How the interaction of AI and algorithmic management shapes job quality outcomes

IER's Dr Sangwoo Lee has been published in New Technology, Work and Employment. The study, titled "The Dual Face of Workplace AI: A Typology of Digital Work Configurations and Job Quality Implications in Europe," challenges conventional narratives that technological exposure alone dictates workforce outcomes. The research maps how worker-led AI tool usage and employer-led algorithmic management interact to create deeply stratified workplace realities. The findings reveal that identical intensities of AI tool usage produce fundamentally different job quality outcomes depending on the surrounding organisational control infrastructure.

While "Digitally Empowered" workers who use AI under low algorithmic control enjoy a 21 percentage point earnings premium relative to country medians with their autonomy intact, "Managed Augmenters" with near-identical AI usage see that wage premium halved because intensive employer surveillance allows firms to capture more of the technological surplus. Conversely, workers subjected to intensive algorithmic management without direct AI access experience severe autonomy erosion and work intensification with no compensating wage premiums at all, highlighting a regime of constraint without compensation. Even highly autonomous AI users face a distinct catch, experiencing the lowest overall working time quality due to informal, market-driven pressures to deliver rapid, AI-assisted turnarounds.

The analysis indicates that supply-side policy frameworks focusing solely on worker upskilling or expanding technology access have structural limitations. Dr Lee demonstrates that without directly regulating algorithmic monitoring and control practices, expanding AI access risks simply transitioning more workers into high-intensity, highly managed configurations rather than genuinely empowering them.