An Examination of AI in Travel Planning Across Traveler Spending Segments

This study explores how travelers use artificial intelligence (AI) for travel planning across four spending segments: Budget, Premium, Aspirational, and Luxury. Based on a survey of 1,029 U.S. travelers, the analysis shows that AI is most widely used for discovery tasks, particularly identifying activities and attractions.

However, adoption varies by segment when AI is used for budgeting or validating travel decisions. Premium and Luxury travelers tend to use AI to quickly evaluate options, Aspirational travelers rely on it for curated hotel recommendations, and Budget travelers use it primarily to identify value. Across all segments, concerns about accuracy, transparency, and generic recommendations continue to limit broader trust and adoption.

GenAI also has the potential to increase the prevalence of hyper-segmentation and personalization by leveraging large datasets to treat each traveler as a segment of one (Unite.AI 2024). Major platforms such as Google and Expedia have already integrated GenAI to generate tailored itineraries and recommendations (Seyfi et al. 2025). Conversational AI further enables travelers to actively seek information, improving trip-planning efficiency while delivering customized recommendations and functioning as a 24/7 digital travel assistant (Wong et al. 2023).

In addition to functionality, however, AI adoption in travel planning ultimately hinges on consumer acceptance. Academic literature often frames this challenge through Innovation Resistance Theory (IRT), which explains why consumers may resist adopting new technologies even when they offer clear benefits (Lyu et al. 2024). For practitioners,  this  framework can serve as a predictive map for implementation challenges. Resistance to new technologies is rarely random; instead, it tends to be driven by specific functional barriers such as concerns about accuracy and trustworthiness, as well as psychological barriers, including the loss of human interaction and privacy risks.

Despite this technological transformation, a significant gap remains in understanding how different traveler segments perceive and adopt AI tools. It is often assumed that a single, one-size-fits-all approach to AI will appeal to the entire market. However, the value proposition of AI is not universal,  and motivations for adoption—or resistance—are likely shaped by a traveler’s financial capacity, behavioral patterns, and psychological preferences. This study addresses that gap by examining AI adoption motivations and perceived barriers across four distinct traveler spending segments: Budget, Premium, Aspirational, and Luxury.

This research aims to provide a more nuanced understanding of AI’s evolving role in the travel ecosystem. Using survey data from U.S. travelers, the study explores how factors such as price sensitivity, a desire for unique experiences, and expectations for human interaction influence the adoption of AI-driven travel planning tools. The ultimate goal is to offer actionable insights that help tourism stakeholders design AI tools aligned with the distinct needs and expectations of different traveler segments.

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