AI Search for Hotels: How to Get Your Property Recommended by ChatGPT, Gemini, and Google AI Overviews
Five out of six hotel properties in the world are currently invisible when travellers use AI tools to search for accommodation. That figure comes from HotelWorld AI’s 2025 Index, the most comprehensive hotel-specific AI visibility study published to date, drawing on 2.36 million data points across ChatGPT, Gemini, and Perplexity. It covers 2,105 hotel brands and 130,884 properties across 30 countries. Five-sixths invisible. The remaining one-sixth is compounding its advantage with every passing month.
If you have been watching AI search from a distance, waiting to see how it develops before committing, this post is the clearest signal that the window for doing that has closed.
What follows is a practical account of how AI search for hotels actually works, what determines whether a property gets recommended or ignored, and what the path to visibility looks like across the platforms that matter. This is not a beginner’s guide to the terminology — if you need that, our earlier post covers the foundations. This is about what you do next.
01
Why AI search for hotels is a distribution problem, not just a marketing problem
The hotel industry has been here before. When OTAs mastered Google SEO in the early 2000s, the industry watched a new distribution layer form around it, one built on backlink authority, domain scale, and paid search dominance that individual hotel brands could rarely match. The consequences compounded for two decades: commission dependency, eroding direct booking share, and a structural disadvantage that proved extraordinarily difficult to reverse.
AI search is forming a new distribution layer. The same dynamics are in motion.
OTAs currently account for 55.3% of all AI-generated travel citations, according to Cloudbeds’ 2025 study analysing 810 prompts across ChatGPT, Perplexity, and Gemini for 145 properties in six global destinations. Tripadvisor, Booking.com, and Expedia lead. They are well-structured, data-consistent, and machine-readable at scale. AI models find them easy to cite.
The distribution problem is even sharper when you look at what happens after a hotel is recommended. LuxDirect’s study of 2,700 queries across 25 London luxury hotels found that 65.1% of Google AI Mode responses routed travellers to OTA booking pages rather than hotel websites. A property can appear in an AI recommendation and still lose the direct booking if the citation pathway runs through an intermediary. Being recommended and capturing the booking are two separate problems, and most hotels have not yet addressed either of them.
"The playing field will be levelled with new search habits. This is a re-democratisation event."
The case for optimism is genuine, though. OTAs captured traditional search by excelling at the signals Google rewarded: backlink profiles, domain authority, and scale. AI models weigh different signals entirely. Structured data quality, editorial mentions in travel publications, review sentiment, content specificity, and information consistency across platforms are what determine AI visibility. These are areas where individual hotel brands, particularly those with distinctive identities and genuine guest relationships, can compete in ways they never could in traditional search.
Hotel direct websites already capture 13.6% of AI citations, which is modest in absolute terms but significantly above the 9% cross-industry average for brand-owned sites. The foundation exists. Building on it is the work.
02
Why most hotels are invisible in AI search — and why it is not about domain authority
One of the most significant findings from recent AI search research is that the content getting cited does not come from the sources you would expect. BrightEdge’s longitudinal tracking reveals that only 17% of AI Overview citations come from pages ranking in the organic top ten. Five out of six citations originate from content that never appeared on page one of traditional Google results.
This matters enormously for hotels that have spent years investing in SEO but found themselves consistently outranked by OTAs. The rules have changed. Domain authority and backlink profiles, the foundations of OTA dominance in traditional search, are not the primary currency in AI search. The question AI models are asking is different: can I extract a clear, accurate, and trustworthy answer from this content?
Most hotel websites cannot satisfy that question, for reasons that are fixable rather than structural.
The most common issues we find when auditing hotel properties for AI visibility are predictable once you understand how AI models process content. Information that is inconsistent across platforms, a different address format on TripAdvisor than on the website, room types described differently on Booking.com than on the hotel’s own pages, creates uncertainty that suppresses citation confidence. Content that describes the property using “the hotel” or “the resort” rather than the property name leaves AI models unable to reliably attribute information to the right brand when processing content in isolation. FAQ content written for humans who are already on the website, rather than for AI models trying to answer a specific question from a cold start, fails to provide the extractable answers that AI tools are looking for.
These are not technical problems requiring specialist development. They are content and consistency problems that a structured approach can resolve.
The traffic context makes the stakes clear. Adobe Analytics found that GenAI-referred traffic to travel sites grew 3,500% year-on-year in July 2025. Despite that growth, AI platforms still drive approximately 1% of overall web traffic, which means the channel is expanding fast from a small base. The visitors arriving via that channel convert at four to five times the rate of traditional organic visitors, according to convergent findings from Noble Studios and Semrush. The volume is small now. The trajectory is not.
03
How each AI platform decides what to recommend
One of the most practically important findings from recent AI citation research is that each major platform has materially distinct citation behaviour. A strategy built around one platform will underperform on others. Understanding the differences is the starting point for a multi-platform approach.
Gemini is likely to be the AI search layer many travellers encounter first because it is connected to Google’s AI search experience. For hotels, this means Google-readable assets matter: the hotel website, structured data, Google Business Profile, images, video content, location information, reviews and clear page content.
Yext’s 2025 analysis of 6.8 million AI citations across ChatGPT, Gemini and Perplexity found clear differences between platforms. Gemini favoured websites, with 52.1% of citations coming from websites. OpenAI leaned more heavily on listings, at 48.7%. Perplexity showed a more diversified citation pattern, including sources such as MapQuest and TripAdvisor. The important caveat is that Yext’s published methodology covered retail, financial services, healthcare and food service, rather than hotels specifically, so these figures should be treated as useful directional evidence rather than hotel-specific benchmarks.
For hotels, the practical point is that Gemini visibility is likely to depend heavily on well-structured, Google-readable brand information: the hotel website, structured data, Google Business Profile, images, video content, reviews and clear page content. A YouTube presence may help in some contexts, but it should not be treated as the primary lever on its own.
ChatGPT operates differently. It remains the dominant AI chatbot referral source, but it does not behave like Google Search. Recent Statcounter data reported ChatGPT at 76.85% of AI chatbot referral share in April 2026, followed by Gemini at 9%, Perplexity at 7.73%, Copilot at 3.76% and Claude at 2.66%. ChatGPT still dominates, but the market is becoming more fragmented.
For hotels, ChatGPT can draw from a broad mix of public web sources, including hotel websites, review platforms, editorial articles, destination content, comparison pages and other third-party references. A hotel with strong brand-owned content but limited third-party presence may perform well in some AI environments and poorly in others. Broad distribution across credible third-party sources remains important.
Perplexity is different again. In January 2025, Perplexity launched a TripAdvisor integration for hotel search. The integration uses TripAdvisor information to enrich hotel results with descriptions, images, ratings and key attributes such as location, service and cleanliness. That makes TripAdvisor an especially important source to audit for hotel visibility on Perplexity, without making it the whole answer. Review volume, review recency, profile completeness, imagery and information consistency are obvious areas to examine.
Claude, developed by Anthropic, is a growing AI tool increasingly used for research and planning tasks. Detailed citation behaviour data for Claude in the travel sector is more limited than for the other platforms covered here, but its growing adoption makes it worth monitoring. The same foundations that build visibility on Gemini and ChatGPT, well-indexed brand content and consistent off-site presence, apply here too.
Copilot remains primarily relevant for corporate and business travel clients in Microsoft 365 environments, and is worth monitoring for that segment specifically.
The practical implication is that a hotel optimising only for its own website may be improving visibility in some AI environments while neglecting others. A hotel investing only in TripAdvisor reviews may be addressing Perplexity more strongly than Gemini. A hotel with limited third-party coverage may struggle to give ChatGPT enough external evidence to work with.
Effective AI search for hotels requires a platform-aware strategy that addresses each model’s distinct behaviour, rather than treating AI search as a single channel.
04
The five factors that determine AI visibility for hotels
The research across multiple studies points to a consistent set of factors that separate visible properties from invisible ones. Each of these operates at a strategic level and represents a programme of work rather than a single action.
Content that answers real questions in extractable form
AI models prioritise content that can provide a direct, accurate answer to a specific question without requiring interpretation. For hotels, this means pages that address the real questions prospective guests bring to AI tools: what makes this property different from comparable options, who is it genuinely suited to, what is the experience actually like, what is nearby and how accessible is it.
FAQ content is the most directly actionable format, but the standard of FAQ content on most hotel websites is not fit for purpose in AI search. Questions written for humans already browsing the site differ from questions phrased the way a traveller types into ChatGPT. The latter are conversational, specific, and often multi-constraint: “Is this hotel suitable for families with young children and is there a pool?” Content written to answer that question in one place, clearly and completely, is what AI models extract and cite.
Technical foundations that allow AI to read and use your content
AI agents, like traditional search crawlers, need to be able to access and process your content. Schema markup is among the highest-leverage technical factors: Hotel or LodgingBusiness schema with complete attributes, including amenities, check-in and check-out times, price range, and a clear property description, gives AI models structured data to draw on. FAQ schema applied to FAQ content signals directly that the page is designed to answer questions. Pages that load slowly, rely heavily on client-side JavaScript rendering, or contain inconsistent structured data are harder for AI models to process reliably.
Google’s AI Overviews eligibility follows standard indexation rules, which means a page must be crawled, indexed, and snippet-eligible to appear. This is foundational, not optional.
Off-site presence across the sources AI models trust
The research data on this point is striking and consistently underestimated by hotel marketers focused primarily on their own website. Cloudbeds found that 98% of AI-recommended hotel properties appeared on YouTube, 97% in travel blogs, and 95% on Reddit. Approximately 48% of AI citations come from community platforms like Reddit and YouTube, and 85% of brand mentions originate from third-party pages.
This means that for most AI platforms, a hotel’s off-site presence matters alongside its on-site content, and in some contexts may be the deciding factor. Editorial coverage in travel publications, appearances in travel blogs, a YouTube presence, and an active review profile across TripAdvisor, Google, and Booking.com collectively form the external signal set that AI models use to validate and characterise a property. A hotel whose entire digital presence is its own website is a hotel with insufficient external evidence for AI models to cite with confidence.
Information consistency across every platform where you appear
AI models aggregate information about a hotel from multiple sources simultaneously. When that information is inconsistent, the model has to make judgements about which version is accurate. This introduces uncertainty that reduces citation confidence. The address format used on TripAdvisor should match the format on the website. Room types named differently on Booking.com than on the hotel’s own booking engine create confusion. Amenity descriptions that vary between platforms give AI models conflicting signals about what the property actually offers.
Consistency is not a technical complexity. It is an operational discipline that requires a systematic audit of every platform where the property appears, followed by a structured process for keeping that information current.
Review signals and reputation management
Review volume, recency and sentiment across Google, TripAdvisor, Booking.com and other relevant platforms all contribute to the external evidence AI systems can use. Google reviews are particularly important for Google-led experiences because they sit close to the information ecosystem Google uses across Search, Business Profiles and AI Overviews. A property with strong, recent reviews across multiple platforms provides AI models with positive, credible, third-party evidence to draw on.
Review solicitation is an operational habit, not a marketing campaign. The properties winning AI citations have built it into the guest journey systematically, not as a periodic effort when review scores decline.
05
The flywheel effect: why early movers are pulling ahead
Citation positions in AI search are not like search rankings, which shift with algorithm updates and competitive content investment. They are proving remarkably stable once formed. BrightEdge data shows that 96.8% of cited domains show zero change week to week. The top two citation positions are 99.4% stable. When changes do occur, 87% are losses — domains drop out entirely rather than gradually fading.
This stability is explained by what HotelWorld AI calls the flywheel effect. Properties appearing in AI recommendations generate more bookings, which generates more reviews, which generates more third-party mentions, which reinforces AI visibility. The mechanism is self-reinforcing once it is in motion. Properties not yet in the loop face an increasingly difficult entry problem as the gap between visible and invisible properties widens.
The concentration of AI visibility in travel amplifies this effect. BrightEdge data shows an average of 26.2 brands mentioned per prompt across the travel sector, the highest figure of any industry tracked. Five London luxury hotels captured 57% of all AI recommendations across 25 properties studied. The brands appearing consistently are not distributing visibility evenly. They are concentrating it.
Travel is also the vertical with the sharpest organic traffic decline following AI search adoption. A study of 800 UK companies found that organic traffic growth in hospitality slowed from 26.3% before AI Overviews launched to just 3.7% after, making hospitality the most affected sector of the sixteen industries studied. The mid-funnel, where travellers compare destinations and hotel options, is eroding fastest as AI synthesises these comparisons directly. The traffic that used to flow through those comparison searches is being captured inside AI platforms, and the hotels cited in those AI responses are the hotels receiving the intent-driven visitors who click through.
The 12 to 18 month window to establish visibility before citation patterns become harder to shift should not be treated as a precise forecast. But as a strategic principle, it is sound: the earlier a hotel starts building AI visibility, the more time it has to compound the signals that matter.
06
Where Digital Dialog starts
The work of building AI visibility for a hotel or travel brand begins with understanding exactly where that brand currently stands, platform by platform. This is not a theoretical question. The same property, tested with the same query, frequently produces completely different results across ChatGPT, Gemini, Perplexity, and Claude. A brand can be cited accurately on one platform and entirely absent on another. A property can appear in AI recommendations while the citation pathway routes travellers to an OTA booking page rather than the brand’s own website.
Knowing which of these situations applies is the starting point. Everything else follows from that.
We work with hotels and travel brands to establish their current AI visibility across the platforms their future guests are already using, identify the specific gaps driving invisibility or citation leakage, and build the content, technical, and off-site foundations that cause AI models to find, trust, and cite them. The methodology is grounded in the same principles we apply to our own business: search “AI consultant tourism” or “AI course tourism” in ChatGPT or Gemini and Digital Dialog appears consistently. That is the outcome of deliberate, systematic optimisation using the approach we bring to client work.
If you want to understand where your property currently stands in AI search, get in touch.
Not sure where your business stands in AI search?
Manu Kastia
Manu Kastia is Founder and AI consultant at Digital Dialog, an AI consultancy specialising in tourism, travel and hospitality. With over 15 years of experience, Manu's expertise encompasses AI strategy, training, and advisory services for the sector. He has successfully worked with major brands including Switzerland Tourism, British Airways, Eurostar, Tourism Ireland, and Marketing Manchester. Manu's passion for making AI practical and accessible has positioned him as a sought-after speaker at industry events and a trusted consultant for organisations across tourism, travel, and hospitality. He helps businesses navigate AI decisions through strategic advisory, hands-on training, and comprehensive AI literacy resources. Manu has played a pivotal role in advancing AI knowledge through training sessions and strategy consulting, empowering professionals to harness AI for genuine business outcomes. His extensive sector background and practical approach make him a trusted advisor for those looking to navigate AI opportunities with confidence.