Upskilling Older Employees in the Artificial Intelligence Era
Izpopolnjevanje starejših zaposlenih v dobi umetne inteligence
DOI:
https://doi.org/10.37886/ip.2025.007Keywords:
knowledge society, upskilling, knowledge management, retraining, older employees, artificial intelligence, lifelong learningAbstract
Research Question (RQ): What is the effect of new technologies, with an emphasis on artificial intelligence (AI), for the need to upskill older employees (50+ years).
Purpose: The purpose of the research was to carry out a systematic literature review of existing research in the field of the effect of AI on the upskilling needs of older employees.
Method: We performed a systematic literature review in six academic search engines, ProQuest, Emerald, Sage Journals, Springer, Research Gate and Google Scholar.
Results: Artificial intelligence is significantly transforming the labor market, as it requires constant adaptation to new skills and knowledge. AI has a significant effect on older employees, who are exposed to greater challenges due to a possible lack of digital skills and sensitivity to change. In this context, training and further education are key mechanisms to ensure that skills match the requirements of the work environment and the labor market. Organizations must quickly adapt to changing requirements by creating a culture of lifelong learning that encourages seniors and other employees to improve. It is crucial that training programs are based on the specific needs and challenges faced by older employees.
Organization: The research emphasizes the importance of training older employees in the age of AI and encourages organizations to create a culture of lifelong learning as part of the organization's strategic directions and goals.
Society: The importance of the research for society is reflected in the insight into the involvement of all age groups in the possibility of improving knowledge, skills and attitudes towards the use of modern technologies. Organizations and society itself bear the social responsibility to enable older employees to successfully integrate into the work environment in the AI era.
Originality: The research addresses the need to improve the skills of a specific age group in the age of AI, where it simultaneously highlights the importance of fostering a culture of lifelong learning in a rapidly changing world. The research findings provide guidelines for policymaking in the field of training on national level in the context of an aging workforce and new technologies.
Limitations/further research: The literature review was limited to six publicly available databases. In the article, older employees were considered as all people in the labor process older than 50 years. We must emphasize that older employees differ from each other in terms of education, economic, social and other circumstances. It would be appropriate to investigate the effect of new technologies also regarding the mentioned circumstances in this age group.
References
Acemoglu, D., & Restrepo, P. (2021). Demographics and automation. The Review of Economic Studies, 89(1), 1–44.
Aisa, R., Cabeza, J., & Martin, J. (2023). Automation and aging: The impact on older workers in the workforce. The Journal of the Economics of Ageing, 26, 1–12.
Alcover, C.-M., Guglielmi, D., Depolo, M., & Mazzetti, G. (2021). Aging-and-Tech Job Vulnerability: A proposed framework on the dual impact of aging and AI, robotics, and automation among older workers. Organizational Psychology Review, 11(2), 175–201.
Bianco, A. (2021). Ageing workers and digital future. Rivista trimestrale di scienza dell'amministrazione, 3(3), 1–22.
Birkstedt, T., Minkkinen, M., Tandon, A., & Mäntymäki, M. (2023). AI governance: themes, knowledge gaps and future agendas. Internet Research, 33(7), 133–167.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T., Mulrow, C.,… Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. British Medical Journal.
Bokek-Cohen, Y. (2018). Conceptualizing employees’ digital skills as signals delivered to employers. International Journal of Organization Theory & Behavior, 21(1), 17–27.
Bruun, E.P.G., & Duka, A. (2018). Artificial Intelligence, Jobs and the Future of Work: Racing with the Machines. Basic Income Studies, 13(2), 1–15.
Casas, P., & Román, C. (2024). The impact of artificial intelligence in the early retirement decision. Empirica, 51, 583–618.
Chetty, K. (2023). AI literacy for an ageing workforce: Leveraging the experience of older workers. OBM Geriatrics, 7(3), 1–17.
Classen, J., Wegemer, D., Patras, P., Spink, T., & Hollick, M. (2018). Anatomy of a Vulnerable Fitness Tracking System: Dissecting the Fitbit Cloud, App, and Firmware. In T. Ploetz & L. Mamykina (Eds.), Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2(1), (pp. 1–24). ACM digital Library.
Cramarenco, R. E., Burcă-Voicu, M. I., & Dabija, D.-C. (2023). The impact of artificial intelligence (AI) on employees’ skills and well-being in global labor markets: A systematic review. Oeconomia Copernicana, 14(3), 731–767.
Cros, F., Bobillier Chaumon, M.-E., & Cuvillier, B. (2021). Is the obsolescence of the skills of older employees an inevitable consequence of digitalization? In M.-E. Bobillier Chaumon (Ed.), Digital transformations in the challenge of activity and work: Understanding and supporting technological changes (pp. 169–181). Retrieved 23 March 2024 from https://doi.org/10.1002/9781119808343.ch13.
Dolničar, V., Vukčevič, K., Kronegger, L., & Vehovar, V. (2002). Digitalni razkorak v Sloveniji. Družboslovne razprave, 18(40), 83–106.
Hughes, C., Robert, L., Frady, K., & Arroyos, A. (2019). A Hiring Paradigm Shift through the Use of Technology in the Workplace. In E. Parry (Ed.), Managing Technology and Middle- and Low-skilled Employees: Advances for Economic Regeneration, 49–59. Retrieved 19 January 2024 from https://doi.org/10.1108/9781789730777.
Janeš, A., Madsen, S. S., Saure, H. I., Lie, M. H., Gjesdal, B., Thorvaldsen, S., ….Klančar, A. (2023). Preliminary Results from Norway, Slovenia, Portugal, Turkey, Ukraine, and Jordan: Investigating Pre-Service Teachers’ Expected Use of Digital Technology When Becoming Teachers. Education Sciences, 13(8), 783.
Komp-Leukkunen, K., Poli, A., Hellevik, T., Herlofson, K., Heuer, A., … Motel Klingebiel, A. (2022). Older workers in digitalizing workplaces: A systematic literature review. The Journal of Aging and Social Change, 12(2), 37–59.
Krašovec, S. J. (2015). Izobraževanje in usposabljanje starejših delavcev–mednarodna primerjava. Andragoška spoznanja, 21(2), 29–46.
Lee, C. C., Czaja, S. J., & Sharit, J. (2008). Training older workers for technology-based employment. Educational Gerontology, 35(1), 15–31.
Li, C., Zhang, Y., Niu, X., Chen, F., & Zhou, H. (2023). Does artificial intelligence promote or inhibit on-the-job learning? Human reactions to AI at work. Systems, 11(114), 1–26.
Lincoln, J. (2017). An Ageing Workforce in The Digital Era: Older Workers, Technology and Skills. Business in the Community, A Business in the Community report, supported by Tata Consultancy Services. UK: The Prince’s Responsible Business Network, Tata Consultancy Services. Retrieved 30 January 2025 from https://www.bitc.org.uk/wp-content/uploads/2022/12/bitc-report-age-ageing-worforce-digital-era-march20.pdf.
Morandini, S., Fraboni, F., De Angelis, M., Puzzo, G., Giusino, D., & Pietrantoni, L. (2023). The impact of artificial intelligence on workers’ skills: Upskilling and reskilling in organisations. Informing Science, 26, 39–68.
Nedelkoska, L., & Quintini, G. (2018). Automation, skills use and training (Vol. 202). Paris: OECD Publishing.
Novak, V. (2014). Izzivi dolgožive družbe: staranje prebivalstva, trg dela in ravnanje s starejšimi zaposlenimi. In M. Bernik (Ed.), Transformacija kadrovskega managementa (pp. 19–44). Maribor: Univerza v Mariboru.
OECD. (2024). Training Supply for the Green and AI Transitions: Equipping Workers with the Right Skills, Getting Skills Right. Paris: OECD.
Orr, W., & Davis, J. L. (2020). Attributions of ethical responsibility by Artificial Intelligence Practitioners. Information, Communication and Society, 23(5), 719–735.
Pradhan, I.P., & Saxena, P. (2023). Reskilling workforce for the artificial intelligence age: Challenges and the way forward. In The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part B (pp. 181–197). Emerald Publishing Limited.
Tiku, S. (2023). AI-Induced Labor Market Shifts and Aging Workforce Dynamics: A Cross-National Study of Corporate Strategic Responses in Japan, USA, and India. SSRN Electronic Journal.
Trunkina, L. V., Kipervar, E. A., & Mizya, M. S. (2019). Increasing the competitiveness of older age groups in the digitalization environment. In International Scientific and Practical Conference on Digital Economy (ISCDE 2019) (pp. 236–239). Atlantis Press.
Verdiesen, I., Tubella, A. A., & Dignum, V. (2021). Integrating comprehensive human oversight in drone deployment: a conceptual framework applied to the case of military surveillance drones. Information (Switzerland), 12(9), 1–13.
Vuorenkoski, V., Lehikoinen, A., Hakola-Uusitalo, T., & Urrila, P. (2018). Learning and skills in transition. In O. Koski & K. Husso (Eds.), Work in the age of artificial intelligence: Four perspectives on the economy, employment, skills and ethics. Work in the age of artificial intelligence (pp. 37–46). Publications of the Ministry of Economic Affairs and Employment.
Waligóra, Ł. (2024). Employees' age diversity - between supportive workplaces and organizational outcomes. Katowice: University of Economics in Katowice.
Zwick, T. (2015). Training older employees: what is effective? International Journal of Manpower, 36(2), 136–150.
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