Artificial intelligence and team effectiveness in management: a transformative impact on decision-making, collaboration, and productivity
DOI:
https://doi.org/10.37547/ijmef/Volume05Issue03-16Keywords:
Artificial Intelligence (AI), ethical AI governance, development of AI systemsAbstract
Artificial Intelligence (AI) is transforming modern management by enhancing team effectiveness through advanced decision-making, seamless communication, automation, and performance optimization. AI-powered tools equip managers with predictive analytics, real-time collaboration features, and workflow automation, fostering greater efficiency and data-driven strategies. However, AI integration also presents challenges, including trust concerns, ethical dilemmas, job displacement anxieties, and difficulties in addressing human emotions and creativity. This paper provides a critical analysis of AI’s influence on team effectiveness by reviewing existing literature, outlining its key advantages and challenges, and identifying future research opportunities. The findings underscore the significance of human-AI collaboration, ethical AI governance, and the development of AI systems designed to augment human capabilities rather than replace them.
References
Amabile, T. M. (2018). Creativity in Context: Update to the Social Psychology of Creativity. Routledge. https://doi.org/10.4324/9780429501234.
Bélanger, F., Van Dyke, T., & Pate, J. (2022). AI and decision-making: The impact on managerial judgment. Journal of Management Science, 58(2), 123-139.
Brynjolfsson, E., & McAfee, A. (2017). Machine, Platform, Crowd: Harnessing Our Digital Future. W.W. Norton & Company.
Business Insider. (2025, March 13). Companies large and small are using AI for employee onboarding. It can save HR days of time. Retrieved from https://www.businessinsider.com/generative-ai-employee-onboarding-human-resources-2025-3.
Chen, C., Chen, Z., Luo, W., Xu, Y., Yang, S., Yang, G., Chen, X., Chi, X., Xie, N., & Zeng, Z. (2023). Ethical perspective on AI hazards to humans: A review. Medicine (Baltimore), 102(48), e36163. https://doi.org/10.1097/MD.0000000000036163.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Dignum, V. (2019). Responsible Artificial Intelligence: How to Develop and Use AI in a Responsible Way. Springer. https://doi.org/10.1007/978-3-030-30371-6.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
Ekbia, H. R., & Nardi, B. A. (2017). Heteromation, and Other Stories of Computing and Capitalism. Cambridge, MA: The MIT Press.
García, R., Martella, A., & Quax, P. (2020). “Evacuate everyone south of that line”: Analyzing structural communication patterns during natural disasters. Applied Network Science, 5(1), 1-20. https://link.springer.com/article/10.1007/s42001-020-00092-7.
Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057.
Grover, V. (2023). Using AI to enhance employee communications. SHRM.
Hackman, J. R. (1987). The design of work teams. In J. W. Lorsch (Ed.), Handbook of organizational behavior (pp. 315-342). Englewood Cliffs, NJ: Prentice Hall.
Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14.
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making. Business Horizons, 61(4), 577-586.
Kaplan, A., & Haenlein, M. (2020). Rulers of the world, unite! The challenges and opportunities of artificial intelligence. Business Horizons, 63(1), 37-50.
Lipton, Z. C. (2018). The mythos of model interpretability: In machine learning, the concept of interpretability is both important and slippery. Queue, 16(3), 31-57. https://doi.org/10.1145/3236386.3241340.
Østerlund, C., Crowston, K., Jackson, C., & Mugar, G. (2021). Artificial intelligence in teams: A review, framework, and research agenda. Journal of Management Information Systems, 38(3), 695–735.
Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66-83. https://doi.org/10.1177/0008125619862257.
Tarafdar, M., Beath, C. M., & Ross, J. W. (2019). How AI fits into your team’s work. Harvard Business Review, 97(4), 7-9.
The Guardian. (2024, December 19). More time, less tedium: How AI is helping SMEs to innovate and compete. Retrieved from https://www.theguardian.com/work-redefined/2024/dec/19/more-time-less-tedium-how-ai-is-helping-smes-to-innovate-and-compete.
Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students' cognitive abilities: A systematic review. Smart Learning Environments, 11(28).
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Fayzullayeva Marifat Abduvaxob qizi
This work is licensed under a Creative Commons Attribution 4.0 International License.
