This study examines how managerial decision-making processes in modern healthcare organizations are being transformed through AI-enabled decision support systems (DSS) from a strategic management perspective. Faced with increasing data volumes, cost pressures, and performance expectations, healthcare institutions are shifting from intuitive and reactive decision models toward data-driven, predictive, and integrated decision mechanisms. In this context, AI-enabled DSS are regarded not merely as technical tools, but as strategic management instruments that enhance institutional decision-making capacity.

The conceptual structure of these systems is organized around three core components: the data layer, the analytical/modeling layer, and output–feedback mechanisms. Unlike traditional decision support systems, AI-based systems can generate proactive and dynamic recommendations due to their learning and adaptive capabilities. In this chapter, the role of AI in healthcare management is analyzed at three levels—strategic, tactical, and operational—in accordance with the original classification developed. Managerial gains in areas such as human resource management, financial sustainability, patient flow optimization, and organizational risk management are evaluated within the existing literature, while key risk domains including algorithmic bias, data security, the black box problem, and organizational resistance are discussed.

The findings indicate that the success of AI-enabled DSS depends not only on technological infrastructure but also on data governance, top management support, explainable AI approaches, and organizational digital capabilities. These systems represent more than operational tools; they constitute a strategic management capability that enables sustainable competitive advantage for healthcare organizations.