Hotels are struggling to appear in artificial intelligence-generated travel recommendations as large language models (LLMs) change how travelers discover and compare lodging options.
With AI-driven travel planning growing in popularity, a hotel’s visibility increasingly is determined by how complete and consistent its information is across digital channels.
A report from HotelWorld AI, World’s Best at AI 2025 Index, found that only about 16% of global hotel supply is visible in search results on ChatGPT, Google's AI and Perplexity, leaving most properties absent from what are becoming key resources for trip planning.
The report, which analyzed 131,000 properties in 30 countries, ranked visibility in these platforms across the hotel landscape. The findings indicate that chain-affiliated properties are significantly more likely to be surfaced compared to independents, with seven parent chains accounting for the top 25 most-visible brands.
According to the report, this creates a two-tier system of brands that are “known” and, more often, brands that are unknown to AI.
From search results to shortlists
AI is also compressing the discovery process from long lists of links into a small set of recommendations, according to the HotelWorld AI report. That means hotels that are not named in AI-generated answers may never enter the consideration set.
Hotels have long jockeyed for position in traditional search, but these new platforms introduce a different set of requirements.
“This is becoming a new distribution layer,” said Philip Warthen, CEO and co-founder of First Wave AI, which provides an AI platform for hotels. “In the past, hotels optimized for search rankings. Now, they need to optimize to be recommended, summarized and trusted by AI systems.”
Many hotels are excluded because their data footprint is fragmented or inconsistent, Warthen said.
“AI models prioritize confidence. They surface hotels they can clearly understand and validate across multiple sources."
How AI determines which hotels to surface
Systems such as ChatGPT, Perplexity and Google's AI evaluate a range of signals to decide which hotels appear in results.
According to Warthen, these signals include structured data, review volume and recency, consistency across platforms, third-party mentions and clear, descriptive website content.
But many of these signals are not controlled by the hotel alone.
According to research from marketing platform Limy, AI tools frequently rely on third-party sources such as Reddit, NerdWallet, The Points Guy, Kayak, Going and Skyscanner, alongside forums, blogs and travel publishers. Limy also found that niche travel blogs often outperform major travel guides in AI-driven results.
“Sites that write for agents first are rewarded by AI search engines,” said Aviv Shamny, co-founder and CEO of Limy. “They’re not trying to sell you a room or a flight. Their single-minded focus on helpfulness is exactly what both Google and AI tools are designed to surface.
“AI tools love depth and specificity because it signals authority.”
Even when hotels are surfaced, they are not always represented accurately.
The HotelWorld AI report said that nearly half of hotels show some misalignment between how they position themselves and how AI systems interpret them, as models rely on pricing, reviews, imagery and external content to form a view.
“AI reflects the internet’s version of your brand, not your internal brand deck,” Warthen said.
Technical and content gaps limit visibility
Many of the factors limiting visibility are structural rather than strategic.
Warthen said in a LinkedIn post that hotel websites often are not designed in a way AI systems can easily interpret.
Many AI crawlers do not execute JavaScript, he said, meaning content embedded in booking engines or dynamic elements may not be visible.
He also pointed to gaps in structured data and schema markup, which help AI systems understand key attributes such as amenities, location and ratings.
Content quality is another constraint. Warthen said AI systems are more likely to surface information that is specific, factual and easy to extract, rather than “aspirational language.”
What hotels can do next
The steps to improve visibility are largely operational.
Warthen told PhocusWire that hotels should focus on strengthening the signals AI systems rely on. That includes cleaning up listings across all channels, adding structured data to websites and improving the volume and recency of reviews.
He also pointed to the importance of clearer, more descriptive content—particularly around amenities, location, guest type and on-property experiences.
These recommendations align with broader findings from Limy, which emphasize the value of specific, audience-focused and evergreen content. AI systems tend to surface sources that demonstrate depth, clarity and authority over purely transactional information.
HotelWorld AI’s research suggests hotels should ensure consistency across channels and reinforce a clear, distinctive positioning.
“Hotels that treat AI search strategically by structuring their content, clarifying their story and actively managing reviews and third-party profiles can win more direct demand and reduce dependency on intermediaries,” the HotelWorld report said.
The AI visibility gap
The shift to AI-driven discovery is taking place as parts of the industry remain early in their adoption of this technology.
Research conducted by Opinium for OpenAI found that three in 10 hospitality businesses are not using AI at all, making it one of the lowest-adopting sectors. The finding is based on a survey of 1,000 “decision-makers” at small and midsize enterprises (SMEs) in the United Kingdom.
Where AI is used, it is most often applied to internal tasks such as research, communications and brainstorming, the research found.
The HotelWorld AI report encourages hotels to take action, comparing the current moment to the early days of SEO. “Hotels that optimize for AI today will shape what AI recommends tomorrow.”
It also suggests AI visibility is self-reinforcing, with more exposure leading to more traffic, reviews and citations.
Warthen said early movers are already seeing results, including “stronger direct discovery, better qualified traffic, higher conversion intent and less dependence on traditional paid channels over time.”
The latest research is largely consistent with an earlier report from hospitality software platform Cloudbeds. Those findings pointed to a need for hotels to provide more information online to gain visibility.
AI systems increasingly are acting as the gatekeepers of discovery, reshaping how hotels are surfaced, considered and ultimately booked.