Artificial intelligence and the transformation of employment: a systematic literature review

This article examines the impact of artificial intelligence (AI) on the labor market, focusing on the dynamics of job creation and elimination within the context of the Fourth Industrial Revolution. The primary focus is on analyzing current trends and providing forecasts regarding the future interaction between AI and employment. The methodology is based on a systematic review of academic literature and a quantitative analysis of data on publications and citations from the Scopus database. A key element of the study was the content analysis of the 25 most-cited publications, which allowed for the identification of key methodological approaches, research directions, and main findings related to the impact of AI on various sectors of the labor market. The analysis showed that authors primarily focused on topics such as the impact of AI on high-skilled jobs, process automation, and human resource management. The results demonstrate a significant increase in interest in this topic since 2016, with research activity peaking in 2022. A geographic analysis of publications highlighted the leading positions of the United States and India, underscoring the global nature of research activity. Key authors whose works have significantly influenced the development of this field were also identified. In conclusion, the study emphasizes the need for further research aimed at deepening the understanding of the relationship between AI and the labor market, which will help build a sustainable and inclusive economy in the future.

1. Robertson J. (2024) The legacy of Adam Smith: government and economic development in the Wealth of Nations // Victorian Liberalism. London: Routledge. Р. 15–41. (In English).

2. Dmitriev V. (2024) The Theory of Value of David Ricardo: An Attempt at a Rigorous Analysis // The Foundations of Price Theory. London: Routledge. Vol. 6. Р. 159–218. (In English).

3. Spencer D.A. (2024) Marx, Keynes and the future of working time // Cambridge Journal of Economics. Vol. 48. No. 1. P. 25–40. (In English).

4. Bessen J. (2022) AI and Jobs: The Role of Demand // National Bureau of Economic Research. URL: http://www.nber.org/papers/ (accessed: 14.05.2025) (In English).

5. Brynjolfsson E., Mitchell T. (2021) What Can Machine Learning Do? Workforce Implications // Science. Vol. 358, no. 6370, pp. 1530–1534. (In English).

6. Fossen F., Sorgner A. (2022) AI and the Labor Market: Impacts, Risks, and Policies // Economic Policy. Vol. 37, no. 109, pp. 127–152. (In English).

7. Stevenson B. (2019) AI, Income, and Employment: Economic Perspectives // The Economics of Artificial Intelligence: An Agenda. Chicago: University of Chicago Press. P. 45–76. (In English).

8. Tokaev K.K. Kazahstan v jepohu iskusstvennogo intellekta: aktual’nye zadachi i ih reshenija cherez cifrovuju transformaciju: Poslanie Glavy gosudarstva narodu Kazahstana // Ofic.sajt Pravitel’stva Respubliki Kazahstan (GOV. KZ). Astana, 2025. URL: https://www.gov.kz/memleket/entities/tsm/press/news/details/1063655?lang=ru (data obrashhenija: 13.09.2025) (In Russian).

9. Salimhanova A.S. (2021) Ispol’zovanie iskusstvennogo intellekta i sistemy podderzhki prinjatija reshenij v oblasti zashhity okruzhajushhej sredy // Vestnik Kazahstansko-Britanskogo tehnicheskogo universiteta. No.18(1). Р. 143–149. DOI: 10.55452/1998-6688-2021-18-1-143-149. (In Russian).

10. Huang M.-H., Rust R.T. (2018) Artificial Intelligence in Service // Journal of Service Research. Vol. 21. No. 2. P. 155–172. DOI: 101177/1094670517752459. (In English).

11. Mitchell M., Wu S., Zaldivar A., Barnes P., Vasserman L., Hutchinson B., Spitzer E., Raji I. D., Gebru T. (2019) Model Cards for Model Reporting // Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT*’19). New York: ACM. 10 p. DOI: 10.1145/3287560.3287596. (In English).

12. Makridakis S. (2017) The Forthcoming Artificial Intelligence (AI) Revolution: Its Impact on Society and Firms // Futures. Vol. 90. P. 46–60. DOI: 10.1016/j.futures.2017.03.006. (In English).

13. Zeng Z., Chen P.-J., Lew A.A. (2020) From High-Touch to High-Tech: COVID-19 Drives Robotics Adoption // Tourism Geographies. Vol. 22. No. 3. P. 724–734. DOI: 10.1080/14616688.2020.1762118. (In English).

14. Toorajipour R., Sohrabpour V., Nazarpour A., Oghazi P., Fischl M. (2021) Artificial Intelligence in Supply Chain Management: A Systematic Literature Review // Journal of Business Research. Vol. 122. P. 502–517. DOI: 10.1016/j.jbusres.2020.09.009. (In English).

15. Li J., Bonn M.A., Ye B.H. (2019) Hotel Employee’s Artificial Intelligence and Robotics Awareness and Its Impact on Turnover Intention: The Moderating Roles of Perceived Organizational Support and Competitive Psychological Climate // Tourism Management. Vol. 73. P. 172–181. DOI: 10.1016/j.tourman.2019.02.006. (In English).

16. Kaplan A., Haenlein M. (2020) Rulers of the World, Unite! The Challenges and Opportunities of Artificial Intelligence // Business Horizons. Vol. 63. No. 1. P. 37–50. DOI: 10.1016/j.bushor.2019.09.003. (In English).

17. Kuziemski M., Misuraca G. (2020) AI Governance in the Public Sector: Three Tales from the Frontiers of Automated Decision-Making in Democratic Settings // Telecommunications Policy. Vol. 44. No. 6. Art. 101976. DOI: 10.1016/j.telpol.2020.101976. (In English).

18. Furman J., Seamans R. (2019) AI and the Economy // Innovation Policy and the Economy. Vol. 19. No. 1. P. 161–191. DOI: 10.1086/699936. (In English).

19. Modgil S., Singh R. K., Hannibal C. (2022) Artificial Intelligence for Supply Chain Resilience: Learning from COVID-19 // The International Journal of Logistics Management. Vol. 33. No. 4. P. 1246–1268. DOI: 10.1108/IJLM-02-2021-0094. (In English).