Human Capital Analytics and Employee Well-being: The Role of Artificial Intelligence in Improving Organizational Performance
DOI:
https://doi.org/10.62872/51sqs863Keywords:
human capital analytics, artificial intelligence, employee well-being, organizational performance, human resource managementAbstract
The rapid diffusion of artificial intelligence (AI) into human resource management has repositioned human capital analytics (HCA) as a strategic lever for employee well-being and organizational performance. This article synthesizes empirical and conceptual literature published between 2021 and 2026 to examine how AI-enabled HCA practices, covering predictive workforce analytics, AI-driven engagement monitoring, and intelligent decision-support systems, shape employee well-being and, in turn, organizational performance. Using a systematic literature review method following the PRISMA protocol, twenty-five peer-reviewed sources retrieved from Google Scholar-indexed databases were analyzed thematically. Findings indicate that AI-supported HCA improves workforce planning accuracy, reduces administrative workload, and enables personalized well-being interventions, although effectiveness remains contingent on transparent governance, ethical data use, and supportive leadership. The review identifies an underexplored mediating pathway in which psychological safety and perceived organizational support connect AI-driven HCA to well-being and performance outcomes, a gap inadequately addressed in models that treat AI adoption as a direct performance driver. The article proposes an integrative conceptual framework linking human capital analytics, AI capability, employee well-being, and organizational performance, offering theoretical contribution to human resource management scholarship and practical guidance for organizations deploying AI responsibly within people analytics functions.
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References
Agarwal, H. (2025). Artificial intelligence in employee well-being and human resource management. OPJU Business Review, 4(1). https://doi.org/10.63825/opjubr.2025.4.1.07
Anantharajan, R., & S, S. (2024). Studying the role of HR analytics in enhancing employee wellbeing. International Journal of Research Publication and Reviews, 5(12). https://doi.org/10.55248/gengpi.5.1224.0222
Ateeq, K., Oswal, N., Jawabri, A., Masaeid, T. F. A., Alquqa, E., Basha, S. E., & Alami, R. (2025). The transformative impact of artificial intelligence (AI) on organisational behaviour (OB): A study of employee engagement, performance, and ethical implications. Journal of Posthumanism, 5(4). https://doi.org/10.63332/joph.v5i4.1221
Bedad, F., Mokhtari, D., & Sammache, A. (2026). Algorithmic determinism versus human agency: A systematic review and meta-analysis of artificial intelligence and HR analytics in organizational decision-making. Lex localis – Journal of Local Self-Government. https://doi.org/10.52152/pdmv2293
Betgeri, S. N., & Chekuri, N. P. (2025). Leveraging data analytics in human resource management. International Journal of Science and Research Archive, 15(1). https://doi.org/10.30574/ijsra.2025.15.1.1009
Bibi, M., Tan, T. G., & Yao, H. (2025). Exploring the impact of AI capabilities on employee well-being: A mediated moderation analysis. SAGE Open, 15(3). https://doi.org/10.1177/21582440251361981
Chuang, Y., Chiang, H., & Lin, A. (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
Damnjanović, A., Rašković, M., & Skoropad, V. (2025). Artificial intelligence and data analytics in human resource management: Digital transformation and competitive advantage of enterprises. SCIENCE International Journal. https://doi.org/10.35120/sciencej0402033d
Gardezi, S. H. I. (2025). Human capital analytics for organizational performance. International Journal of Multidisciplinary and Applied Studies, 1(4). https://doi.org/10.65477/ijmdas.2025.v1.i4.01
Giermindl, L., Strich, F., Christ, O., Leicht-Deobald, U., & Redzepi, A. (2021). The dark sides of people analytics: Reviewing the perils for organisations and employees. European Journal of Information Systems, 31(3), 410–435. https://doi.org/10.1080/0960085x.2021.1927213
Gong, Q., Fan, D., & Bartram, T. (2025). Integrating artificial intelligence and human resource management: A review and future research agenda. The International Journal of Human Resource Management, 36(1), 103–141. https://doi.org/10.1080/09585192.2024.2440065
Gupta, S. K., M, H., P, M., Farhana, S., Priya, G. K., & S., S. (2025). The mediating effect of AI in recruitment and talent management practices on organizational performance. Book of Abstracts. https://doi.org/10.36690/iceaf-2025-123
Kayusi, F., Chavula, P., Omwenga, M. K., Juma, L., Kayus, B. A., Vallejo, R. G., & Mishra, R. (2025). AI-driven HR analytics: Transforming talent management and employee engagement. Revista Multidisciplinaria Voces de América y el Caribe, 2(1). https://doi.org/10.69821/remuvac.v2i1.214
Kim, B.-J., & Lee, J. (2024). The mental health implications of artificial intelligence adoption: The crucial role of self-efficacy. Humanities and Social Sciences Communications, 11. https://doi.org/10.1057/s41599-024-04018-w
Madhuri, A., & Kumar, B. (2025). HR analytics and decision-making: A data-driven approach to employee performance management. Journal of Neonatal Surgery, 14. https://doi.org/10.52783/jns.v14.2425
Mahade, A., Elmahi, A., Alomari, K. M., & Abdalla, A. A. (2025). Leveraging AI-driven insights to enhance sustainable human resource management performance: Moderated mediation model: Evidence from UAE higher education. Discover Sustainability, 6. https://doi.org/10.1007/s43621-025-01114-y
Matin, A. (2025). Embedding artificial intelligence in next generation human resource development implementations. International Journal of Advanced Engineering, Management and Science, 11(4). https://doi.org/10.22161/ijaems.114.17
Mikalef, P., & Gupta, M. (2021). Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & Management, 58(3), 103434. https://doi.org/10.1016/j.im.2021.103434
Neiroukh, S., Emeagwali, O. L., & Aljuhmani, H. Y. (2025). Artificial intelligence capability and organizational performance: Unraveling the mediating mechanisms of decision-making processes. Management Decision, 63(10), 3501–3532. https://doi.org/10.1108/MD-10-2023-1946
Olaniyan, O. P., Elufioye, O. A., Okonkwo, F. C., Udeh, C. A., Eleogu, T. F., & Olatoye, F. O. (2023). AI-driven talent analytics for strategic HR decision-making in the United States of America: A review. International Journal of Management & Entrepreneurship Research, 4(12). https://doi.org/10.51594/ijmer.v4i12.674
R, P., K, S., A, N., K, S., R, D., & A, I. B. (2026). A behavioral analytics-driven workforce intelligence model for productivity forecasting and employee retention management. In 2026 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1–9). IEEE. https://doi.org/10.1109/icses66558.2026.11479059
Radhika, A., & Pothuri, D. (2026). The role of AI-driven HR analytics in enhancing employee well-being and organizational performance. International Journal of Research and Innovation in Social Science. https://doi.org/10.47772/ijriss.2026.100500414
Rožman, M., Oreški, D., & Tominc, P. (2022). Integrating artificial intelligence into a talent management model to increase the work engagement and performance of enterprises. Frontiers in Psychology, 13. https://doi.org/10.3389/fpsyg.2022.1014434
Rožman, M., Oreški, D., & Tominc, P. (2023). Artificial-intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA environment. Sustainability, 15(6), 5019. https://doi.org/10.3390/su15065019
Rožman, M., Tominc, P., & Milfelner, B. (2023). Maximizing employee engagement through artificial intelligent organizational culture in the context of leadership and training of employees: Testing linear and non-linear relationships. Cogent Business & Management, 10(1). https://doi.org/10.1080/23311975.2023.2248732
Safshekan, M., Feili, A., Shojaeifard, A., & Sorooshian, S. (2026). Artificial intelligence in human resource management: Models for recruitment, training, performance, compensation, and retention. Frontiers in Artificial Intelligence, 9, 1718244. https://doi.org/10.3389/frai.2026.1718244
Sahu, D., Singh, S., & Sharma, A. (2025). From HR analytics to AI-driven HRM: Enhancing workforce productivity and engagement. Journal of Information Systems Engineering and Management, 10(21s). https://doi.org/10.52783/jisem.v10i21s.3395
Sharma, C., Chanana, N., & Chen, H.-Y. (2025). Mapping the evolution: A bibliometric analysis of employee engagement and performance in the age of artificial intelligence-based solutions. Information, 16(7), 555. https://doi.org/10.3390/info16070555
Tyagi, S., & Agarwal, T. (2025). Artificial intelligence and employee wellbeing in human resource management. Pranjana: The Journal of Management Awareness. https://doi.org/10.5958/0974-0945.2025.00008.2
Venugopal, M., Madhavan, V., Prasad, R., & Raman, R. (2024). Transformative AI in human resource management: Enhancing workforce planning with topic modeling. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2432550
Vhora, M. A., Bhandwalkar, V., & Rege, P. M. (2024). AI-driven HR analytics: Enhancing decision-making in workforce planning. The Scientific Temper, 15(4). https://doi.org/10.58414/scientifictemper.2024.15.4.39
Virgillito, D., & Ledda, C. (2026). Personalized AI for workplace health promotion: Performance management and healthcare worker engagement through digital analytics. Frontiers in Public Health, 13. https://doi.org/10.3389/fpubh.2025.1718474
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