AI and Data-Driven Decision-Making in Travel and Tourism

Tourism has always depended on forecasting, from hoteliers anticipating seasonal demand to destination planners preparing for major events. Yet in an industry where visitor demand can surge or decline overnight in response to economic shifts, weather events, or media coverage, decisions based solely on historical trends are increasingly insufficient. Artificial intelligence (AI) and advanced analytics now enable organizations to model scenarios, monitor real-time conditions, and evaluate potential outcomes before committing resources. These capabilities allow airlines, hotels, attractions, and destination management organizations to improve capacity planning, optimize operations, and respond more effectively to changing market conditions. At the same time, reliance on data-driven insights raises important questions about balancing economic objectives with the needs of local communities, cultural assets, and environmental sustainability. AI and Data-Driven Decision-Making in Travel and Tourism explores how AI, predictive analytics, and intelligent decision-support systems are transforming strategic and operational decision-making across the tourism sector. It examines how data-sharing platforms, connected infrastructure, and machine learning technologies help organizations identify emerging risks, anticipate demand, allocate resources, and improve service delivery. This book also considers the organizational capabilities, workforce skills, and leadership practices required to effectively implement and manage these technologies. Covering topics such as crisis response in data sharing, multi-source tourism data, and workforce analytics, this book is a valuable resource for graduate and doctoral students, tourism researchers, destination managers, hospitality leaders, operations professionals, policymakers, and industry practitioners.