Artificial Intelligence in Event Management: A Systematic Literature Review

Abstract

This study explores the applications of artificial intelligence (AI) in event management, analyzing its role across different types of events and different stages of the event life cycle. It also uncovers key opportunities and challenges of AI application in event management and lays down future research directions for this domain. A systematic literature review methodology was employed to analyze peer-reviewed studies on AI in event management. The review categorizes AI applications across various event types and event life cycle stages, highlighting trends, technological advancements, and industry implications. The findings demonstrate that AI is increasingly integrated into event management, offering significant benefits across a wide range of event types, including sports, festivals, art, music, and both corporate and private events. AI adds substantial value throughout the entire event life cycle, from planning to postevent evaluation, by enhancing efficiency, personalization, and decision-making. Key AI applications such as predictive analytics, chatbots, automated marketing, and real-time monitoring have proven to optimize event management processes. However, challenges related to data privacy, ethical considerations, algorithmic biases, and accessibility remain critical concerns. The study highlights the need for a balanced approach that integrates AI-driven automation with human expertise to ensure efficient, sustainable, and responsible event management. This study contributes to the growing research on AI in event management by synthesizing the existing literature in the field and offering a structured analysis of its applications, opportunities, and challenges. It provides a foundation for future research and practical recommendations for industry professionals navigating AI-driven event management. This includes focusing on different types of events such as music and sports in terms of potential AI adoption. By doing so small community-based events that happen at the grassroots level can be compared and contrasted to larger events that have the capacity to utilize newer forms of AI for event management.