How Hotels Appear in AI Search Results Before Guests Book

Nearly 40% of travellers used generative AI to research trips in 2025, yet most hotel websites still don’t appear clearly in AI answers. Hotels improve visibility by making their sites easier for AI to read, strengthening trusted third-party signals, and keeping booking paths, contact details and factual information current and machine-readable.

A striking hospitality study by Nokumo found that only 5.7% of accommodation websites were detected by any AI model, while Booking.com appeared in 95.3% of tested queries. The study covered 450 queries, four AI models, 3,600 AI responses and 1,337 website audits between August 2025 and February 2026.

The question isn’t whether AI search is becoming relevant. It already is. The question is whether your hotel is legible and credible enough to be included when a guest asks an AI tool where to stay.

This piece looks at why hotel sites are still being overlooked, what travellers are using AI for, which signals seem to help, and where direct bookings fit once your property has made the shortlist. For context on how this connects to hospitality operations more broadly, Coir’s Why AI Concierge Is The Future Of Luxury Hospitality is a useful companion read.

The clearest answer is that many hotel sites are still difficult for AI systems to interpret with confidence. Nokumo’s research found that 77.1% of accommodation websites had no booking engine visible to AI, and the average digital readiness score across audited sites was 38.1 out of 100.

That’s a serious commercial weakness. AI tools don’t only need content; they need signals they can parse, compare and cite. If the booking path is unclear, the property data is patchy, or the trust signals sit elsewhere, the answer engine is more likely to reach for a source it already understands.

Nokumo’s data is useful because it goes beyond generic SEO commentary. It shows a market where OTA authority still dominates AI recommendations, and where direct hotel websites are often too thin, too inconsistent or too opaque to compete for inclusion.

Here’s the most practical way to frame it:

That’s a core distribution problem.

OTAs tend to offer exactly what AI systems prefer: structured listings, broad authority, consistent entity data and a clear path from recommendation to action. Nokumo also found that clean URLs and trust signals had 2 to 3 times the impact of schema markup on AI visibility.

That point is worth sitting with for a moment.

Plenty of hospitality teams are being told to chase technical add-ons before they’ve sorted the basics. Yet the stronger signal, at least in this study, came from clarity and credibility.

Yes, and the pattern is becoming easier to read.

Phocuswright reported in November 2025 that nearly 40% of U.S. travellers had used generative AI to research trips that year, up 11 percentage points from the year before. In a separate July 2025 research update, Phocuswright found that 78% of travellers said GenAI results were somewhat or very helpful for trip planning and in-destination use.

That doesn’t mean guests are handing the entire booking process over to AI.

IMG’s 2026 Travel Outlook Survey, based on more than 1,000 customers, found that 33% said they were likely to use tools such as ChatGPT, Gemini, Copilot or Claude for travel planning in 2026. Among those users, 75% said they would use AI for recommendations, 70% for itineraries, 69% for discovering ideas, 55% for comparisons and only 13% for booking.

That distribution tells you where to focus.

AI is being used heavily at the point where travellers narrow their options. So if your property doesn’t appear during that stage, you’re absent before the booking engine even has a chance.

There’s another reason to take AI discovery seriously without overstating it. A March 2026 Civitatis survey of more than 7,000 users found that more than 60% still preferred human-curated recommendations and verified information, and nearly half of those who had used AI for trip planning had encountered incorrect or outdated information. The most common issues were hours, pricing and availability.

So the commercial pattern is fairly clear.

Guests are willing to ask AI for ideas, but they still want proof before they commit. That places a premium on accurate, current, cross-checked hotel information.

The best answer from the available evidence is simple: make it easier for machines to trust what they find.

Nokumo’s study points to a mix of readable site structure, factual content, clear booking paths and off-site authority. It also makes a useful observation that many marketers miss: trust signals and clean URLs appeared to have more effect than schema markup alone.

A hotel website should make core facts unmistakable. Property name, address, phone number, room types, amenities, contact routes and booking options need to be explicit, current and easy to interpret.

 

Yet basic information is often fragmented across pages, wrapped in design elements that work for humans but not for retrieval systems, or left stale after operational changes. Given that travellers report AI errors around pricing, hours and availability, stale information does more than reduce accuracy; it reduces confidence.

Your own site won’t carry the whole burden. Nokumo’s report highlights the importance of third-party authority such as Booking.com, TripAdvisor, destination listings and editorial coverage when AI systems assemble recommendations.

 

That means AI visibility is partly earned off-site.

 

If your Google Business Profile, OTA listings, tourism board entries and press mentions are inconsistent, the AI system has less reason to present your property with confidence. If they align well, the model has more corroboration to work with.

Start with what guests and machines both need: accurate facts, consistent entity signals and a direct route to act.

 

This is where a lot of AI advice becomes strangely abstract. In practice, hotel AI visibility begins with disciplined digital housekeeping.

For most independent hotels, the first month should focus on tightening the signals that are easiest to verify and easiest to neglect. Audit your contact details, room descriptions, rate information, booking engine visibility, Google Business Profile, OTA consistency and destination listings. Then review whether your most important pages answer obvious guest questions in plain language.

 

You should also look at mobile experience and page clarity, because AI-led discovery still hands off to a human user who has to verify and decide.

It’s sensible to implement structured data well. It’s less sensible to assume structured data on its own will solve visibility. Nokumo’s findings suggest that clean URLs and trust signals had 2 to 3 times the impact of schema markup in its hospitality research.

 

That should shape priorities.

 

Technical enhancement helps most when the underlying information is already reliable, visible and corroborated elsewhere.

AI visibility becomes commercially useful when a guest can move from recommendation to reassurance without leaving the trail you’ve created.

That’s where many hotel sites still fall short. Nokumo found that 77.1% of accommodation websites had no booking engine visible to AI, which means even a discoverable hotel may still lose ground when the user tries to take the next step.

If OTAs remain more legible to AI than hotel websites, AI-assisted discovery can easily reinforce intermediary dependence rather than reduce it. A hotel may be named, but the booking intent can still be captured elsewhere if the direct route feels less certain.

 

That’s why the conversation about direct bookings now needs a wider frame. It’s essential whether your property information, booking path and trust signals are strong enough to support a direct decision when AI helps create the shortlist.

Not every guest moves from AI answer to online booking. Some will call.

 

And when they do, the old pressure points return. Our past research points to Hostie AI’s 2025 study of 500,000 hospitality calls, which found that venues using AI phone handling reduced missed call rates from 36% to 3% and improved reservation conversions by 55%.

That creates a useful continuity between discovery and response. If AI helps a guest find you but the phone still goes unanswered, the revenue gap hasn’t disappeared. It has simply moved further down the funnel.

The strongest reading of the evidence is that trustworthy discovery will belong to hotels that keep their facts current across both owned and third-party sources.

That may sound less exciting than chasing every new platform feature.

It’s still where the advantage sits. Civitatis found that inaccurate or outdated information remains a live problem for AI-assisted travel planning, particularly around hours, pricing and availability. At the same time, traveller use of AI for research continues to grow.

So the task for hotels is straightforward, even if it takes discipline. Be discoverable. Be verifiable. Be easy to contact and easy to book.

Hotels don’t need to guess whether AI is influencing travel decisions. The research already shows it is. Phocuswright’s 2025 data points to rising use of generative AI in trip research, while specialist hospitality studies show that most hotel websites still struggle to appear clearly in AI answers.

What follows from that is more interesting than another technical checklist.

 

The hotels that benefit most are likely to be the ones that treat factual accuracy, entity consistency and booking clarity as part of commercial performance. If AI is helping guests decide where to stay, what exactly is your hotel giving it to trust?

The strongest evidence suggests you need more than keywords. Hotels are more likely to appear when their websites present clear factual information, readable booking paths, clean URLs and up-to-date contact details, while trusted third-party sources such as Booking.com, TripAdvisor and destination listings reinforce the same facts. Nokumo’s 2025 to 2026 hospitality study found these trust and clarity signals were highly influential.

OTAs tend to provide structured listings, broad authority and consistent information across many properties, which makes them easier for AI systems to cite. In Nokumo’s hospitality research, Booking.com appeared in 95.3% of tested queries, while only 5.7% of accommodation websites were detected by any AI model. That gap suggests AI tools still rely heavily on strong intermediary signals.

Yes, particularly for research and comparison. Phocuswright reported in November 2025 that nearly 40% of U.S. travellers had used generative AI to research trips that year. IMG’s 2026 Travel Outlook Survey also found that likely AI users were most interested in recommendations, itineraries, discovery and comparisons, while only 13% expected to use AI tools for booking.

It does help, but current hospitality research suggests it shouldn’t be treated as the first or only fix. Nokumo found that clean URLs and trust signals had 2 to 3 times the impact of schema markup in its accommodation visibility study. Schema works best when the hotel’s core information, booking path and third-party corroboration are already strong.

Recent survey evidence suggests travellers often verify hours, pricing and availability after using AI travel tools. Civitatis’ March 2026 survey of more than 7,000 users found that nearly half of those who had used AI for trip planning had encountered incorrect or outdated information, with those three categories among the most common issues. Hotels should treat these details as conversion-critical, not administrative.

It can, but only if the direct route is easy to trust and easy to complete. AI tools can influence the shortlist by helping travellers discover and compare hotels, yet Nokumo found that 77.1% of accommodation websites had no booking engine visible to AI. Better visibility helps most when a hotel also provides clear booking pathways, current information and responsive enquiry handling.