The question guests are already asking
Travellers no longer just search Google. An increasing share ask ChatGPT, Perplexity, or the AI overview at the top of their search results: “What are the best boutique hotels in Lisbon near the Alfama?” or “Is there a family-friendly Airbnb in the Swiss Alps under 200 euros a night?”
These questions get answered. The AI recommends specific properties by name. If your property is not named, you have lost the booking before the guest ever reached a booking platform.
What “entity recognition” actually means for a hotel
An entity, in AI terms, is a real-world thing that has a unique, consistent identity across the web: a name, a location, a category, a set of attributes. Your hotel or rental property either exists as a distinct entity in the AI’s knowledge graph, or it doesn’t.
An AI knows about an entity when it has seen that entity described consistently across multiple authoritative sources: your own website, Google Business Profile, TripAdvisor, Booking.com, Airbnb (for rentals), and any editorial coverage. The more consistent the name, address, category and key attributes, the more confident the AI is that the entity is real and relevant.
Inconsistency is the most common failure: the hotel is listed as “The Grand Harbour Hotel” on its website, “Grand Harbour” on TripAdvisor, and “Grand Harbour Hotel & Spa” on Booking.com. The AI sees three different things and doesn’t confidently unify them.
The three signals AI uses to rank a recommendation
Signal 1: Entity confidence. Does the AI know this property unambiguously? Consistent name, address and category across sources builds confidence. Inconsistency erodes it.
Signal 2: Information quality. What does the AI know about the property beyond its existence? Attributes (room count, amenities, proximity to landmarks, pricing tier, style descriptors) that appear consistently in reviews, descriptions and structured data give the AI material to match against a guest’s query. A property described only as “a nice hotel in Rome” cannot match a query for “a boutique hotel near the Colosseum with a rooftop bar”.
Signal 3: Review signal. AI engines weight reviews as a proxy for real-world quality. Not just the star rating: the volume, the recency, and the specific language guests use. A property where guests repeatedly mention “breakfast”, “views” and “quiet rooms” becomes more findable for queries that include those terms.
What AI search is not doing
AI search is not reading your website in real time. It is not browsing Booking.com to check availability. Most of what an AI knows about your property was learned during a training run that happened months ago, or was retrieved from a cached crawl of the web.
This has two implications. First, changes you make today may take weeks to appear in AI recommendations. Second, the AI may be confidently recommending a property based on outdated information.
The exception is search-augmented AI: Perplexity and Google AI Overviews do fetch live web content and include it in their answers. But even these systems weight authoritative, well-structured content over raw real-time data.
Why being on Booking.com is not enough
Booking.com and Airbnb create entity signals, but they don’t control what the AI learns. The AI reads many sources. A property that exists only on one OTA platform is a thin entity: one source, limited attributes, no corroboration. A property with a well-structured website, a complete Google Business Profile, strong and specific reviews across multiple platforms, and schema.org markup is a rich entity: many sources, many attributes, easy to match.
The properties that dominate AI recommendations in any city are almost always the ones with the richest entity profiles, not necessarily the best locations or the highest review scores.
Where to go next
If you are an Airbnb host: Airbnb & STR covers what a host can do without a website or technical knowledge.
If you run a hotel: Hotels covers property-level implementation and what to fix first.
To understand the technical implementation: Get found covers schema.org, llms.txt and entity consistency in detail.