AI Visibility in Hospitality 2026
What determines whether AI recommends your property β and is your website ready for the answer? Two original studies across five European markets, the four leading AI models, and 1,337 audited accommodation websites.
The new reality: AI is replacing search
For two decades, finding a hotel meant a keyword, a list of blue links, and a booking. AI-powered discovery has no blue links β the assistant either recommends your property or it does not. To map this new landscape, Nokumo Research ran two studies between August 2025 and February 2026: 450 hospitality prompts tested across four AI models (GPT-5, Gemini 2.5, Perplexity and Mistral), yielding 3,600 responses, 20,370 URL citations and 13,859 brand mentions; and a technical audit of 1,337 accommodation websites across 30,935 pages in five markets β Germany, Austria, Switzerland, Slovenia and Croatia.
Booking.com β the undisputed champion
One domain towers over all others. Booking.com appears in 95.3% of every query tested and takes 14.5% of all citations β one in seven. The top three domains (Booking.com, TripAdvisor, Airbnb) account for 19.5% of citations, and only 86 of 4,043 domains are cited across all five countries. For every independent-hotel URL an AI cites, it cites roughly two OTA URLs.
Four AI models, four personalities
The models differ enough to be statistically distinct (ΟΒ² = 494.63, p < 0.001). GPT-5 is the most generous citer at 10.6 URLs per response and gives the best direct-booking outcomes. Perplexity is the most predictable and leans on review and UGC sources. Gemini 2.5 is the most OTA-heavy β significant given its place inside Google Search. Mistral returns zero URL citations yet still names 2.5 brands per answer.
What travelers ask, who AI recommends
Intent is destiny. Luxury, wellness and business queries surface direct hotel sites most often; budget-city, family and event queries are captured by OTAs. Event and festival queries are the OTA trap β 39.4% OTA dependency, the highest of any intent β so hotels near a venue should publish event-specific pages with direct booking CTAs.
| Intent | OTA % | Direct hotel % | Indep. hotel % | STR / VR % | DMO % |
|---|---|---|---|---|---|
| Luxury / Romantic | 16.6 | 39.2 | 20.4 | 1.5 | 3.4 |
| Wellness | 21.3 | 36.5 | 27.6 | 0.2 | 5.4 |
| Business | 19.8 | 37.3 | 19.0 | 1.0 | 4.5 |
| Unique Stays | 24.1 | 31.2 | 22.9 | 12.4 | 2.1 |
| Cultural / Sightseeing | 28.7 | 22.4 | 10.3 | 6.8 | 8.9 |
| Budget City | 34.2 | 18.6 | 9.2 | 8.4 | 3.2 |
| Family Vacation | 32.4 | 20.1 | 5.9 | 14.7 | 5.3 |
| Adventure / Nature | 22.8 | 16.4 | 11.3 | 30.2 | 15.3 |
| STR / Rental | 18.4 | 9.2 | 7.1 | 36.0 | 6.8 |
| Event / Festival | 39.4 | 14.8 | 3.6 | 7.4 | 5.8 |
The language effect
Every query was asked in English, German and Croatian. Language changes the answer. Croatian-language queries produce the highest OTA dependency in almost every market β up to ten percentage points higher β likely because Croatian-language training data is thinner on local providers, so models fall back to globally indexed OTAs. For DACH properties, German-language queries deliver the best direct-booking outcomes.
1,337 websites under the microscope
The technical audit found an industry operating at under 40% of its digital potential, with an average score of 38.1 / 100. 77.1% of properties have no booking engine, only 39.2% use any schema markup, and just 7% implement Hotel/LodgingBusiness schema. Most decisively, only 5.7% of audited properties were detected in any AI model response at all.
What AI-cited properties do differently
Of 1,337 properties, only 76 (5.7%) were cited by any model β and they are not the flashiest sites, they are the clearest. URL quality is the single biggest differentiator (Cohen's d = 0.60), followed by trust signals (0.50) and content quality (0.36). Schema, while worth doing, shows the smallest measured effect. A well-written 10-page site beats a flashy 50-page one.
Content strategy: what AI wants vs. what exists
The corpus is dominated by basic property information (61.1% of pages) while the categories AI actually queries on are missing. Zero percent of audited sites had a dedicated room-listing, dining or amenities page. The biggest content gap is budget and value β 74.8% of properties have none β followed by luxury positioning and events. Even a basic five-page site with entity-rich content outperforms 60% of the corpus.
| Rank | Company | Mentions | Category |
|---|---|---|---|
| 1 | Booking.com | 628 | Platform |
| 2 | Airbnb | 417 | Platform |
| 3 | Motel One | 99 | Chain |
| 4 | Hotel Cubo Ljubljana | 67 | Independent |
| 5 | Hostel Celica Ljubljana | 46 | Independent |
| 6 | City Hotel Ljubljana | 44 | Independent |
| 7 | Expedia | 41 | Platform |
| 8 | TripAdvisor | 38 | Review / UGC |
| 9 | Hilton | 36 | Chain |
| 10 | Marriott | 34 | Chain |
The playbook: what to do now
The actions that move the needle, in order. Today: complete and maintain your Booking.com and TripAdvisor listings, and list on your national and regional DMO. This week: sharpen a specific, factual positioning and build intent-specific pages for luxury, wellness or business travel. This month: add Hotel/LodgingBusiness schema and make sure robots.txt allows GPTBot, Google-Extended, ClaudeBot and PerplexityBot. Ongoing: build external authority through editorial coverage, and re-test 10β20 representative prompts every quarter.
Methodology & AI disclosure
Two studies, August 2025 to February 2026: 450 prompts across four models and three languages in five markets (3,600 responses, 20,370 citations, 13,859 brand mentions), plus a technical audit of 1,337 websites and 30,935 pages. Research design, analysis and conclusions are human-generated and independently verified; AI tools were used only for editorial assistance and source retrieval. Β© 2026 Nokumo Services d.o.o. (Infranet Group). Free to share with attribution (CC BY 4.0, with logo).
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AI Visibility in Hospitality 2026
15 min read Β· Nokumo Research