How Has the Adoption of Machine Learning in Service Industries Influenced Labour Demand for Mid-Skill Workers?

One obstacle to second hand research on the subject is data. The AI adoption data provided by the U.S. Census (Bonney et al., 2024) estimated it to be approximately 10-percent of firms, and the business subscription data (Ramp AI Index) stands at approximately 44-percent, which is four times less different between conceptualisations and measures (Stanford Digital Economy Lab, 2025). As of 2023, most econometric studies are founded on data up until now and remain incapable of fully reflecting the entire generative AI effect. The Stanford Digital Economy Lab has very little evidence regarding the actual changes in the workers tasks after they have adopted ML (survey based) and limited evidence regarding longitudinal income of the workers who have been displaced by ML (both survey based identified as a gap in the existing body of knowledge). Moreover, evidence base is biased towards developed economies, in particular, the United States and Western Europe. The adoption of service sector ML and the impact it has on middle-income economy mid-skilled workers have limited coverage, which will become more crucial as adoption of ML is adopted globally in investment in digital infrastructure and the falling cost of compute power.

●      Acemoglu, D., & Autor, D. (2011). Skills, tasks and technologies: Implications for employment and earnings. Handbook of Labor Economics, 4, 1043–1171. ●      Acemoglu, D., & Loebbing, J. (2024). Automation and polarization. NBER Working Paper No. 30528.https://economics.mit.edu/sites/default/files/202402/Automation%20and%20Polarization.pdf  ●      Acemoglu, D., & Restrepo, P. (2020). Robots and jobs: Evidence from US labor markets.Journal of Political Economy, 128(6), 2188–2244  ●                 ALM    Corp.       (2026).     AI job displacement statistics 2026–2030: 60+            data      points. https://almcorp.com/blog/ai-job-displacement-statistics/ ●      Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. Quarterly Journal of Economics, 118(4), 1279–1333. ●      Autor, D. H., & Dorn, D. (2013). The growth of low-skill service jobs and the polarization of the US labor market. American Economic Review, 103(5), 1553–1597. ●      Autor, D. H. (2024). Applying AI to rebuild middle class jobs. NBER Working Paper No.32140. ●      Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. Quarterly Journal of Economics. ●      Bureau of Labor Statistics (BLS). (2026). Industry and occupational employment projections overview and highlights, 2024–34. Monthly Labor Review. https://doi.org/10.21916/mlr.2026.1 ●      Dallas Federal Reserve. (2026, February). AI is simultaneously aiding and replacing workers, wage data suggest. https://www.dallasfed.org/research/economics/2026/0224 ●              DemandSage.  (2026).  77 AI job replacement statistics 2026. https://www.demandsage.com/ai-job-replacement-stats/  ●      Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. arXiv:2303.10130. https://arxiv.org/abs/2303.10130 ●      Gloat. (2025). AI employment impact. https://gloat.com/blog/ai-employment-impact/