How AI Travel Planning Varies by What Travelers Spend

Artificial intelligence has rapidly moved from a novelty to a routine part of trip planning, and new research from Christopher Anderson and Young Jang examines how travelers at different spending levels are adopting the technology in distinct ways.

AI Use Rises Fast, but Not Evenly Across the Market

The study by Anderson and Jang, released this year as part of a broader wave of travel and hospitality research, places traveler spending power at the center of the AI adoption story. Their analysis aligns with recent consumer surveys showing that roughly one third of travelers in major Western markets have used generative AI for at least one element of trip planning, from destination research to in-trip recommendations.

Higher-spending travelers emerge as the most enthusiastic early adopters. Industry reports indicate that nearly half of frequent flyers, luxury guests and loyalty program elites now turn to generative AI tools to plan or modify their itineraries, a markedly higher share than among occasional or budget travelers. In these segments, AI is becoming an expected feature of the planning experience rather than an experimental add-on.

Lower-spending segments are engaging more cautiously. Surveys from technology and analytics firms suggest that many value-focused travelers are still in the discovery phase, testing AI for narrow tasks such as comparing prices, identifying low-cost routes or checking visa rules. Their usage levels are rising, but they tend to maintain manual checks on booking sites and metasearch platforms rather than delegating the full planning process to an AI agent.

Anderson and Jang position these differences as an emerging fault line that could reshape how travel providers design digital experiences. As AI becomes more deeply embedded in apps, websites and chat interfaces, the gap between high- and low-spend users may influence which tools get prioritized and which types of travelers benefit most from innovation.

High-Spend Travelers Push for Deep Personalization

Among premium and frequent travelers, the research points to a clear preference for AI systems that can handle complex, multi-constraint planning. These users are more likely to request tailored itineraries that account for loyalty status, preferred hotel brands, seat classes, wellness offerings and sustainability considerations. They also tend to travel more often, giving AI models richer data to learn from and raising expectations for personalized results.

Recent analytics from technology providers show that a significant share of AI travel users at the upper end of the market rely on assistants to orchestrate entire journeys, from first inspiration to last-mile transfers. For these travelers, AI is increasingly tasked with optimizing time as much as money, identifying efficient connections, minimizing layovers and aligning trips with demanding work schedules.

The spending profile of these travelers also changes how AI recommendations translate into revenue. When an AI system suggests a room upgrade, a private transfer or a high-end dining experience, high-spend users are more likely to act on those prompts. This, in turn, supports the argument that investment in sophisticated, data-rich AI tools can deliver higher returns when targeted at premium segments.

Anderson and Jang note that this dynamic may create a feedback loop. As providers see stronger revenue lifts from AI interactions with high-value guests, they may direct more innovation toward that group, further enhancing personalization at the top of the market while leaving entry-level experiences more basic.

Value Seekers Use AI to Stretch Their Budgets

In contrast, travelers in lower spending segments are primarily using AI to stretch limited budgets and reduce planning friction. Surveys from analytics firms and travel technology companies show that many of these users turn to generative AI for destination comparisons, fare-watching strategies and advice on the cheapest times to fly or stay. They are less likely to allow an AI tool to complete a booking on their behalf and more likely to treat AI suggestions as one input among many.

Anderson and Jang’s spending-segment lens helps explain why budget-conscious travelers approach AI differently. With less room for discretionary add-ons, these users focus on minimizing risk. They often double-check AI recommendations against traditional search results and place high value on transparency around fees, cancellation rules and total trip costs. For them, the key benefit is time saved in filtering options, not the thrill of a fully automated concierge.

The research also highlights how AI might shift on-the-ground spending for this group. By surfacing low-cost local restaurants, free cultural attractions or affordable public transport options, generative tools can redirect limited budgets toward experiences rather than logistics. This pattern is visible in recent survey findings that a majority of AI users ask about attractions, food and in-destination activities rather than only flights and hotels.

However, the study underscores a risk that budget travelers could be disadvantaged if AI models are trained primarily on premium inventory or heavily weighted commercial offers. Without safeguards, recommendation engines might systematically favor higher-margin options, making it harder for lower-spend users to find authentic, lower-cost alternatives even when they ask specifically for them.

Segmented Trust, Transparency and Data Concerns

Trust in AI generated advice also appears to vary by spending level. Higher-spend travelers, accustomed to tailored service from human agents and premium loyalty desks, often approach AI as an extension of that ecosystem. They may be more comfortable sharing extensive preference data and travel histories in exchange for more accurate recommendations and faster service.

By contrast, lower-spend travelers tend to express more concern about opaque algorithms, data privacy and potential bias. Publicly available research on consumer sentiment suggests that while many are curious about AI tools, they want clear explanations of how suggestions are generated, what data is being stored and whether commercial sponsorship influences the rankings they see.

Anderson and Jang argue that spending segments may therefore require different approaches to consent and communication. Premium users might respond well to highly integrated, proactive assistants that anticipate needs, while budget travelers may prefer more modular tools that they can toggle on and off and that offer plain-language rationales for each recommendation.

These variations in trust intersect with age and digital literacy, but the spending lens adds another dimension for travel brands. Designing AI interfaces that can adapt both to a user’s wallet and to their comfort level with automation is emerging as a core design challenge for online travel agencies, airlines and hotel groups.

Implications for Travel Brands and Destination Strategies

The spending-segment view of AI adoption carries significant implications for marketing, product design and destination management. Anderson and Jang point out that as more discovery and decision making shift into AI-driven environments, the pathways through which travelers encounter destinations will change, and not uniformly across income brackets.

For high-spend travelers, destinations may need to ensure that luxury, boutique and experience-led products are accurately represented in the datasets that feed AI tools. This includes up-to-date content on wellness, gastronomy and exclusive experiences, all of which are particularly relevant to this segment and more likely to be surfaced when an assistant is tasked with curating “exceptional” itineraries rather than simply “affordable” ones.

For value-focused visitors, a different content strategy is required. Destinations and local businesses will need to make sure that budget-friendly options, neighborhood experiences and public transport guidance are structured in ways that AI systems can easily parse. If not, the digital visibility gap between high-end and everyday travel products may widen as generative AI becomes the primary gateway for discovery.

The research suggests that travel brands ignoring these nuances risk amplifying existing inequalities in who benefits from technology. Providers that design AI journeys around a single archetypal traveler may find that they over-serve a narrow, affluent segment while leaving large groups of potential visitors with generic or mismatched advice. By treating spending segments as a core design variable, Anderson and Jang contend that the industry can steer AI development toward more inclusive and balanced growth.