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Vol. 5 No. 2 (2026)
"Labor Economics and Human Capital" Scientific electronic journal

Human Capital

LEADERSHIP IN THE ALGORITHMIC ERA: NEW SKILLS MANAGERS NEED FOR AI GOVERNANCE

DOI 10.55439/lehc/vol5iss2/299 Google Scholar Google Scholar Pages 55-69

Published 2026-06-29

Authors
Sirojiddinova Shahrizoda Davronovna
Affiliation
Student, "Silk Road" International University of Tourism and Cultural Heritage
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Abstract

The rapid incorporation of artificial intelligence (AI) into business processes has changed managerial functions drastically across nearly all sectors. This theoretical study seeks to determine what competencies leaders need to have in order to successfully govern AI within their organizations today. Utilising a systematic narrative review of 34 different types of research documents – such as peer-reviewed journal articles, practitioner reports, government policy documents and foundational monographs published from 2015-2024 – we present the Algorithmic Leadership Competency (ALC) Model as our contribution. The ALC Model consists of three domains: (1) Algorithmic Literacy, (2) Ethical Accountability, and (3) Human-Centered Facilitation. These three competency areas are interrelated and mutually supportive. The ALC Model is theoretically grounded in the organizational behaviour, data science ethics, and AI governance literature and mapped to existing empirical studies as well as significant regulatory frameworks, including the newly developed European Union AI Act (2024). We also consider the evidence supporting each competency area, compare the ALC Model to previous frameworks and outline implications for leadership development programs, HR policies and organisation-wide AI governance structures. This study recognises certain limitations including the lack of primary empirical data and presents a structured agenda for future quantitative and qualitative research to validate the ALC model.

References

  1. Burns, J. M. (1978). Leadership. Harper & Row
  2. Cath, C. (2018). Governing artificial intelligence: Ethical, legal and technical opportunities and challenges. Philosophical Transactions of the Royal Society A, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080
  3. Dafoe, A. (2018). AI governance: A research agenda. Future of Humanity Institute, University of Oxford
  4. European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union
  5. Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91–108. https://doi.org/10.1111/j.1471- 1842.2009.00848.x
  6. Greenleaf, R. K. (1977). Servant leadership: A journey into the nature of legitimate power and greatness. Paulist Press
  7. Harreis, H., Koullias, T., Roberts, R., & Te, K. (2023, June). Rewired: The McKinsey guide to outcompeting in the age of digital and AI. McKinsey & Company
  8. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2
  9. Kolbjørnsrud, V., Amico, R., & Thomas, R. J. (2016). How artificial intelligence will redefine management. Harvard Business Review. https://hbr.org/2016/11/how-artificial-intelligence-will-redefine- management
  10. McKinsey & Company. (2023). The state of AI in 2023: Generative AI’s breakout year. McKinsey Global Institute
  11. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishers
  12. Orlikowski, W. J. (2000). Using technology and constituting structures: A practice lens for studying technology in organizations. Organization Science, 11(4), 404–428. https://doi.org/10.1287/orsc.11.4.404.14600
  13. Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press
  14. Ransbotham, S., Candelon, F., Kiron, D., LaFountain, B., & Khodabandeh, S. (2020). The cultural benefits of artificial intelligence in the enterprise. MIT Sloan Management Review and Boston Consulting Group
  15. Thomas, J., & Harden, A. (2008). Methods for the thematic synthesis of qualitative research in systematic reviews. BMC Medical Research Methodology, 8(1), 45. https://doi.org/10.1186/1471-2288-8-45
  16. Weick, K. E. (1995). Sensemaking in organizations. SAGE Publications

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