Hotel AI Search: How ChatGPT and Google AI Overviews Choose Which Hotels to Recommend

A growing share of travel planning now starts with a question to an AI assistant rather than a search box. The assistants answer with a shortlist of hotels and a handful of cited sources. This article explains how those shortlists are built and what it takes to get on them.

The question has changed

Five years ago a guest typed "hotels in Lisbon" and scrolled through ten blue links, most of them OTAs. Today the same guest is as likely to ask ChatGPT, Perplexity or Google's AI Overviews a full sentence: "Where should I stay in Lisbon for a month with fast wifi and a desk, under €200 a night?" The answer is not ten links. It is three to five named hotels, a few sentences about each, and a short list of sources the assistant leaned on.

For a hotel, that is a very different competition. There is no page two. You are either in the answer or you are not.

How assistants build a shortlist

The exact models differ, but the pattern is consistent across the major assistants.

They start from what they already know. Large language models carry a compressed memory of the public web as it stood when they were trained. If your hotel has been written about consistently — on your own site, in press, in guides — the model already has a sense of what you are and who you are for. If it has only seen an OTA listing, it knows your rate and your star count and little else.

They then search, and read a small number of pages. Most assistants now run a live web search for travel questions and read the top results. They favour pages that answer the question directly, that are clearly structured, and that come from sources they consider trustworthy: established travel media, well-maintained hotel sites, and review platforms.

They cross-check facts. Assistants compare what your site says with what Google Business Profile, OTAs and press say. Consistent facts — name, location, amenities, long-stay terms — make a hotel safe to recommend. Contradictions make it risky, and risky hotels get left out.

They match the guest's intent. "Fast wifi and a desk" is a filter. A hotel whose content explicitly describes its workspace, its measured connection speed and its monthly rates can be matched to the question. A hotel whose content says "comfortable rooms and warm hospitality" cannot.

What this means for a hotel

Three things follow.

First, the content that earns an AI recommendation is the same content that earns a page-one ranking: specific, human-written, structured, and about the things guests actually ask. Nothing about this rewards keyword stuffing or AI-generated filler; if anything, assistants are better than Google at ignoring it.

Second, third-party mentions matter more than ever. A guest blog, a press release picked up by a travel title, a quote in a hospitality publication: each one is a source an assistant can cite, and each one makes your hotel more likely to appear.

Third, the long tail is where independents win. Assistants answer specific questions. A 40-room property will not out-rank a chain for "hotels in Bangkok", but it can absolutely be the first answer to "quiet hotel in Bangkok for remote work with a monthly rate" if it has written the page that answers it.

Where to start

Audit what the assistants currently say about you — ask ChatGPT and Perplexity the questions your guests ask, and note whether you appear and what sources are cited. Fix inconsistencies in your basic facts across your site, Google Business Profile and the OTAs. Then publish the answers: a page for each of the five questions your ideal guest asks before booking, written plainly, with the numbers.

This is the work we do under AI search optimization, and it sits on top of the same hotel SEO and PR foundations we have always built.