AI Predictive and Analytics on Strategic Decision-Making: A Case of Safaricom Kenya Limited
Author
Odhiambo Vinci Vincent; Dr. James Reuben
Abstract
Telecom firms collect vast amounts of data, yet heavy AI investment often fails to improve the strategic decisions managers actually make. This study examined two AI capabilities, predictive analytics and analytical sophistication, and their effect on strategic decision-making at Safaricom Kenya Limited. It pursued two objectives: to examine the effect of AI predictive analytics capability on strategic decision-making, and to assess the effect of AI analytical sophistication on strategic decision-making. The work draws on the Resource-Based View, Dynamic Capabilities Theory, Decision Theory, and the Technology-Organization-Environment framework. Using a descriptive survey design, a sample of 124 senior managers was drawn from a target population of 180 by the Yamane formula and stratified random sampling; 120 valid questionnaires were returned (96.8%). Data were analysed in SPSS using Pearson correlation and simple and multiple linear regression. Both capabilities correlated positively and significantly with strategic decision-making: predictive analytics (r = 0.833) and analytical sophistication (r = 0.546), both p < 0.001. On its own, predictive analytics explained 69.3% of the variation and analytical sophistication 29.8%. In the full four-capability model (R² = 0.906), predictive analytics remained a significant unique driver (β = 0.531, p < 0.001) while analytical sophistication did not (β = 0.118, p = 0.142). The study recommends that Safaricom prioritise predictive analytics in its AI investment and treat analytical sophistication as a supporting capability that feeds, rather than replaces, forward-looking tools.
Keywords
Artificial Intelligence; predictive analytics; analytical sophistication; strategic decision-making; Safaricom
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References
Accenture. (2022). The art of AI maturity: Advancing from practice to performance. Accenture.
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
Brem, A. (2024). Decision-making in the age of artificial intelligence: Rethinking managerial cognition. Technovation, 129, 102876.
McKinsey & Company. (2021). The state of AI in 2021. McKinsey & Company.
McKinsey & Company. (2025). The state of AI: How organizations are rewiring to capture value. McKinsey & Company.
Muriuki, J. (2022). Predictive analytics and decision-making in Kenyan telecommunications. Unpublished MBA project, University of Nairobi.
Otieno, M. (2023). AI analytics and strategic decision quality in telecommunication firms in Kenya. Unpublished MBA project, Management University of Africa.
PwC Africa. (2022). AI and analytics: Unlocking value in African enterprises. PwC.
Safaricom. (2025). Annual report and financial statements 2025. Safaricom PLC.
Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99–118.
Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
Theodorakopoulos, L., Antonopoulou, H., & Halkiopoulos, C. (2025). Augmenting strategic decision-making with artificial intelligence. Future Internet, 17(3), 112.
Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.
Von Neumann, J., & Morgenstern, O. (1944). Theory of games and economic behavior. Princeton University Press.
Wamba, S. F., Dubey, R., Gunasekaran, A., & Akter, S. (2020). The performance effects of big data analytics and AI capability. International Journal of Production Economics, 222, 107498.
Yamane, T. (1967). Statistics: An introductory analysis (2nd ed.). Harper & Row.