Three different AI systems, three different approaches
Not all AI search works the same way. Understanding the differences helps you prioritise where to invest.
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 “matching a query” means
When a traveller asks “good hotel in Valencia for a romantic weekend, not a big chain”, the AI is doing several things simultaneously:
- Identifying the intent: accommodation recommendation, not information about Valencia
- Parsing the location: Valencia (which Valencia? Context clues determine Spain)
- Parsing the attributes: romantic (associated with: views, aesthetics, spa, couples activities), weekend (short stay, central location likely preferred), not a big chain (boutique, independent)
- Retrieving entities: which hotels in Valencia match this description based on what the AI knows
- 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 review language feeds the AI
One of the most important and underappreciated signals is the specific language guests use in reviews. 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.
The information hierarchy
When AI systems make travel recommendations, they weight different information sources differently:
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