Discussions surrounding artificial intelligence in the workplace often treat the technology as a single, uniform force that will either universally empower workers or inevitably displace them. However, a recent academic paper by Sangwoo Lee, titled “The Dual Face of Workplace AI: A Typology of Digital Work Configurations and Job Quality Implications in Europe,” challenges this simplistic narrative. Analysing survey data from over 50,000 workers across 27 European countries, the study demonstrates that the impact of AI on job quality depends fundamentally on how the technology is deployed alongside systems of managerial control. For those involved in vocational education and training (VET), the findings offer insights into the skills and institutional support workers will need to navigate the evolving digital landscape.

The core contribution of the paper is a typology that classifies digital work arrangements based on two intersecting dimensions: the intensity of a worker’s AI tool usage and their exposure to employer digital control, such as algorithmic management and digital monitoring. This classification yields four distinct configurations. The “Non-Digital Baseline” represents traditional work with low AI use and minimal digital oversight. The “Heavily Controlled” group, which constitutes the majority of the European workforce in the sample, experiences low AI adoption but intense algorithmic management and surveillance. A small minority falls into the “Digitally Empowered” category, where workers use AI extensively while maintaining high autonomy. Finally, “Managed Augmenters” are those who use AI tools frequently but do so under conditions of strict digital monitoring.

The study reveals that these different configurations are associated with profoundly divergent job quality outcomes. Workers in the “Digitally Empowered” group experience substantial earnings premiums and report higher levels of skill use and discretion. In stark contrast, the “Heavily Controlled” configuration is associated with reduced autonomy, poorer social environments, and increased work intensity, without any corresponding increase in pay. Crucially, providing AI tools to heavily monitored workers – the “Managed Augmenters”-does not offset the negative effects of digital surveillance. While these workers see some wage benefits compared to the baseline, they continue to face constrained autonomy and intensified work pressures.

These findings suggest that the dividing line between beneficial and detrimental technological change lies in workplace design and governance, rather than the technical capabilities of AI itself. The study also highlights significant cross-national variation, with some European countries exhibiting much higher rates of heavy digital control than others. This variation points to the importance of national institutions, employment protections, and mechanisms for worker voice in shaping how digital transformation unfolds on the ground.

For educators, trainers, and managers in the VET sector, the implications are substantial. If the goal of vocational learning is to prepare individuals for sustainable, high-quality employment, training programmes must move beyond merely teaching the technical operation of AI tools. While digital literacy remains essential, the evidence suggests that simply upskilling workers in AI usage risks pushing them into “Managed Augmenter” roles, where they face the dual pressures of complex tool use and intense algorithmic surveillance.

Instead, VET curricula should also focus on developing workers’ capacity to exercise professional judgement, negotiate the conditions of their work, and understand the implications of workplace data collection. Preparing learners for the future of work means equipping them to advocate for organisational designs that preserve human autonomy. Furthermore, policymakers and industry leaders must recognise that expanding access to AI will not automatically improve job quality unless it is accompanied by governance frameworks that restrict excessive monitoring and ensure that the productivity gains of new technologies are shared equitably.

In conclusion, “The Dual Face of Workplace AI” provides a necessary corrective to technological determinism. It shifts the critical question from whether AI will improve work to the specific organisational conditions under which those improvements can be realised. As European labour markets continue to integrate intelligent systems, the task for the VET community is to foster not just technical competence, but the broader capabilities required to shape a digital work environment that respects and elevates human labour.

Reference

Lee, S. (2026). The Dual Face of Workplace AI: A Typology of Digital Work Configurations and Job Quality Implications in Europe. New Technology, Work and Employment. https://onlinelibrary.wiley.com/doi/epdf/10.1111/ntwe.70040