Human-centred artificial intelligence and organisational well-being: An integrative framework for responsible technology management

Paper ID: EIJTEM_2026_13_3_17-27

Author's Name: Dr. Ignacio Bonasa Alzuria

Volume: 13

Issue: 3

Year: 2026

Page No: 17-27

Abstract:

Artificial intelligence is increasingly embedded in organisational decisions, workflows and employee interactions, yet technical performance and productivity gains do not necessarily improve the quality of work. This conceptual study develops the Human-Centred Artificial Intelligence for Organisational Well-Being (HCAI-WB) framework and explains the mechanisms through which AI configurations can support or erode sustainable human performance. A structured integrative review synthesises research on human-centred AI, work design, the job demands-resources model, meaningful work, psychological safety, procedural justice and responsible AI governance. The synthesis identifies six interdependent dimensions: purpose and human agency; work design and cognitive load; trust, fairness and data dignity; capabilities and AI literacy; social connection and psychological safety; and learning, measurement and accountability. Eight falsifiable propositions connect these dimensions to engagement, exhaustion, meaningfulness, trust, innovation and long-term performance. The article contributes a six-stage implementation architecture, non-compensatory stage gates, a multidimensional indicator dashboard, a five-level maturity model and an empirical validation roadmap. Its distinctive contribution is to bridge model-centred governance with the lived organisation of work: an AI system is not considered human-centred merely because a person remains nominally in the loop, but because that person retains intelligible authority, adequate time, capability, voice and access to review. The framework offers managers and researchers a testable architecture for aligning responsible technology management with organisational well-being.

Keywords: Artificial intelligence; organisational well-being; human-centred AI; work design; responsible innovation; AI governance; employee experience; sustainable performance.

References:

Amershi, S., Weld, D., Vorvoreanu, M., Fourney, A., Nushi, B., Collisson, P., Suh, J., Iqbal, S., Bennett, P. N., Inkpen, K., Teevan, J., Kikin-Gil, R., & Horvitz, E. (2019). Guidelines for human-AI interaction. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–13. https://doi.org/10.1145/3290605.3300233
Bakker, A. B., & Demerouti, E. (2007). The job demands-resources model: State of the art. Journal of Managerial Psychology, 22(3), 309–328. https://doi.org/10.1108/02683940710733115
Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942. https://doi.org/10.1093/qje/qjae044
Chuang, Y.-T., Chiang, H.-L., & Lin, A.-P. (2025). Insights from the job demands-resources model: AI’s dual impact on employees’ work and life well-being. International Journal of Information Management, 83, 102887. https://doi.org/10.1016/j.ijinfomgt.2025.102887
Deci, E. L., & Ryan, R. M. (2000). The ‘what’ and ‘why’ of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
Edmondson, A. (1999). Psychological safety and learning behavior in work teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999
European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union. https://eur-lex.europa.eu/eli/reg/2024/1689/oj
Green, F., Felstead, A., Gallie, D., & Henseke, G. (2024). Work and life: The relative importance of job quality for general well-being. Socio-Economic Review, 22(2), 835–861. https://doi.org/10.1093/ser/mwad067
International Labour Organization. (2025). Generative AI and jobs: A refined global index of occupational exposure. ILO Working Paper 140. https://www.ilo.org/publications/generative-ai-and-jobs-refined-global-index-occupational-exposure
International Organization for Standardization. (2021). ISO 45003:2021 Occupational health and safety management—Psychological health and safety at work—Guidelines for managing psychosocial risks. ISO.
International Organization for Standardization. (2023). ISO/IEC 42001:2023 Information technology—Artificial intelligence—Management system. ISO.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems, 1–16. https://doi.org/10.1145/3313831.3376727
National Institute of Standards and Technology. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0) (NIST AI 100-1). https://doi.org/10.6028/NIST.AI.100-1
National Institute of Standards and Technology. (2024). Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1). https://doi.org/10.6028/NIST.AI.600-1
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
OECD. (2024). Recommendation of the Council on Artificial Intelligence. OECD Legal Instruments. https://legalinstruments.oecd.org/en/instruments/OECD-LEGAL-0449
Parker, S. K., & Grote, G. (2022). Automation, algorithms, and beyond: Why work design matters more than ever in a digital world. Applied Psychology, 71(4), 1171–1204. https://doi.org/10.1111/apps.12241
Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation-augmentation paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
Rosso, B. D., Dekas, K. H., & Wrzesniewski, A. (2010). On the meaning of work: A theoretical integration and review. Research in Organizational Behavior, 30, 91–127. https://doi.org/10.1016/j.riob.2010.09.001
Savolainen, I., Ylinen, L., Grönroos, R., & Oksanen, A. (2026). AI transformation in working life: A systematic review of usage and attitudes towards AI among workers. Digital Business, 6(1), 100162. https://doi.org/10.1016/j.digbus.2025.100162
Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe and trustworthy. International Journal of Human-Computer Interaction, 36(6), 495–504. https://doi.org/10.1080/10447318.2020.1741118
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039
Sun, L., Hu, R., & Su, H. (2026). Unlocking human potential in the AI age: How employee-AI collaboration transforms work engagement through dual psychological pathways. Frontiers in Psychology, 16, 1705671. https://doi.org/10.3389/fpsyg.2025.1705671
Torraco, R. J. (2005). Writing integrative literature reviews: Guidelines and examples. Human Resource Development Review, 4(3), 356–367. https://doi.org/10.1177/1534484305278283
UNESCO. (2021). Recommendation on the ethics of artificial intelligence. https://www.unesco.org/en/legal-affairs/recommendation-ethics-artificial-intelligence
Valtonen, A., Saunila, M., Ukko, J., Treves, L., & Ritala, P. (2025). AI and employee wellbeing in the workplace: An empirical study. Journal of Business Research, 199, 115584. https://doi.org/10.1016/j.jbusres.2025.115584
World Health Organization. (2022). WHO guidelines on mental health at work. World Health Organization. https://www.who.int/publications/i/item/9789240053052

View PDF