Artificial Intelligence and Leadership Effectiveness: Empirical Evidence from Multinational Corporations in Uganda
Year: 2025
Type: article
Study design: Observational Study
Abstract: 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.
Source: RA Journal Of Applied Research
Authors: Pradeep Kumar Upreti, Neha Gangwar
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410922799