Transforming Travel Benefits through Generative AI: A Machine Learning Perspective on Enhancing Personalized Consumer Experiences

Abstract

With continuing advancements in generative artificial intelligence (AI), the opportunity arises to change how travel benefits are designed and curated. Generative AI models could be trained to produce customized travel benefits shaped through interactive dialogues with consumers based on their individual preferences and wants. Furthermore, the benefits could be augmented with personalized auxiliary information generated through AI across various textual, numeric, and visual formats. These possibilities could entrench the role of travel service providers as AI-curated travel solution architects and change how consumers interact with the services and benefits. In light of this vision, this research investigates how machine learning generative AI technologies could reshape the design and delivery of personalized travel benefits to consumers. Each of the four essential components in the generative AI pipeline that realizes this transformation is analyzed. First, the design and modeling of personalized travel benefit solutions are examined using a novel bi-agent generative model framework that simulates consumers’ benefit-acquiring behavior and travel benefit co-designing behavior. Second, the large-scale enrichment of the co-designed travel benefits using diverse auxiliary information generated through AI is analyzed. Third, the implementation of travel benefit consumer interaction with embedded generative AI capabilities is explored. Finally, the technology infrastructure for realizing the AI-centric generative travel benefit services is discussed. The findings and developmental pathways could enlighten researchers and practitioners in travel and consumer service industries about using generative AI technologies to enhance personalized consumer experiences in services.