The Human-AI Leadership Nexus: Integrating Emotional Intelligence and Artificial Intelligence in Organizations

Abstract

Artificial intelligence (AI) is becoming embedded in organizational decision-making, managerial processes, and leadership practice, creating new opportunities for analytical augmentation while simultaneously raising questions about judgment, trust, accountability, and the human dimensions of leadership. Although research has examined Emotional Intelligence (EI) and AI-enabled management independently, limited theoretical attention has been given to how these forms of intelligence can be integrated within leadership practice. This conceptual paper introduces the Human-AI Leadership Nexus as a dynamic leadership capability through which leaders combine AI-enabled analytical intelligence with emotionally intelligent human judgment to interpret information, make decisions, allocate tasks, and manage the relational consequences of AI-mediated organizational action. Drawing on literature from Emotional Intelligence, Leadership, Human-AI collaboration, Algorithmic Management, and AI Governance, this paper develops an integrative framework comprising four mechanisms: analytical augmentation, emotional-contextual sense-making, integrative judgment, and relational legitimation. Four theoretical propositions specify how these mechanisms may influence leadership effectiveness, decision quality, employee trust, and acceptance of AI-mediated decisions, while accounting for task, technological, emotional, ethical, and organizational boundary conditions. The paper further argues that the effectiveness of Human-AI complementarity depends on calibrated reliance, meaningful human accountability, and governance arrangements that preserve fairness, transparency, employee voice, and contestability. The framework contributes to emerging scholarship on AI-enabled leadership by shifting the focus from technological substitution to an integration of distinct but complementary forms of intelligence. It concludes with a research agenda for construct development, empirical testing, multilevel analysis and investigation of autonomous AI systems.