OTAs Already Speak AI-Discovery Fluently. Most Hotel Marketers Are Still Learning the Vocabulary.

Hotel marketers are falling behind OTAs in fluency with AI-driven discovery, and a new Hospitality Net piece argues that gap is creating a structural negotiating disadvantage in distribution — not a marketing nice-to-have but a bargaining-power problem. The piece names “drift” as the specific failure mode: inaccurate AI-generated property descriptions from outdated training data or conflicting third-party listing data, worse for multi-property portfolios where inconsistent data across channels compounds. Its response is a literacy list — thirteen terms it argues hotel marketers now need fluency in, including MCP, Share of Model, Fact Engine, Agent Directives, and both GEO (Generative Engine Optimization) and AEO (Answer Engine Optimization) — framed as the direct successor discipline to SEO, requiring the same kind of dedicated measurement and correction workflow hotels eventually built for search.

Cendyn’s guide to the AI-driven travel era supplies the scale behind the urgency: 40% of global travelers have already turned to AI platforms — ChatGPT, Claude, Gemini, Perplexity — for travel recommendations, a shift Cendyn frames as fragmenting discovery from a single search path into a scattered ecosystem spanning search, AI assistants, social, and OTAs simultaneously. Cendyn flags “zero-click search” — travelers getting a full answer from an AI assistant without ever visiting a hotel’s own site — as a measurement problem that breaks traditional discovery-to-booking attribution outright. Cendyn’s own scale claim (roughly 35,000 customers and $20 billion in annual hotel revenue managed) is the kind of aggregate-data backing that makes the “40% of travelers” figure worth taking seriously rather than dismissing as vendor hyperbole.

Put the two pieces together and the sequencing problem is clear: OTAs built AI-discovery fluency as infrastructure while most individual hotel marketing teams were still optimizing for search-engine SEO. Closing that gap means treating AI-search visibility as its own measured discipline — tracking drift, correcting inaccurate AI mentions, and building the same discovery-to-booking attribution rigor for AI channels that already exists for search — before, not after, direct bookings show the effect of losing that fight.