AI TOOLS IN THE WORKPLACE: PRODUCTIVITY GAINS, DESKILLING RISKS AND THE ROLE OF CONTINUOUS EMPLOYEE EDUCATION

Authors

  • Tamara Papic Faculty of Technical Sciences, Singidunum University, Belgrade, Serbia Author

DOI:

https://doi.org/10.35120/sciencej0503369p

Keywords:

artificial intelligence, workplace productivity, deskilling, continuous education, upskilling

Abstract

The pace of AI adoption in today’s business world has sparked a debate about the dual nature of AI and its effect on human productivity. AI tools are great for work, but they also make employees overreliant, with weaker skills and less critical thinking. This paper examines the advantages of AI tools at the workplace level, together with the issues of cognitive offloading, deskilling and low learning motivation among employees. We conducted a systematic review of the literature on AI coding apps (GitHub Copilot, Cursor, etc.) and on corporate e-learning platforms (Coursera and Udemy) from the past several years. The review shows that these tools bring real productivity gains - in controlled experiments between 14% and 40% faster task completion - and that the gains are strongest for less experienced employees. At the same time, the same literature documents growing evidence of cognitive offloading and deskilling when AI assistance is used uncritically. The outcome of this research is that AI, combined with continuous education and the development of a learning culture (microlearning, MOOC-based upskilling and guided on-the-job learning), keeps employees motivated and more productive, while deskilling is reduced. The paper concludes that AI tools should not replace competence but should add to it. Management should build AI usage policies with clear ongoing education programs for employees. These findings matter to human resource managers, executives and policymakers who plan workforce development in the digital age.

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References

Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3-30. https://doi.org/10.1257/jep.33.2.3

Alasmari, T. (2024). MOOCs for reskilling and upskilling: A perspective of employees' acceptance. International Journal of Multi Discipline Science (IJ-MDS), 7(2). https://journal.stkipsingkawang.ac.id/index.php/IJ-MDS/article/view/6260

Al Naqbi, H., Bahroun, Z., & Ahmed, V. (2024). Enhancing work productivity through generative artificial intelligence: A comprehensive literature review. Sustainability, 16(3), 1166. https://doi.org/10.3390/su16031166

Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889-942. https://doi.org/10.3386/w31161

Cui, K. Z., Demirer, M., Jaffe, S., Musolff, L., Peng, S., & Salz, T. (2026). The effects of generative AI on high-skilled work: Evidence from three field experiments with software developers. Management Science. Advance online publication. https://doi.org/10.1287/mnsc.2025.00535

Koivisto, H., Koivusaari, J., & Saarela, K. (2026). Adoption of AI-assisted code generation in software development: Insights from GitHub Copilot trainings. In Proceedings of the Fifth International Conference on Innovations in Computing Research (ICR'26) (Lecture Notes in Networks and Systems, pp. 180-194). Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-32636-2_16

Le, H. (2026). Impact of generative AI assistants on software engineers' skills, workflow efficiency, and decisions. In AI impacts on deskilling and reskilling software engineers (pp. 1-26). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-8450-4.ch001

Lovett, N. (2026). Individual differences in generative AI effectiveness: An empirical puzzle for HRD research. New Horizons in Adult Education and Human Resource Development. Advance online publication. https://doi.org/10.1177/19394225261472792

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. https://doi.org/10.1126/science.adh2586

Premović, J., Krmpot, V., & Premović, T. (2025). The implementation of artificial intelligence in the educational institutions: evidence from Serbia. SCIENCE International Journal, 4(4), 123-128. https://doi.org/10.35120/sciencej0404123p

Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676-688. https://doi.org/10.1016/j.tics.2016.07.002

Rosendale, J., & Wilkie, L. A. (2021). Scaling workforce development: Using MOOCs to reduce costs and narrow the skills gap. Development and Learning in Organizations: An International Journal, 35(2), 18-21. https://doi.org/10.1108/DLO-11-2019-0258

Subramanya, S., Krishnan, S., & Azad, N. (2026). The complementarity paradox: Human-AI collaboration, overreliance, and skill decline in entrepreneurial contexts. Information Systems Frontiers. https://doi.org/10.1007/s10796-026-10804-5

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Published

2026-09-21

How to Cite

Papic, T. (2026). AI TOOLS IN THE WORKPLACE: PRODUCTIVITY GAINS, DESKILLING RISKS AND THE ROLE OF CONTINUOUS EMPLOYEE EDUCATION. SCIENCE International Journal, 5(3), 369-373. https://doi.org/10.35120/sciencej0503369p

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