Artificial intelligence and labour markets: what we know so far

Artificial intelligence (AI) is already present in workplaces across Europe and beyond. From recruitment software to automated scheduling, from generative tools supporting knowledge work to algorithmic systems coordinating tasks, AI's presence is no longer applied to future scenarios. Yet its impact on jobs, skills and labour markets remains widely misunderstood.

 

The European Training Foundation’s latest analysis of the impact AI in labour markets brings clarity to a debate often shaped by speculation. Drawing on international evidence and recent empirical studies, the report shows that AI’s effects are often uneven and shaped by policy choices. What emerges is neither a story of mass job destruction nor one of automatic progress, but a more complex picture of work in transition.

One of the report’s central findings is that job transformation outweighs job loss. While a large share of jobs will undergo significant task changes, only a limited number of roles can be fully automated. Most occupations combine tasks that can be automated, tasks that are reshaped by AI support, and tasks that remain firmly human.

 

This task‑based perspective matters. It helps explain why employment levels in many AI‑exposed sectors have remained broadly stable so far, even as work content changes rapidly. AI reallocates tasks within jobs rather than replacing entire occupations, particularly in knowledge‑intensive roles. The result is a reshaping of how work is done, not a sudden disappearance of work itself.

 

At the same time, the report flags a structural risk that deserves close attention. Entry‑level jobs are more exposed than senior roles. Many routine cognitive tasks traditionally assigned to junior staff are now automated or assisted by AI systems. This weakens career ladders and raises longer‑term concerns for skills development and professional progression.

AI does not affect all workers in the same way. Highly educated and digitally skilled workers tend to benefit most, often using AI as a productivity‑enhancing tool that supports learning and autonomy. On the flip-side, for others, AI is experienced less as an assistant and more as a source of pressure.

 

The report highlights the growing use of algorithmic management. These systems automate managerial functions such as task allocation or performance monitoring. Once associated mainly with platform work, they are now spreading into logistics, manufacturing, services and office‑based environments. Their impact depends less on the technology itself than on how organisations choose to deploy it.

 

In some contexts, algorithmic tools improve coordination or support safer working practices. In others, they intensify work, reduce discretion and increase surveillance. This expansion of workplace data collection raises questions about privacy, transparency and the balance of power at work.

The most immediate effects of AI are visible in job quality, not employment numbers. The report documents a wide range of outcomes. Some workers experience more engaging tasks and clearer access to learning opportunities. Others face higher work intensity, limited scope to use their skills, or increased psychosocial strain.

 

Low‑skilled and routine jobs are more likely to experience work intensification without corresponding gains in autonomy or pay. High‑skilled roles often benefit from AI‑driven support, yet even here the pace of work can increase. These mixed effects underline the importance of organisational practices and labour institutions in shaping outcomes.

AI tends to amplify existing labour market inequalities. Education remains the strongest predictor of who benefits. Workers with limited digital skills or lower incomes face higher risks of job degradation. Women are overrepresented in clerical and administrative roles with higher exposure to automation and remain underrepresented in AI development and advanced digital occupations.

 

The report also points to persistent bias in AI systems used for recruitment, evaluation and promotion. When trained on historical data, these tools can reproduce existing patterns of discrimination. Without deliberate corrective action, the digital divide risks hardening into an AI divide.

 

AI adoption is highly uneven across countries. Advanced economies, supported by stronger digital infrastructure and broader skills bases, are better positioned to benefit from AI‑related productivity gains. Many developing countries face a different starting point, with limited access to digital technologies and fewer opportunities for AI‑related skills development.

 

At the same time, millions of workers in lower‑income countries are integrated into the AI value chain through low‑paid micro‑task work such as data annotation and content moderation. These jobs are essential to AI development but often involve weak protections and intensive monitoring. Without policy intervention, AI risks widening global inequalities.

 

A consistent message runs through the analysis. AI’s impact is not predetermined. Countries with strong labour protections, effective social dialogue and forward‑looking skills policies are better equipped to steer AI towards job upgrading rather than job erosion.

 

Regulation has an important role to play. Recent EU frameworks set safeguards for high‑risk AI systems used in employment and education. Yet regulation on its own is insufficient. The report points to the need for sustained investment in digital and AI literacy, targeted upskilling and reskilling, and social protection systems that can adapt to changing forms of work.

 

As AI reshapes labour markets, we supports partner countries in understanding emerging trends and aligning education and training systems with evolving skills needs. Evidence, foresight and dialogue remain essential tools for navigating technological change.

 

 

Read the full report to explore the evidence, country perspectives and policy implications in detail: