Upskilling Older Employees in the Artificial Intelligence Era
Izpopolnjevanje starejših zaposlenih v dobi umetne inteligence
DOI:
https://doi.org/10.37886/ip.2025.007Ključne besede:
družba znanja, izpopolnjevanje, management izobraževanja, prekvalifikacija, starejši zaposleni, umetna inteligenca, vseživljenjsko učenjePovzetek
Raziskovalno vprašanje (RV): Kakšen je učinek novih tehnologij, s poudarkom na umetno inteligenco (UI), na potrebo po izpopolnjevanju starejših zaposlenih (50+ let).
Namen: Namen raziskave je bil opraviti sistematični pregled literature dosedanjih raziskav s področja učinka UI na potrebe po izpopolnjevanju starejših zaposlenih.
Metoda: Opravili smo sistematični pregled literature v šestih akademskih iskalnikih in sicer ProQuest, Emerald, Sage Journals, Springer, Research Gate ter v Google Učenjaku.
Rezultati: Umetna inteligenca pomembno preoblikuje trg dela, saj zahteva nenehno prilagajanje novim spretnostim in znanju. Pomemben učinek ima UI na starejše zaposlene, ki so zaradi morebitnega pomanjkanja digitalnih veščin in občutljivosti na spremembe izpostavljeni večjim izzivom. V tem kontekstu sta izpopolnjevanje in dodatna izobrazba ključna mehanizma za zagotavljanje skladnosti spretnosti z zahtevami delovnega okolja in trga dela. Organizacije se morajo hitro prilagajati spreminjajočim se zahtevam z oblikovanjem kulture vseživljenjskega učenja, ki spodbuja starejše in ostale zaposlene k izpopolnjevanju. Ključno je, da izobraževalni programi temeljijo na specifičnih potrebah in izzivih, s katerimi se soočajo starejši zaposleni.
Organizacija: Raziskava poudarja pomen izpopolnjevanja starejših zaposlenih v dobi umetne inteligence in organizacije spodbujanja k ustvarjanju kulture vseživljenjskega učenja, kot dela strateških usmeritev in ciljev organizacije.
Družba: Pomen raziskave za družbo se odraža v vpogledu vključenosti vseh starostnih skupin v možnosti izpopolnjevanja znanja, veščin in odnosa do uporabe sodobnih tehnologij. Organizacije in družba sama je namreč nosilec socialne odgovornosti, da starejšim zaposlenim omogočijo uspešno vključevanje v delovno okolje v dobi UI.
Originalnost: Raziskava naslavlja potrebo po izpopolnjevanju specifične starostne skupine v dobi UI, kjer sočasno osvetljuje pomen ustvarjanja kulture vseživljenjskega učenja v hitro se spreminjajočem svetu.
Omejitve/nadaljnje raziskovanje: Pregled literature je bil omejen na šest javno dostopnih baz podatkov. V članku so bili starejši zaposleni obravnavani kot vse osebe v delovnem procesu starejše kot 50 let. Pri tem je potrebno izpostaviti, da se starejši zaposleni med seboj razlikujejo glede na izobrazbo, ekonomske, socialne in druge okoliščine. Primerno bi bilo, da bi učinek novih tehnologij raziskali tudi glede na omenjene okoliščine pri tej starostni skupini.
Literatura
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.
Dodatne datoteke
Objavljeno
Kako citirati
Številka
Rubrike
Licenca
Avtorske pravice (c) 2025 Tinkara Žabar, Aleksander Janeš

To delo je licencirano pod Creative Commons Priznanje avtorstva-Deljenje pod enakimi 4.0 mednarodno licenco.
![]()



