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Policymakers Urged to Broaden AI Focus Beyond Jobs and Wages
New research indicates that teh impact of Artificial Intelligence (AI) on the workforce extends far beyond employment numbers and salary figures,prompting calls for a more thorough approach to technology policy and labor regulation.
The findings underscore a critical need for policymakers to expand their considerations beyond conventional metrics like employment and wages when addressing the integration of AI into the workplace. The study highlights that AI’s transformative effects on aspects such as worker stress, autonomy, sense of purpose, and overall health are significant and require dedicated policy attention. As noted by Martin and Hauret (2022), the quality of a job encompasses not only income but also factors like working hours, safety, and employee well-being.
Furthermore, the evidence emerging from Germany suggests that existing institutional frameworks play a crucial role in the smooth adoption of AI. Institutions such as works councils, co-determination policies, and robust employment protections appear to have facilitated a less psychologically taxing integration of AI for workers in Germany. Nations lacking comparable institutional safeguards may need to explore alternative protective measures, perhaps through regulatory frameworks, collective bargaining agreements, or the establishment of ethical design standards for AI technologies.
While widespread job displacement due to AI may be an overestimation, the research confirms that concerns regarding the degradation of job quality are valid and already manifesting. Consequently, policies that concentrate solely on reskilling initiatives or job matching may inadvertently overlook the broader human implications of AI’s increasing presence in the labor market.
the study emphasizes the urgent requirement for improved data collection methods. The observed discrepancies between objective measures of AI exposure and workers’ self-reported experiences highlight the necessity for more detailed surveys that capture task-level AI usage and real-time indicators of both AI implementation and worker outcomes.
References
Acemoglu, D, D Autor, J Hazell and P Restrepo (2022), “Artificial Intelligence and Jobs: Evidence from Online Vacancies”, Journal of Labor Economics 40(S1): S293-S340.
Bonfiglioli, A, R Crinò, G Gancia and I Papadakis (2025), “Artificial Intelligence and Jobs: Evidence from US Commuting Zones”, Economic Policy 40(121): 145-94.
Brynjolfsson, E, D Li and L Raymond (2025), “Generative AI at Work”, Quarterly Journal of Economics, forthcoming.
Cerutti, E, AG Pascual, Y Kido, L Li, G Melina, MM tavares and P Wingender (2025), “Global Impact of AI: Mind the Gap”, IMF Working Paper No. 25/76.
Frey, CB, G Presidente and P Andres (2025), “Redirecting AI: Privacy, Regulation and the Future of Artificial Intelligence“, VoxEU.org, 5 January
Gihleb, R, O Giuntella, L Stella and Y Wang (2022), “Industrial Robots, Workers’ Safety, and Health”, Labour Economics 78: 102205.
Giuntella, O, J König and L Stella (2025), “Artificial Intelligence and the Wellbeing of Workers”, Scientific Reports 15(1): 20087.
Ilzetzki, E, and S Jain (2023), “The Impact of Artificial Intelligence on Growth and Employment“, VoxEU.org, 20 June.
Martin, L, and L Hauret (2022), “Digitalisation, Job Quality, and Subjective Well-being”, in Handbook of Labor, Human Resources and Population Economics.
Nazareno, L, and D Schiff (2021), “The Impact of Automation and Artificial Intelligence on Worker Well-being”, Technology in society 67: 101679.
