Artificial Intelligence and Leadership Effectiveness: Empirical Evidence from Multinational Corporations in Uganda
Downloads
As Artificial Intelligence (AI) technologies continue to reshape global business landscapes, their implications for leadership effectiveness remain underexplored in developing economies. This study investigates the impact of AI adoption on leadership effectiveness within multinational corporations (MNCs) operating in Uganda. Using a concurrent mixed-methods design, data were collected from 160 leaders and senior managers through structured surveys and 15 executive interviews across 20 MNCs. The findings reveal that AI is widely used for strategic planning (72.9%), operational efficiency (70.8%), and customer insights (68.8%), with 75% of respondents leveraging AI tools for decision-making. AI adoption was found to significantly enhance leadership effectiveness, explaining 65% of the variance in leadership outcomes. Key improvements were noted in decision accuracy, team coordination, and strategic foresight. However, challenges such as poor data quality, limited technical skills, cybersecurity concerns, and cultural resistance hinder full integration. Leaders emphasized the need to balance AI-driven insights with empathy, ethical reasoning, and cultural sensitivity—particularly within Uganda’s diverse workforce. The study concludes that while AI holds transformative potential for enhancing leadership, successful implementation requires context-specific strategies that integrate technology with human-centered leadership. Recommendations include investing in digital upskilling, strengthening data infrastructure, and developing localized AI governance frameworks to ensure equitable and ethical adoption. This research contributes novel empirical evidence from an African perspective and offers practical insights for responsible AI integration in emerging market leadership contexts.
Ameen, A., Tarhini, A., Reppel, A., & Anand, A. (2021). The role of artificial intelligence capability and organizational culture in organizational innovation: Evidence from the UAE. Journal of Innovation & Knowledge, 6(2), 112–124.
https://doi.org/10.1016/j.jik.2021.01.001
Asongu, S. A., & Odhiambo, N. M. (2021). Enhancing ICT for inclusive human development in Sub-Saharan Africa. Technology in Society, 64,
https://doi.org/10.1016/j.techsoc.2020.101509
Babic, B., Gerke, S., Evgeniou, T., & Cohen, I. G. (2021). Beware explanations from AI in health care. Science, 373(6552), 284–286.
https://doi.org/10.1126/science.abg1834
Belhadi, A., Kamble, S., Zkik, K., Cherrafi, A., & Touriki, F. E. (2021). The integrated effect of Big Data Analytics, Lean Six Sigma and Green Manufacturing on the firms’ performance: Evidence from the automotive sector. Journal of Cleaner Production, 278, 123744.
https://doi.org/10.1016/j.jclepro.2020.123744
Bonga, W. G., & Nyoni, T. (2023). Artificial intelligence and employment in Africa: New threats or new opportunities? African Journal of Economic Review, 11(1), 1–21.
Bresciani, S., Ferraris, A., Del Giudice, M., & Santoro, G. (2021). The management of organizational ambidexterity through alliances in a new context of analysis: Internet of Things (IoT) smart city projects. Technological Forecasting and Social Change, 169, 120843. https://doi.org/10.1016/j.techfore.2021.120843
Chui, M., George, K., & Mirembe, R. (2023). Responsible AI for Africa: Charting the path forward. McKinsey Digital Insights.
https://www.mckinsey.com/capabilities/mckinsey-digital
Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (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.2019.08.002
Dwivedi, Y. K., Hughes, D. L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... & Williams, M. D. (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.2019.08.002
Haenlein, M., & Kaplan, A. (2021). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14.
https://doi.org/10.1177/0008125619864925
Hofstede, G. (2021). Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations Across Nations (3rd ed.). SAGE Publications.
Kar, A. K., & Dwivedi, Y. K. (2020). Theory building with big data-driven research – Moving away from the “what” towards the “why.” International Journal of Information Management, 54, 102205.
https://doi.org/10.1016/j.ijinfomgt.2020.102205
Rai, A. (2020). Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), 137–141.
https://doi.org/10.1007/s11747-019-00710-5
Ransbotham, S., Kiron, D., LaFountain, B., & Khodabandeh, S. (2020). Expanding AI’s Impact with Organizational Learning. MIT Sloan Management Review.
https://sloanreview.mit.edu/projects/expanding-ais-impact-with-organizational-learning/
Ransbotham, S., Kiron, D., LaFountain, B., & Khodabandeh, S. (2020). Expanding AI’s Impact with Organizational Learning. MIT Sloan Management Review.
https://sloanreview.mit.edu/projects/expanding-ais-impact-with-organizational-learning/
Sheng, J., Amankwah-Amoah, J., Khan, Z., & Wang, X. (2021). COVID-19 pandemic: The effects of AI, big data and ICT on virus detection, diagnosis and prevention. IEEE Access, 9, 145039–145060. https://doi.org/10.1109/ACCESS.2021.3122671
Syam, N., & Sharma, A. (2021). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 90, 323–330.
https://doi.org/10.1016/j.indmarman.2020.07.018
Wahab, S. N. A., Rahman, M. S., & Sulaiman, M. (2022). Digital leadership and organizational performance: The mediating role of AI capability and work design innovation. Sustainability, 14(5), 2615. https://doi.org/10.3390/su14052615.
Wang, J., Yang, Y., Xu, Y., & Li, J. (2023). Ethical AI and organizational performance: The role of trust, transparency, and fairness. Journal of Business Research, 158, 113655.
Copyright (c) 2025 Pradeep Kumar Upreti, Neha Gangwar
This work is licensed under a Creative Commons Attribution 4.0 International License.
All Content should be original and unpublished.
