AI Discovery · Direct Bookings
How AI assistants decide which hotels to recommend
A study of 19,579 AI runs and 31,138 hotels shows the machines read the OTAs — then send the traveller to your own website. And independents get more of those links than chains.
If you want to understand how ChatGPT, Gemini, Perplexity and Grok pick hotels, one study does most of the work. Nicolas Sitter's AI Hotel Landscape 2026 ran 2,500 prompts across six models — 19,579 runs in total — covering 25 cities, eight traveller personas and nine hotel types, and recorded 245,046 unique sources cited and 31,138 hotels mentioned.
Two findings from it change how an independent hotel should think about this channel. Neither is the one usually repeated.
The mechanism: they read the OTAs, then link to you
Every model consults online travel agencies heavily when forming its answer. Booking.com was cited in 63% of Gemini runs and 53.9% of GPT 5.2 runs. TripAdvisor appeared in 99.9% of Grok runs and 95.5% of Perplexity's. Every model scanned an OTA or metasearch site in more than half its answers.
Then it links somewhere else.
| Model | Link to hotel direct | Link to OTA |
|---|---|---|
| GPT 5.2 | 91.1% | 8.9% |
| Gemini 2.5 Flash | 89.4% | 10.6% |
| GPT 5.1 | 87.9% | 12.1% |
| Perplexity Sonar | 74.7% | 25.3% |
Between 75% and 91% of the hotel links an AI puts in front of a traveller point at the hotel's own website, not at an OTA.
This is close to the exact inverse of Google. On a search results page the OTAs occupy the paid slots, the map pack and most of the organic ones, and an independent hotel fights for a click on its own name. In an AI answer the OTA is the reference material and the hotel is the destination.
Note where the exception is: Perplexity, at 25.3% OTA plus a further 8% to TripAdvisor. Perplexity has had a TripAdvisor partnership since January 2025. Commercial arrangements shape these answers, and they will keep doing so — which is a reason to treat today's numbers as a snapshot rather than a law.
The second finding: independents get more of those links than chains
Within direct links, the split between chain and independent properties runs against expectation:
| Model | Chain | Independent |
|---|---|---|
| GPT 5.2 | 37.2% | 53.9% |
| GPT 5.1 | 34.9% | 53.0% |
| Gemini 2.5 Flash | 40.7% | 48.7% |
| Perplexity Sonar | 32.9% | 41.8% |
And it sharpens by segment. Four-star properties get 60.1% independent links. In Paris and London the independent share is 69%; in Tokyo, 75%. Couples as a persona: 61.3% independent.
Chains win at five stars — 55.8% of links, against just 2% going to OTAs — because a five-star chain property has a brand site, a Wikipedia entry and press coverage, which is exactly the material these systems trust.
But in the four-star, city-centre, character-property segment where most European independents actually sit, the machines are already favouring you over the chains. That is a genuinely new distribution fact, and it has a short window attached to it.
What this changes practically
Your website stops being a brochure and becomes the landing page for a channel you don't control. If an AI sends someone to your homepage and they cannot see availability, a price and a way to book within a few seconds, the referral converts to nothing. The traffic is qualified — the person has already read a recommendation — and it lands cold.
Your OTA listings stop being only a sales channel and become training data. The description, amenity list and review profile on Booking.com and TripAdvisor are what the model reads to decide whether to mention you at all. A thin or inconsistent listing does not just cost OTA bookings; it removes you from an answer that would have linked to your own site.
That is an uncomfortable dependency to sit with, and it is real: you are being described to travellers by text you wrote for a competitor's platform.
Depth is why consistency beats optimisation
GPT 5.2 scans an average of 27.34 URLs across 16.22 domains per query. Its predecessor scanned 11.8 URLs across 7.79 domains. Grok averages 58.5 URLs. Perplexity, limited by its API defaults, scans 8.19.
Search depth roughly doubled in one model generation. The practical consequence is blunt: if you appear on two or three sources and a competitor appears on twelve, the competitor wins — not because their pages are better, but because they survive a wider sweep.
This is why the tactics that worked for Google search transfer badly. There is no single page to rank. There is a footprint to make consistent: your own site, Google Business Profile, TripAdvisor, the OTAs, any editorial coverage, and increasingly the places people talk.
The models differ in where they look, and they move fast. GPT 5.1 cited Wikipedia in 75.1% of its runs; GPT 5.2 dropped that to 30% and roughly tripled its citation of hotel brand sites. Reddit fell from 14.6% to 2.3% between the same two versions. Gemini favours YouTube (13.6%). Grok leans on Facebook groups and subreddits — r/chubbytravel alone accounted for 1,299 citations.
Chasing any one of those is a bad use of a small hotel's time. The stable conclusion underneath the churn is that breadth and consistency survive model changes, and single-platform tactics do not.
Five things that actually move this
- Make your own site answer the question. Rates, availability, real photographs, address, neighbourhood, policies, in text a machine can read. Most independent hotel sites are designed to look good and say very little.
- Make the facts identical everywhere. Same address format, same amenity list, same room names, same phone number across your site, Google, TripAdvisor and every OTA. Contradictions make you a less reliable entity to cite.
- Add structured data. Schema.org markup for the property, rooms, ratings and location. It is the difference between a machine inferring what you are and being told.
- Be specific about who you are for. These systems answer persona-shaped questions — a honeymoon in Barcelona, a solo business trip to Berlin. Generic positioning matches nothing. A property that is clearly for couples, or clearly for families, gets matched.
- Check your server logs for AI referrers. ChatGPT, Perplexity and Gemini traffic is identifiable. Most hoteliers have never looked and are surprised by what is already arriving.
What does not work
Overclaiming. The study's own conclusion is worth repeating in plain terms: these models read your marketing and your reviews together, and where the two disagree they follow the reviews. A property that calls itself boutique luxury while its reviews describe a decent business hotel gets described as a decent business hotel.
There is no prompt, no schema and no agency that fixes that gap. It is the one part of AI visibility that is just the hotel being good.
Related: Is your hotel invisible to ChatGPT? — a ten-minute method for checking whether you appear at all. And the AI adoption gap — why independents are better placed here than the numbers suggest.
Published August 2026. Data: Nicolas Sitter, "AI Hotel Landscape 2026" — 19,579 runs across GPT 5.1, GPT 5.2, Gemini 2.5 Flash, Perplexity Sonar and Grok, collected December 2025 – January 2026. Model behaviour changes quickly; we review this article every six months.