AI-Driven Strategic Leadership: Integrating Decision Intelligence and Emotional Intelligence in MBA-Level Organizations

This paper proposes an integrated framework for AI-driven strategic leadership that combines Decision Intelligence (DI) and Emotional Intelligence (EI) to enhance organizational performance in complex, data-intensive environments. While artificial intelligence increasingly supports managerial decision-making through predictive analytics, optimization, and automation, existing models often overlook the critical role of emotional judgment, ethical accountability, and human-centered governance. Addressing this gap, the study introduces the AI–EI–DI (AI-EID) Leadership Model, which positions AI as a cognitive and analytical engine, DI as a structured decision architecture, and EI as the ethical, cultural, and trust-based moderator of strategic outcomes. Drawing on interdisciplinary literature from management information systems, strategic leadership, behavioral economics, and organizational psychology, the paper synthesizes theoretical insights with practical governance mechanisms applicable to MBA-level organizations. The framework maps the full decision lifecycle—from problem identification and scenario modeling to implementation, performance tracking, and corrective oversight—while clearly delineating the boundaries between algorithmic authority and human responsibility. Particular attention is given to risks such as cognitive bias amplification, accountability erosion, and ethical failure when AI systems operate without emotionally intelligent leadership oversight. The study contributes to the strategic management literature by offering a scalable leadership architecture that balances efficiency gains from AI with long-term organizational legitimacy, cultural resilience, and trust. Practical implications include a structured roadmap for executives and MBA practitioners seeking to leverage AI for competitive advantage without compromising ethical integrity or human accountability. The paper concludes with directions for future empirical research on AI-enabled leadership systems and governance models in digitally transformed organizations.