposure to GenAI. The ILO says job transformation is more likely than full replacement because many tasks still require human input. In the index’s highest exposure gradient, 3.3% of global employment falls in that category: 4.7% of female employment and 2.4% of male employment. These are estimates of occupational exposure, not predicted job losses or gains.
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The ILO developed its task-level framework using a representative sample of 29,753 tasks in Poland’s occupational classification. It collected 52,558 data points on perceived automation potential for 2,861 tasks and combined worker input, expert discussion and AI-assisted scoring to estimate global exposure gradients. The index assesses which work could be affected; it does not observe how many jobs AI adoption has created.
Why do global exposure estimates differ?
The IMF and ILO figures are not competing estimates of the same measure. The IMF’s 2024 analysis estimates exposure to AI broadly, while the ILO’s 2025 index focuses on generative AI and occupational task exposure. Their scope and methods differ.
| Estimate | What it measures | What it does not establish |
|---|---|---|
| ILO, 2025: one in four workers globally | Share of workers in occupations with some GenAI exposure | How many jobs will be created or lost |
| IMF, 2024: almost 40% of global employment | Share of global employment exposed to AI broadly; the IMF estimates about 60% of jobs in advanced economies may be impacted | A directly comparable GenAI exposure rate or a net employment change |
Neither percentage is an employment forecast. The IMF describes two possible paths for exposed work: AI can complement workers and enhance productivity, or applications can perform key tasks and reduce labor demand, potentially affecting wages and hiring. The actual outcome depends on how employers use the technology and how work and demand change.
What do forecasts say about jobs created and displaced?
The World Economic Forum’s Future of Jobs 2025 report projects that 170 million jobs will be created and 92 million displaced by 2030, for a net increase of 78 million. This is a forecast based on employer expectations and ILO employment data across multiple labor-market trends, not an AI-only estimate. The report considers shifts including technology, demographics, economic growth and the green transition. Its net figure should therefore not be presented as the number of jobs AI will add.
The WEF also estimates changing shares of tasks done by people, technology or human-machine collaboration. Those shares do not indicate the absolute amount of work output. A task-share change is not, by itself, evidence of a job being created or eliminated.
How do employment effects vary by occupation and region?
Exposure is uneven. In the ILO’s 2025 index, clerical occupations remain the most exposed. Exposure has also grown for some digitized media, software and finance work. The index estimates that 11% of employment in low-income countries and 34% in high-income countries is in occupations with some GenAI exposure. Its highest exposure gradient is also larger for female than male employment.
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These differences describe exposure, not the amount or direction of employment change. The ILO’s 2026 evidence review draws on seven countries, and its findings cannot be assumed to apply identically across regions or industries. OECD analysis also reports greater GenAI exposure among high-skilled workers and women, and greater potential impact in metropolitan areas. A highly exposed professional job may be transformed rather than eliminated; a less exposed occupation may still be affected indirectly as businesses and demand change.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can earlier automation studies tell us?
Earlier automation research offers context, but it is not a direct measurement of generative AI’s effect. In 2024, the OECD reported that, in regional historical analysis, a 10% increase in the share of jobs at high risk of automation was associated with a 5.6% increase in labor productivity over five years. This is an association, not proof that AI caused global employment growth. The OECD found that regional employment did not decline on average over the prior decade, but some regions experienced employment losses, and new jobs did not necessarily benefit workers displaced by automation.
The OECD also reported that four in five workers surveyed said AI improved their work performance, while three in five said it increased their enjoyment of work. Those are survey responses, not measured productivity gains or evidence of higher employment. They describe workers’ reported experience rather than a count of jobs or output.
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AI can contribute to employment growth if it helps businesses produce more, lowers costs or enables new products and services, and the resulting demand leads employers to hire. It can also create work around developing, integrating and maintaining AI systems. But productivity improvements do not automatically produce more jobs: an employer may meet demand with the same staff, reduce hiring, or reorganize existing roles.
Conversely, when AI performs tasks that previously required workers, employers may need fewer people for those tasks. The effect on total employment depends on what happens next: whether demand expands, displaced workers can move into other work, and new roles emerge quickly enough and in places workers can access. The evidence available does not yield one global balance across these channels.
Quick Recap
How to read claims that AI is a “job creator”
- Check the measure. Exposure, adoption, task automation, productivity, hiring, layoffs and employment totals are different outcomes.
- Check the technology scope. Broad AI estimates and GenAI estimates should not be treated as interchangeable.
- Check whether the number is observed or forecast. Employer expectations about jobs through 2030 are not observed outcomes.
- Check what is attributed to AI. A forecast combining several economic and technological trends cannot be credited to AI alone.
- Check who and where are covered. An estimate from selected countries, firms or occupations may not describe the world as a whole.
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