Do ChatGPT, Perplexity and Google AI Overview all work the same way?

No. ChatGPT, Perplexity, Claude and Google AI Overview each handle a travel query differently, so which one matters most to you depends on where your hotel’s signals are strongest.

ChatGPT (without browsing) recommends hotels based entirely on what it learned during training. Its training data has a cutoff (periodically updated, but not real time). It knows about properties that were well-represented in web content before the cutoff. If your hotel opened recently, or has thin online coverage, it may not know about you at all.

ChatGPT with browsing / Claude can fetch live web content when needed. These systems visit websites, read content, and incorporate it into their answers. For travel queries, they typically search, visit travel review sites and hotel websites, and include recent information. Your website’s readability and structure matter significantly here.

Perplexity always fetches live content. Every answer is generated from pages it retrieves in real time. It is the most “current” AI search engine for travel queries and the most directly affected by what your hotel website and review profiles look like right now.

Google AI Overview draws primarily on Google’s own data (Business Profile, Maps, indexed content) and real-time web fetch. It is most influenced by your Google Business Profile and schema.org markup.

What does an AI system actually do when it matches a travel query?

Matching a query means the AI runs through five steps at once: identifying the intent, parsing the location, parsing the attributes, retrieving the entities that fit, and generating a response naming specific properties. When a traveller asks “good hotel in Valencia for a romantic weekend, not a big chain”, those five steps look like this:

  1. Identifying the intent: accommodation recommendation, not information about Valencia
  2. Parsing the location: Valencia (which Valencia? Context clues determine Spain)
  3. Parsing the attributes: romantic (associated with: views, aesthetics, spa, couples activities), weekend (short stay, central location likely preferred), not a big chain (boutique, independent)
  4. Retrieving entities: which hotels in Valencia match this description based on what the AI knows
  5. Generating a response: naming specific properties and briefly describing why they fit

The properties that appear in step 4 are those whose entity profiles include all the relevant attribute signals. A hotel described across all its platforms as “intimate boutique hotel in the heart of Valencia, perfect for couples, rooftop terrace with city views” will match this query. A hotel described as “comfortable hotel in Valencia city centre” will not.

How does the language guests use in reviews feed into what the AI knows?

The specific wording guests use in reviews becomes part of the AI’s understanding of a hotel, so a hotel with hundreds of reviews repeating the same phrases is teaching the AI those associations directly. When hundreds of reviews of a Lisbon hotel mention “cobblestone views”, “tram outside the door”, “closest hotel to the castle” and “perfect for a first visit to Lisbon”, these phrases become part of the AI’s understanding of that hotel.

The AI doesn’t just learn that the hotel has good reviews. It learns the semantic content of those reviews: location signals, attribute signals, guest type signals. This is why responding to reviews, encouraging detailed reviews, and ensuring your property description uses the same language your guests naturally use are all meaningful AEO actions.

Which information sources do AI systems weight most heavily?

AI systems weight your own hotel website, your Google Business Profile, TripAdvisor and editorial travel publications most heavily, ahead of OTA listings and tourism boards, and well ahead of unverified social content. The full breakdown:

High weight: your own hotel website (especially structured data), Google Business Profile, TripAdvisor, editorial travel publications, prominent travel blogs.

Medium weight: Booking.com and other OTA listing content, local tourism board listings.

Lower weight: unverified user-generated content in obscure directories, social media posts without structured signals.

The strategy is to build strong, consistent signals in the high-weight sources first.

See also: Does ChatGPT recommend hotels | Get found: the technical playbook | How AI picks hotels