Cross-Domain Applications of AI in Management Decision-Making: Tourism vs. Healthcare

Abstract

Artificial intelligence (AI) has increasingly become a core instrument in management decision-making across diverse institutional domains. This study examines the cross-domain applications of AI in tourism and healthcare to demonstrate that AI primarily reshapes management logic rather than serving as a sector-specific technology. Drawing on verifiable datasets and established policy frameworks, the analysis focuses on how AI supports forecasting, resource optimization, risk management, strategic planning, and governance functions in both domains. The findings show that tourism management systems use AI to predict visitor demand, optimize capacity utilization, and enhance crisis response, while healthcare management systems apply AI to support risk stratification, resource allocation, and system-level planning. Despite differences in data environments, regulatory structures, and operational risks, the functional roles of AI remain structurally similar. AI-enabled decision-making improves decision quality, efficiency, transparency, and institutional coordination in both sectors.

By comparing tourism, healthcare, and urban governance contexts, this study demonstrates the transferability of AI-supported management functions across complex systems. The results highlight that AI operates as a management science tool that augments human judgment, strengthens governance frameworks, and enables data-driven decision processes. Healthcare is treated as one application case rather than the core focus of AI research, reinforcing the author's positioning as an AI management scholar. Overall, the study contributes to management science by clarifying how AI reshapes organizational decision-making logic across domains and by providing a cross-sectoral framework for understanding AI's role in contemporary governance systems.