The legal governance of ai upskilling and reskilling: towards a new employer duty
DOI:
https://doi.org/10.5281/zenodo.21186038Keywords:
Artificial intelligence, Upskilling, Reskilling, Labour law, Human resource managementAbstract
This article examines whether the rapid adoption of artificial intelligence (AI) justifies a broader interpretation of employer responsibilities regarding continuous upskilling and reskilling. An interdisciplinary qualitative methodology was employed, combining doctrinal-comparative legal analysis with a systematic literature review conducted according to the PRISMA 2020 guidelines. Scientific evidence was collected from Scopus, Web of Science, ScienceDirect and SpringerLink, complemented by reports from the International Labour Organization (ILO), the Organisation for Economic Co-operation and Development (OECD) and the World Economic Forum (WEF). The findings indicate that AI-driven workforce transformation extends beyond technological innovation and significantly affects employability, requiring organisations to anticipate skills disruption through strategic workforce development. The study identifies a conceptual gap between labour law, strategic human resource management and corporate governance concerning employer responsibility for workforce capability development. To address this gap, it proposes the AI Employability Governance Framework (AEGF), an original model integrating strategic workforce foresight, continuous upskilling, reskilling pathways, shared employer responsibility, labour rights protection and sustainable employability. The framework contributes to the emerging debate on responsible AI governance by providing a conceptual foundation for more inclusive, resilient and legally informed models of workforce transformation.
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References
Badie, F., & Rostomyan, A. (2025). Competency Mapping as a Knowledge Driver in Modern Organisations. Knowledge, 5(3), 13. https://doi.org/10.3390/knowledge5030013
Barbosa I, & Real de Oliveira E (2026), "Digital reskilling and workforce transformation: exploring identity, capability and adaptation in AI-mediated workplaces". Journal of Organizational Effectiveness: People and Performance, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JOEPP-12-2025-1109
Chhibber, S., Rajkumar, S., & Dassanayake, S. (2025). Will Artificial Intelligence Reshape the Global Workforce by 2030? A Cross-Sectoral Analysis of Job Displacement and Transformation. Blockchain, Artificial Intelligence, and Future Research, 1(1), 35–51. https://doi.org/10.70211/bafr.v1i1.178
Doellgast, V., Appalla, S., Ginzburg, D., Kim, J., & Thian, W. L. (2025). Global case studies of social dialogue on AI and algorithmic management (No. 144). ILO Working Paper. https://www.ilo.org/publications/global-case-studies-social-dialogue-ai-and-algorithmic-management
International Labour Organization. (2025). Workforce 2030: Skills for thriving in the green and digital transition. ILO. https://doi.org/10.54394/XXZW7148
Kulkov, I., Kulkova, J., Rohrbeck, R., Menvielle, L., Kaartemo, V., & Makkonen, H. (2024). Artificial intelligence-driven sustainable development: Examining organizational, technical, and processing approaches to achieving global goals. Sustainable Development, 32(3), 2253–2267. https://doi.org/10.1002/sd.2773
Minbaeva, D. B. (2023). Strategic human resource management in the context of the COVID-19 pandemic: Learning from crisis to advance SHRM theory. Human Resource Management. https://doi.org/10.1002/hrm.22162
Nawaz, N., Arunachalam, H., Pathi, B. K., & Gajenderan, V. (2024). The adoption of artificial intelligence in human resources management practices. International Journal of Information Management Data Insights, 4(1), 100208. https://doi.org/10.1016/j.jjimei.2023.100208
Organisation for Economic Co-operation and Development. (2023). OECD Employment Outlook 2023: Artificial Intelligence and the Labour Market. OECD Publishing. https://doi.org/10.1787/08785bba-en
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., ... Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Pasi, B. N., Dhamak, P. S., Todkari, V. C., & Gholap, V. D. (2026). AI-augmented remote work and career sustainability: An integrated analysis of adoption determinants, productivity dynamics and predictive outcomes. Journal of Organizational Effectiveness: People and Performance. Advance online publication. https://doi.org/10.1108/JOEPP-12-2025-1086
Porcheddu, D., & Prosdocimi, S. (2025). The impact of AI on the European financial sector and the role of social dialogue. ETUI Research Paper-Working Paper. http://dx.doi.org/10.2139/ssrn.5821003
Pronello, C., & Fedeli, E. (2025). Just technology transition. A policy agenda to minimise the social impacts of digitalisation and automation on the transport workforce. Transportation Research Interdisciplinary Perspectives, 34, 101670. https://doi.org/10.1016/j.trip.2025.101670
Taheri Hosseinkhani, N. (2025). Economic and social implications of AI-driven automation and workforce transformation. SSRN. https://doi.org/10.2139/ssrn.5403524
World Economic Forum. (2025). The Future of Jobs Report 2025. World Economic Forum. https://doi.org/10.58275/9782940631851
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Data Availability Statement
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
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Copyright (c) 2026 Joel Menezes-Barreto, Duliet Hong-León, Jaime E. Pérez-Fernández, António Ramos-Congo, Natalia A. Domingas-Chivango (Author)

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