How ChatGPT, Perplexity and Gemini Form Opinions About Your Hotel

Written by Shamus Smith, Founder, LuxDirect

All findings referenced in this article are based on structured query testing across six AI platforms using consistent prompt frameworks.

The Strategic Misconception

Many hotel leaders assume AI assistants function like traditional search engines, crawling the web in real time and presenting the most current information available.

That assumption is understandable. It is also incomplete.

In LuxDirect’s London scans across 25 hotels, just 5 properties captured 57% of all AI mentions. Most independents barely registered.

AI mediated discovery is not simply live search delivered conversationally. It is shaped by trained statistical models, layered retrieval systems, and learned hierarchies of credibility. For independent luxury hotels, understanding that distinction is no longer theoretical. It influences visibility, positioning, and ultimately distribution economics.

How AI Systems Form Hotel Representations

AI platforms including OpenAI (ChatGPT), Anthropic (Claude), Google (Gemini and Google AI Mode), Perplexity AI, and xAI (Grok) are built on large language models trained on mixtures of licensed data, publicly available content, and human generated material.

During training, these systems learn statistical relationships across vast volumes of text. They do not store structured profiles of individual hotels. Instead, they form probabilistic representations based on patterns such as repetition across domains, consistency of descriptions, review language density, structured data signals, and co-occurrence of brand attributes.

By the time a guest asks for a recommendation, the model has already formed a statistical understanding of your property derived from historical web content.

Many platforms now incorporate retrieval systems or live search capabilities. When enabled, these systems may fetch recent content. Even then, the base model interprets retrieved information through prior training. Results are ranked, filtered, and summarised before being presented.

Why Representation May Not Match Reality

AI systems do not discover your hotel from scratch each time. They generate responses based on available signals and reinforced patterns.

If those signals are inconsistent across platforms, outdated following renovations or repositioning, heavily influenced by third-party listings, or sparse in descriptive detail, the resulting representation may diverge from how you define your property internally.

Across LuxDirect audit runs in London, we frequently observe measurable gaps between a hotel’s intended positioning and its AI generated summary. The issue is rarely factual error. It is structural misalignment.

Why OTA Language Often Surfaces

Independent hotels commonly assume their own website is the dominant signal AI systems rely upon. Observed behaviour suggests this is not always the case.

Online travel agencies publish structured descriptions distributed across multiple domains, consistently formatted, reinforced by guest reviews, and repeated across affiliate networks. Cross source consistency appears to function as a strong statistical signal in both training data patterns and retrieval systems.

In many observed outputs, AI summaries mirror OTA listing language more closely than the hotel’s own brand narrative. This pattern reflects what is increasingly described as consensus bias: when a single OTA description is distributed across hundreds of affiliate sites, the AI encounters that specific phrasing far more frequently than your brand website. AI models do not simply look for quality, they look for certainty. If the consensus across the web describes your hotel one way and your own site describes it another, the AI is statistically more likely to treat your site as the outlier.

Citation Authority in the AI Layer

In traditional SEO, domain authority influences ranking. In AI mediated discovery, a related concept applies: citation authority, how consistently and credibly your hotel is described across sources that AI systems treat as reliable.

Specificity matters. A hotel described as “18 individually decorated rooms overlooking Grosvenor Square, each with handpicked antiques and Frette linen” creates a different statistical footprint than one described generically as “elegant accommodation in central London.” Models extract and reproduce precise detail more reliably than abstract luxury language.

This is the foundation of what practitioners in 2026 are calling Generative Engine Optimisation (GEO), the discipline of structuring your digital presence to be cited confidently by AI systems, rather than simply ranked by traditional search algorithms. In the retrieval-augmented generation (RAG) layer that many AI platforms now use, high authority citations carry disproportionate weight. A single mention in a reputable publication such as Condé Nast Traveller, The Telegraph, or a recognised “Best of London” list can carry more influence than a hundred posts on a hotel’s own blog. Editorial coverage is not just a PR metric. It is signal food for AI systems.

Citation authority builds gradually through reinforcement across your own website, editorial coverage, review depth, and cross domain consistency.

In March 2025, Perplexity AI integrated direct booking functionality via Tripadvisor and Selfbook. AI platforms are no longer purely discovery tools. They are becoming transaction layers.

The Structural Gap

To understand why this gap matters commercially, consider how it manifests in practice:

Across audits, the most consistent finding is not that AI systems are incorrect. It is that there is often a measurable gap between how a hotel defines itself, how the web documents it, and how AI systems currently represent it.

AI systems cannot reflect information that has not been clearly distributed and reinforced. Understanding your baseline AI representation is therefore the starting point of any AI visibility strategy.

As AI platforms move beyond forming opinions toward initiating agentic actions, completing bookings, making reservations, executing guest journeys, the consequences of low confidence representation become more direct. A platform that cannot confidently verify a hotel’s attributes may decline to initiate a booking action at all, responding instead with: “I don’t have enough verified information to complete this reservation.” Invisible to the guest. Invisible to the hotel. Lost before the transaction ever began.

Structural Reality

AI systems reflect reinforced patterns across the web. If your positioning is fragmented or inconsistently documented, AI representation is likely to reflect that fragmentation. Alignment requires clarity, consistency, and reinforcement across the sources that shape statistical understanding.

What to Do This Week

• Search your hotel name in ChatGPT, Gemini, and Perplexity. Note how each platform describes your property and where it routes booking intent.
• Check whether your Google Hotels connectivity is active. If booking pathways in Google AI Mode route to an OTA, this is the first structural issue to resolve.
• Audit your website for attribute density. Named suites, specific dining descriptions, and defined guest occasions give AI systems extractable signals to work with.