This study responds to the revolutionary impact of artificial intelligence (AI) on corporate management strategic decision-making. It is informed by the grand research question: How does AI reshape the landscape of strategic decision-making in organizational environments and with what consequences for managerial performance, moral responsibility, and competitive edge? Utilizing a qualitative research methodology that combines intensive literature reviews with exemplar corporate examples of companies such as Amazon, Netflix, and IBM, the study highlights how AI technologies—diverse ranging from machine learning and predictive analytics to natural language processing—are seeking entry points within corporate functions for the purpose of enhancing efficiency, responsiveness, and strategic acuity. The study shows that AI significantly improves the speed, accuracy, and agility of managerial decision-making, enabling more data-driven decision-making processes and competitive strategy. However, there are also limitations in the form of algorithmic bias, opacity of decision-making, overdependence on automation, and disruption to traditional workforce dynamics. These limitations speak to the imperative of injecting strong human governance, moral checks and balances, and organizational change management paradigms into AI implementation. To meet these complexities, the paper suggests a hybrid decision-making model that balances the computational capacities of AI with human judgment, imagination, and ethical acumen. It also suggests investments in AI literacy, ethical infrastructures, strategic alignment, and cross-sector collaboration to ensure that organizations use AI responsibly while maintaining long-term competitiveness and organizational values.
Babar, Z. (2024). A study of business process automation with DevOps: A data-driven approach to agile technical support. Journal of Emerging Business and Technology, 4(4), 1–32. https://doi.org/10.63125/3w5cjn27
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Bory, A. (2023). The future of work with artificial intelligence and automation. Theseus.fi. http://www.theseus.fi/handle/10024/812110
Bostrom, N., & Yudkowsky, E. (2014). The ethics of artificial intelligence. In K. Frankish & W. Ramsey (Eds.), The Cambridge handbook of artificial intelligence (pp. 316–334). Cambridge University Press.
Bryman, A., & Bell, E. (2015). Business research methods (4th ed.). Oxford University Press.
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American Economic Review, 106(5), 133–139.https://doi.org/10.1257/aer.p20161016
Bughin, J., Hazan, E., Lund, S., Dahlström, P., Wiesinger, A., & Subramaniam, A. (2018). Skill shift: Automation and the future of the workforce. McKinsey Global Institute.
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12. https://doi.org/10.1177/2053951715622512
Chatterjee, S., Rana, N. P., Tamilmani, K., & Sharma, A. (2023). Understanding AI adoption in small and medium enterprises: A systematic literature review. Information Systems Frontiers, 25(2), 341–362. https://doi.org/10.1007/s10796-021-10159-8
Chui, M., Manyika, J., & Miremadi, M. (2018). Notes from the AI frontier: Insights from hundreds of use cases. McKinsey Global Institute.
Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). SAGE Publications.
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE Publications.
Davenport, T. H., & Kirby, J. (2016). Only humans need apply: Winners and losers in the age of smart machines. Harper Business.
Davenport, T. H., & Mittal, N. (2020). The AI advantage: How to put the artificial intelligence revolution to work. MIT Press.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Denzin, N. K. (2012). Triangulation 2.0. Journal of Mixed Methods Research, 6(2), 80–88. https://doi.org/10.1177/1558689812437186
Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y.,
Dwivedi, R., Edwards, J. S., & Eirug, A. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.101994
Eisenhardt, K. M. (1989). Building theories from case study research. Academy of Management Review, 14(4), 532–550. https://doi.org/10.5465/amr.1989.4308385
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
Gunning, D. (2017). Explainable artificial intelligence (XAI). Defense Advanced Research Projects Agency (DARPA) Report.
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
Khan, I. (2025). The role of artificial intelligence in transforming Multan’s green businesses towards a sustainable future. Southern Journal of Business, 1(1), 60–80. https://sjb.usp.edu.pk/index.php/123/article/view/3
Lee, J., Davari, H., Singh, J., & Pandhare, V. (2018). Industrial AI and digital transformation for industry 4.0 and beyond. Manufacturing Letters, 18, 20–23. https://doi.org/10.1016/j.mfglet.2018.09.002
Markus, M. L., & Robey, D. (1988). Information technology and organizational change: Causal structure in theory and research. Management Science, 34(5), 583–598. https://doi.org/10.1287/mnsc.34.5.583
McKinsey & Company. (2021). The state of AI in 2021. McKinsey Global Institute.
Oberoi, P. (2020). Artificial intelligence and the future of businesses. In W. Leal Filho (Ed.),
Encyclopedia of the UN Sustainable Development Goals (pp. 1–9). Springer. https://doi.org/10.1007/978-3-319-71059-4_145-1
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
Rajagopal, N. K., Qureshi, N. I., Durga, S., Ramirez Asis, E. H., Huerta Soto, R. M., Gupta, S. K., & Deepak, S. (2022). Future of business culture: An artificial intelligence-driven digital framework for organization decision-making process. Complexity, 2022(2), 1–14. https://doi.org/10.1155/2022/7796507
Russell, S., & Norvig, P. (2021). Artificial intelligence: A modern approach (4th ed.). Pearson Education.
Saunders, M., Lewis, P., & Thornhill, A. (2019). Research methods for business students (8th ed.). Pearson Education.
Schwab, K. (2017). The fourth industrial revolution. Crown Business.
Shrestha, Y. R., Ben-Menahem, S. M., & Krogh, G. V. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257
Simon, H. A. (1972). Theories of bounded rationality. In C. B. McGuire & R. Radner (Eds.), Decision and organization (pp. 161–176). North-Holland.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007
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
Vargo, S. L., & Lusch, R. F. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1–17. https://doi.org/10.1509/jmkg.68.1.1.24036
Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: Writing a literature review. MIS Quarterly, 26(2), xiii–xxiii.
Westerman, G., Bonnet, D., & McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Review Press.
Wilson, H. J., & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114–123.
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE Publications.
Njuru, J., Githui, F., & Oginga, B. (2025). The Role of Artificial Intelligence in Enhancing Strategic Decision-Making in Corporate Management. International Journal of Academic Research in Business and Social Sciences, 15(10), 1038–1050.
Copyright: © 2025 The Author(s)
Published by Knowledge Words Publications (www.kwpublications.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode
