AI-Driven Decision Support Systems for Operational Optimization in Hospitality Technology

Abstract

The hospitality technology industry has advanced infrastructure with integrated information systems. Well-designed AI modules—within an established Data Ecosystem and Development Framework—can support various organizational roles and enhance efficiency, speed, and reliability. Managerial decisions often require the help of data- and/or model-driven Decision Support Systems (DSS), such as reporting dashboards, staff scheduling tools, inventory management systems, and revenue management solutions. Demand forecasting, operational-level staffing, inventory management, dynamic pricing, and service-level optimization are primary decision areas. However, several limits impede DSS effectiveness: lack of user trust, excessive cognitive load, inadequate validation and monitoring, and poorly executed data-driven embeddings. Well-designed AI DSS addresses the challenges.

The proposed DSS concepts combine Decision Science fundamentals with specific Hospitality Technology requirements. Five central dimensions apply: key phases in substantial decision-making processes; different Decision Support levels in the operational-executive domain; the roles of decision-makers—receivers, planners, and validators; Human-in-the-loop methodology; and theoretical pillars, including Robustness, Interpretability, and Explainability. With a suitable architecture, Data Ecosystem, specific methods, and Human-AI collaboration, an AI DSS can assist operational optimization.