Leadership Sense-Making of Workforce Value in AI-Enabled Talent Management: A Qualitative Inquiry
DOI:
https://doi.org/10.33152/jmphss-10.4.2Keywords:
Human capital, Artificial intelligence, Leadership, Sense-making, Talent management, Thematic analysisAbstract
Artificial intelligence is transforming how organizations assess, select, and develop talent, shifting workforce evaluation from static, retrospective criteria toward dynamic, predictive, algorithm-based models. Despite rapid technological growth, little is known about how organizational leaders interpret and use algorithmic outputs when assessing workforce value. Prior research has emphasized technology performance and adoption outcomes while overlooking the conceptual, normative, and social dimensions of decision-making at the human–algorithm interface—an omission that matters because algorithmic outputs become organizational action through meaning-making, not data processing alone. This qualitative study examined how senior leaders make sense of workforce value within AI-enabled talent management systems, focusing on the cognitive, ethical, and social dimensions of that process. Twenty-two senior leaders across four sectors (technology, finance, healthcare, and manufacturing) participated in semi-structured interviews, analyzed using a six-phase thematic analysis framework. Five interrelated themes emerged: a shift toward capability and adaptability as measures of value, tension between human judgment and algorithmic authority, ethical concerns regarding fairness, transparency, and trust, emotional and social complexities in leader decision-making, and evolving skill requirements for leading in AI-supported environments. Drawing on Sense making Theory, the findings introduce ethical mediation as a distinct, practice-based leadership construct in AI-enabled human resource management and offer recommendations for developing leader capabilities in responsible AI governance and human-centered HRM system design.
References
Balasubramanian, N., Ye, Y., & Xu, M. (2022). Substituting human decision-making with machine learning: Implications for organizational learning. Academy of Management Review, 47(3), 448–465. https://doi.org/10.5465/amr.2019.0470
Becker, G. S. (1993). Human capital: A theoretical and empirical analysis, with special reference to education. University of Chicago Press. https://doi.org/10.7208/chicago/9780226041223.001.0001
Benabou, A., & Touhami, F. (2025). Empowering human resource management through artificial intelligence: A systematic literature review and bibliometric analysis. International Journal of Production Management and Engineering, 13(1), 59–76. https://doi.org/10.4995/ijpme.2025.21900
Boudreau, J., & Cascio, W. (2017). Human capital analytics: Why are we not there? Journal of Organizational Effectiveness: People and Performance, 4(2), 119–126. https://doi.org/10.1108/joepp-03-2017-0021
Braun, V. (2016). Collecting qualitative data: A practical guide to textual, media and virtual techniques. Cambridge University Press. https://books.google.com.pk/books?id=rS42DwAAQBAJ
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Braun, V., & Clarke, V. (2022). Thematic analysis: A practical guide. QMiP Bulletin, 1(33), 46–50. https://doi.org/10.53841/bpsqmip. 2022.1.33.46
Brey, B., & van der Marel, E. (2024). The role of human-capital in artificial intelligence adoption. Economics Letters, 244, 111949. https://doi.org/10.1016/j.econlet.2024.111949
Brown, M. E., Treviño, L. K., & Harrison, D. A. (2005). Ethical leadership: A social learning perspective for construct development and testing. Organizational Behavior and Human Decision Processes, 97(2), 117–134. https://doi.org/10.1016/j.obhdp.2005.03.002
Burton, J. W., Stein, M.-K., & Jensen, T. B. (2019). A systematic review of algorithm aversion in augmented decision making. Journal of Behavioral Decision Making, 33(2), 220–239. https://doi.org/10.1002/bdm.2155
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://doi.org/10.1177/001316446002000104
Cooke, F. L., Lu, Y., Yang, M., & Zhao, C. (2026). Spotlighting platform-based gig work research: Toward a common good approach and implications for research in china. Asia Pacific Journal of Human Resources, 64(3), e70084.
Deloitte. (2024). Global human capital trends 2024. https://shorturl.fm/cvacD
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M.,
Albanna, H., Albashrawi, M. A., Al-Busaidi, A. S., Balakrishnan, J., Barlette, Y., Basu, S., Bose, I., Brooks, L., Buhalis, D., …Wright, R. (2023). So what if ChatGPT wrote it? Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
Forum, W. E. (2025). The future of jobs report 2025. https://rb.gy/axlmrg
Guest, D. E. (2017). Human resource management and employee well-being: Towards a new analytic framework. Human Resource Management Journal, 27(1), 22–38. https://doi.org/10.1111/1748-8583.12139
Huang, M.-H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
Jam, F. A., Ali, I., Albishri, N., Mammadov, A., & Mohapatra, A. K. (2025). How does the adoption of digital technologies in supply chain management enhance supply chain performance? a mediated and moderated model. Technological Forecasting and Social Change, 219, 124225. https://doi.org/10.1016/j.techfore.2025.124225
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
Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data and Society, 8(2). https://doi.org/10.1177/20539517211020332
Kallio, H., Pietilä, A.-M., Johnson, M., & Kangasniemi, M. (2016). Systematic methodological review: Developing a framework for a qualitative semi-structured interview guide. Journal of Advanced Nursing, 72(12), 2954–2965. https://doi.org/10.1111/jan.13031
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
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159. https://doi.org/10.2307/2529310
Lilly, C. C. (1998). Qualitative inquiry and research design: Choosing among five traditions. Networks: An Online Journal for Teacher Research, 1(1), 62–62. https://doi.org/10.4148/2470-6353.1252
Maak, T., & Pless, N. M. (2006). Responsible leadership in a stakeholder society: A relational perspective. Journal of Business Ethics, 66(1), 99–115. https://doi.org/10.1007/s10551-006-9047-z
Maitlis, S. (2005). The social processes of organizational sensemaking. Academy of Management Journal, 48(1), 21–49. https://doi.org/10.5465/amj.2005.15993111
Maitlis, S., & Christianson, M. (2014). Sensemaking in organizations: Taking stock and moving forward. Academy of Management Annals, 8(1), 57–125. https://doi.org/10.5465/19416520.2014.873177
Meijerink, J. G., Bondarouk, T., & Lepak, D. P. (2015). Employees as active consumers of HRM: Linking employees’ HRM competences with their perceptions of HRM service value. Human Resource Management, 55(2), 219–240. https://doi.org/10.1002/hrm.21719
Minbaeva, D. (2020). Disrupted HR? Human Resource Management Review, 31, 100820. https://doi.org/10.1016/j.hrmr.2020.100820
Minbaeva, D., & Navrbjerg, S. (2023). Strategic human resource management in the context of environmental crises: A COVID-19 test. Human Resource Management, 62, 811–832. https://doi.org/10.1002/hrm.22162
Murray, A., Rhymer, J., & Sirmon, D. G. (2021). Humans and technology: Forms of conjoined agency in organizations. Academy of Management Review, 46(3), 552–571. https://doi.org/10.5465/amr.2019.0186
Naoum, R.F., Szakadáti, T., & Balogh, G. (2026). Artificial Intelligence (AI) in human resource management (HRM):A systematic review of its dual impact on diversity, equity, and inclusion (DEI). Management Review Quarterly. https://doi.org/10.1007/s11301025-00580-y
Newman, S. A., Ford, R. C., & Marshall, G. W. (2019). Virtual team leader communication: Employee perception and organizational reality. International Journal of Business Communication, 57(4), 452–473. https://doi.org/10.1177/2329488419829895
Patton, M. Q. (2014). Qualitative research and evaluation methods: Integrating theory and practice. Sage publications.
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the 2020 conference on fairness, accountability, and transparency, 469–481. https://doi.org/10.1145/3351095.3372828
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
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 1(5), 206–215. https://doi.org/10.1038/s42256-019-0048-x
Sandberg, J., & Tsoukas, H. (2020). Sensemaking reconsidered: Towards a broader understanding through phenomenology. Organi zation Theory, 1(1). https://doi.org/10.1177/2631787719879937
Scarbrough, H., Chen, Y., & Patriotta, G. (2024). The AI of the beholder: Intra-professional sensemaking of an epistemic technology. Journal of Management Studies, 62(5), 1885–1913. https://doi.org/10.1111/joms.13065
Sharma, P., Bhattacharya, S., & Bhattacharya, S. (2025). HR analytics and AI adoption in IT sector: Reflections from practitioners. Journal of Work-Applied Management. https://doi.org/10.1108/jwam-12-2024-0179
Shumilova, I., Chaliuk, Y., Tonne, O., Akimov, O., & Kobylina, Y. (2026). Development of public administration in preventive health care: Innovative vectors and world experience. Journal of Management Practices, Humanities and Social Sciences, 10(2). https://doi.org/10.33152/jmphss-10.2.1
Weber, E., Büttgen, M., & Bartsch, S. (2022). How to take employees on the digital transformation journey: An experimental study on complementary leadership behaviors in managing organizational change. Journal of Business Research, 143, 225–238. https://doi.org/10.1016/j.jbusres.2022.01.036
Weick, K. (1995). Sensemaking in organizations (Vol. 3). Sage publications Thousand Oaks, CA. https://doi.org/10.3406/sotra.1996.2274
Wright, P. M., & McMahan, G. C. (2011). Exploring human capital: Putting ‘human’ back into strategic human resource management: Exploring human capital. Human Resource Management Journal, 21(2), 93–104. https://doi.org/10.1111/j.17488583.2010.00165.x
Published
Issue
Section
License
Copyright (c) 2026 Rudsada Kaewsaeng-on, Dr Asma Zeeshan, Kashif Hussain
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
