The Impact of Artificial Intelligence on Leadership Changes : An Analysis of E-Leadership and Large Language Models
DOI:
https://doi.org/10.14513/tge-jres.00642Keywords:
E-leadership, Generative AI, Human–AI collaboration, Large Language Models, Managerial Decision-makingAbstract
Purpose – The purpose of this study is to identify and analyze the effects of GenAI and large language models on leadership and managerial decision-making as well as human–AI collaboration in the workplace. The study examines the effects of GenAI on leadership, managerial judgment, and the nature of work and the employee experience.
Design/methodology/approach – This is a review article and is not meant to be a systematic or comprehensive review. Instead, the objective is to provide a structured and transparent review of recent articles in Web of Science and Scopus. The review targets articles that address GenAI and LLMs in relation to e-leadership, managerial decision-making, and human–AI collaboration, published in English from 2023 to 2026. From the screening and duplicate removal performed by Rayyan, 17 articles formed the basis of the thematic review.
Findings – The review finds that GenAI does not substitute leadership; it transforms it. Managers increasingly act as coordinators, interpreters, and validators of AI-based outputs. GenAI provides enhanced decision support by expanding information, options, and analytical capacity. However, GenAI also introduces risks, such as overreliance, hallucination, and reduced accountability. Human–AI collaboration poses risks to team autonomy and the meaning of work. At the same time, it can support knowledge sharing, employee capability, and task performance when it is used responsibly.
Originality - This paper is unique because it connects Generative AI to socio-technical systems and e-leadership. It portrays AI-enabled leadership through the lens of structuring, interpreting, and governing, as a human–AI system.
References
Al-Bashrawi, M. A., Al-Sharafi, M. A., Elgendy, I. A., Helal, M. Y. I., Anbalagan, M. K., Chae, I., & Dwivedi, Y. K. (2026). Agentic AI systems and the future of entrepreneurship: A perspective on co-agency, innovation, and ecosystem transformation. International Entrepreneurship and Management Journal, 22, Article 27. https://doi.org/10.1007/s11365-026-01164-2
Avolio, B. J., Kahai, S., & Dodge, G. E. (2000). E-leadership: Implications for theory, research, and practice. The Leadership Quarterly, 11(4), 615–668. https://doi.org/10.1016/S1048-9843(00)00062-X
Bagchi, S. N., & Sharma, R. (2026). Managerial decision-making and AI: A decision canvas approach. Business Horizons, 69, 55–66. https://doi.org/10.1016/j.bushor.2024.12.001
Berde, É., Szabó-Bakos, E., Németh, P., Remsei, S., & Kuncz, I. (2025). GDP per Capita and Human Capital Investment in Five Countries after Exhaustion of the First Demographic Dividend. Regional Statistics, 15(5), 908–929. http://doi.org/10.15196/RS150504
Callari, T. C., & Puppione, L. (2025). Meaningful work as shaped by employee work practices in human-AI collaborative environments: A qualitative exploration through ideal types. European Journal of Innovation Management, 28(10), 5001–5027. https://doi.org/10.1108/EJIM-11-2024-1339
Czucka-Varga, V. (2025). The impact of leader’s innovational capability on business performance. Tér–Gazdaság–Ember, Journal of Region, Society and Economy. https://doi.org/10.14513/tge-jres.00445
Danó, G., Kovács, S., & Surman, V. (2025). Challenges and opportunities of AI in market research: Virtual interviewers. Tér–Gazdaság–Ember, Journal of Region, Society and Economy. https://doi.org/10.14513/tge-jres.00413
Dutta, D., & Naveen, P. M. (2026). Transforming recruitment and selection practices in organizations through discriminative and generative AI adoption: A structuration lens. Human Resource Management, 65, 77–115. https://doi.org/10.1002/hrm.70018
Hai, S., Long, T., Honora, A., Japutra, A., & Guo, T. (2025). The dark side of employee-generative AI collaboration in the workplace: An investigation on work alienation and employee expediency. International Journal of Information Management, 83, Article 102905. https://doi.org/10.1016/j.ijinfomgt.2025.102905
Hou, J., Wang, L., Wang, G., Wang, H. J., & Yang, S. (2025). The double-edged roles of generative AI in the creative process: Experiments on design work. Information Systems Research. Advance online publication. https://doi.org/10.1287/isre.2024.0937
Hu, J., & Li, Y. (2026). Digital servitization and sustainable growth in the era of generative AI: The role of leadership, knowledge sharing, and employee attitudes. Journal of Innovation & Knowledge, 15, Article 100977. https://doi.org/10.1016/j.jik.2026.100977
Park, M. J. (2026). AI as a cognitive collaborator: Assimilation and accommodation in human–machine teaming for innovation. Journal of Innovation & Knowledge, 12, Article 100892. https://doi.org/10.1016/j.jik.2025.100892
Przegalinska, A., Triantoro, T., Kovbasiuk, A., Ciechanowski, L., Freeman, R. B., & Sowa, K. (2025). Collaborative AI in the workplace: Enhancing organizational performance through resource-based and task-technology fit perspectives. International Journal of Information Management, 81, Article 102853. https://doi.org/10.1016/j.ijinfomgt.2024.102853
Rady, J., Townsend, D., & Hunt, R. (2026). From algorithmic hallucinations to alien minds: Addressing the ideator’s dilemma through entrepreneurial work. Journal of Business Venturing, 41, Article 106550. https://doi.org/10.1016/j.jbusvent.2025.106550
Reinhard, P., Li, M. M., Peters, C., Janson, A., & Leimeister, J. M. (2026). GenAI-infused service delivery: Micro-level augmentation patterns at the service frontline. Journal of Service Research. Advance online publication. https://doi.org/10.1177/10946705251414283
Shonubi, O. A. (2026). Unwrapping generative AI paradigms for product and service innovation differentiation. Journal of Strategy & Innovation, 37, Article 200562. https://doi.org/10.1016/j.jsinno.2025.200562
Steinhauser, S., & Heid, P. (2026). Organizational readiness, AI literacy, and the new frontier of R&D: How generative AI shapes innovation capacity. R&D Management. Advance online publication. https://doi.org/10.1111/radm.70038
Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38. https://doi.org/10.1177/001872675100400101
Urbani, R., Ferreira, C., & Lam, J. (2024). Managerial framework for evaluating AI chatbot integration: Bridging organizational readiness and technological challenges. Business Horizons, 67, 595–606. https://doi.org/10.1016/j.bushor.2024.05.004
Yang, J., Dong, C., Chu, S.-C., & Rheu, M. (2026). Transforming advertising in the age of generative AI: Exploring advertising professionals’ perceptions of human-AI value co-creation. International Journal of Advertising, 45(1), 139–170. https://doi.org/10.1080/02650487.2025.2604910
Yin, J., & Wang, Y. (2026). The paradoxical effects of generative artificial intelligence induced stressors on employee adaptive performance: Evidence from China. Asia Pacific Journal of Human Resources, 64, Article e70074. https://doi.org/10.1111/1744-7941.70074
Yokoi, T., Laureiro-Martinez, D., Magni, F., & Brusoni, S. (2026). Organizing across cognitive asymmetry in human–AI collaboration: A study of perfume creation. Strategic Management Journal. Advance online publication. https://doi.org/10.1002/smj.70089
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Yasin Sahnoun, István Réthy (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in the Tér- Gazdaság - Ember / Journal of Region, Economy and Society are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).
This permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided that the original work is properly cited.
How to Cite
