The rapid adoption of Artificial Intelligence (AI) is transforming public sector organizations, reshaping policy design, service delivery, and decision-making processes. While much research emphasizes technical and operational dimensions of AI, less attention has been given to the leadership capabilities required to navigate AI-driven transformation. Drawing on Adaptive Leadership Theory, alongside public administration scholarship and emerging AI governance literature, this article develops a conceptual framework for AI-informed public sector leadership. The framework identifies key adaptive challenges, including ethical accountability, algorithmic bias, transparency deficits, data governance gaps, skills shortages, and risks to public trust. It also highlights opportunities such as evidence-based decision-making, operational efficiency, strategic foresight, and enhanced citizen engagement. The study argues that traditional bureaucratic leadership models are insufficient for governing AI-enabled systems. Instead, public leaders must cultivate AI literacy, ethical stewardship, interpretive competence, and adaptive capacity to mobilize collective learning and institutional change. Effective leadership in the AI era requires balancing technological innovation with democratic accountability and institutional legitimacy. By integrating adaptive leadership theory into AI governance discourse, this study contributes to research on digital governance and public sector transformation and offers a structured framework for responsible and value-driven AI adoption.
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