Part 5 of our series on AI visibility for hotels
Guest reviews have always mattered for hotels – what's new is who reads them today. Alongside people, ChatGPT, Gemini and Perplexity now also systematically evaluate reviews to formulate hotel recommendations. A recent survey shows: 71 percent of AI hotel recommendations rely on public guest reviews. Your reviews are therefore no longer just a trust signal for your future guests – they are also a data source from which AI systems learn how to describe your hotel.
The problem in everyday hotel operations
Many hotels manage their reviews using the same pattern as ten years ago: respond politely to criticism, occasionally ask for a review, done. That is enough for guests who read and judge for themselves. But it is no longer enough when an AI summarises the reviews and forms a compact assessment of your hotel from them. If many reviews repeatedly mention "loud street noise" or "slow breakfast", that becomes exactly the AI summary of your hotel – regardless of how many positive reviews sit alongside it.
Current findings from studies and practice
Several recent analyses show how strongly the significance of reviews is shifting in the AI age:
- 71 percent of AI-powered hotel recommendations are based on public guest reviews – making them the single most important data foundation for AI systems.
- 83 percent of users trust AI recommendations for purchasing decisions – anyone searching online for a hotel increasingly relies on the AI's condensed assessment rather than reading individual reviews themselves.
- Booking.com is the most frequently cited source for hotel recommendations in ChatGPT and Gemini, TripAdvisor the most frequently cited in Perplexity – a large analysis of over 245'000 AI source citations shows that the major AI systems rely heavily on established booking and review platforms rather than just the hotel's own website.
- Only around 40 percent of hotels respond to online reviews at all – yet the response itself is a signal that both guests and AI systems factor into their evaluation.
- An improvement of just one star on major review platforms can increase revenue by 5 to 9 percent – reviews therefore affect not only AI visibility, but also directly influence booking numbers.
What does this mean for your hotel?
1. Treat reviews as content, not just feedback
AI systems read reviews like text from which they extract facts: what is praised often, what is criticised often, which keywords appear repeatedly. Recurring, concrete statements carry more weight than isolated outliers. If you know which three to five topics come up most often in your reviews, you also have a rough idea of how an AI would summarise your hotel.
2. Respond visibly and specifically
A response to a review is not just a gesture towards the individual guest. It provides additional, publicly visible text that adds context – for example, if you respond to criticism about breakfast with a concrete measure. This puts individual negative points into perspective and shows both people and AI systems that feedback is taken seriously.
3. Cover several platforms, not just one
Since different AI systems favour different review sources – ChatGPT and Gemini rely heavily on Booking.com, Perplexity more on TripAdvisor – it is worth actively maintaining reviews on several platforms rather than just one. Anyone active only on Google remains incompletely visible to systems that draw more heavily on TripAdvisor or Booking.com.
Practical example: a city hotel in Basel
Starting point: A city hotel with 60 rooms had solid average reviews, but one recurring criticism: "Reception overwhelmed at check-in, long wait times." This statement appeared, in various wordings, in around a fifth of all reviews over the past twelve months.
Measure: The hotel introduced a second check-in desk during peak times and began consistently responding to every review that mentioned wait times – with a concrete reference to the measure instead of a general apology.
Result (fictional example, realistically derived from study figures): After six months, mentions of wait times in new reviews declined significantly. As this topic no longer appeared dominantly in the reviews, it also disappeared from the automatically generated summaries that guests received when searching for the hotel via AI – the description came across as more positive and balanced overall.
Common mistakes
- Only responding to negative reviews. Anyone who ignores positive reviews misses the opportunity to confirm and reinforce good statements.
- Maintaining reviews on only one platform. Anyone who focuses exclusively on Google remains invisible to AI systems that rely more heavily on TripAdvisor or Booking.com.
- Ignoring recurring criticism instead of acting on it. A single bad review is chance, a recurring pattern is a signal – also for AI systems, which specifically filter out such patterns.
Conclusion
Reviews were never just a footnote in hotel marketing, but in the AI age they gain an additional function: they are the raw data from which AI systems formulate their recommendations. Anyone who actively reads their reviews, responds to them seriously and maintains them across several platforms influences not only individual guests, but the way their hotel is described in a growing number of AI-powered search queries. This is not additional effort, but a shift in perspective on a task that many hotels are already doing anyway.
Further reading on our blog
- The historic shift in power: why Google is now the most important review platform for your hotel
- Google Business Profile 2026: what hoteliers need to change so AI finds them
- PR as a GEO lever: why media mentions make your hotel visible in ChatGPT
- How to write texts that an AI actually uses as an answer (Part 3 of our series)
Sources
- Hospitality Pioneers: Wie ChatGPT und Co. die Gästeakquise revolutionieren
- Nicolas Sitter: AI Hotel Landscape 2026
- Mara Solutions: Online-Überprüfungsstatistiken jedes Hotel muss im Jahr 2025 wissen
