Original research on how hotels, restaurants, venues, and hospitality brands appear across Google, AI Overviews, ChatGPT, Gemini, Claude, Perplexity, and high-intent travel search.
Key findings
The public page is designed for journalists, analysts, newsletters, and industry operators. Findings stay tied to the benchmark sample and collection window.
Claude suggests a DIY approach in 40.7% of responses while recommending hiring a professional in only 3.7%, showing a strong self-service lean that businesses selling professional services must counter with clearer value framing.
The gap between ChatGPT (22.2%) and Claude (3.7%) on recommending professional hiring shows models disagree sharply on when consumers should pay for expertise versus handle things themselves.
Response length varies from 185 words (Gemini) to 604 words (ChatGPT), meaning the depth of guidance and context a hospitality business gets described with differs drastically by which AI the customer used.
Claude mentions checking reviews or ratings in 29.6% of answers versus 0% for Gemini, so reputation signals like review volume and rating scores carry inconsistent weight depending on the model.
This benchmark is researched and maintained in-house. For the underlying data, methodology detail, or an on-record comment for your story, reach the research lead directly.
Sector intelligence
Two proprietary datasets, refreshed on a set cadence: curated search-demand intelligence across every hospitality service we cover, and a controlled study of how ChatGPT, Claude and Gemini actually advise hospitality buyers. Every figure is citable — anchors are stable.
12-month demand trend
| ChatGPT | Claude | Gemini | |
|---|---|---|---|
| Recommends hiring a professional | 22% | 4% | 11% |
| Suggests DIY first | 22% | 41% | 22% |
| Names specific providers | 41% | 56% | 63% |
| Gives price or cost info | 19% | 22% | 22% |
| Tells to check reviews | 4% | 30% | 0% |
| Tells to verify credentials | 7% | 7% | 0% |
| Mentions case studies / portfolio | 0% | 0% | 0% |
| Mentions local proximity | 19% | 33% | 7% |
| Gives selection criteria | 33% | 56% | 26% |
| Warns about red flags | 4% | 7% | 4% |
| Asks a clarifying question | 41% | 56% | 4% |
| Recommends multiple quotes | 11% | 7% | 0% |
The ARI is the average rate at which ChatGPT, Claude and Gemini recommend hiring a professional, per industry. A named, quarterly-tracked index — cite it as “Authority Specialist AI Recommendation Index”.
| 1 | Legal | |
| 2 | Healthcare | |
| 3 | Automotive | |
| 4 | Beauty | |
| 5 | Home Services | |
| 6 | Real Estate | |
| 7 | Financial Services | |
| 8 | Fitness | |
| 9 | Professional Services | |
| 10 | Education | |
| 11 | Manufacturing | |
| 12 | Technology | |
| 13 | Ecommerce | |
| 14 | Hospitality |
About this benchmark
This report measures how visible hospitality and travel brands are across AI-generated search and recommendation environments, with attention to mentions, recommendations, citations, source quality, sentiment, trust evidence, and hallucination risk.
Measures whether AI systems recognize brands organically and where they appear inside generated recommendations.
Separates brand-owned websites, directories, media, review platforms, regulatory sources, and uncited assertions.
Checks whether public evidence supports safer descriptions, comparisons, and recommendations in high-intent searches.
AI-visible brands
Rankings are only shown when verified public data exists. The language is intentionally visibility-based, not a claim that one company is objectively better than another.
| 1 | Expedia | ||
| 2 | Booking.com | ||
| 3 | Marriott | ||
| 4 | Google Maps | ||
| 5 | Hilton | ||
| 6 | Kayak | ||
| 7 | Google Flights | ||
| 8 | Airbnb | ||
| 9 | Hyatt | ||
| 10 | Skyscanner |
Extracted verbatim from 81 stored AI responses (2026-07 edition). Mention frequency in stored AI responses. A mention is not an endorsement.
Methodology
Results are framed as observations from a defined sample and collection window. AI outputs can vary by model version, location, personalization, retrieval mode, and time.
Build a clean industry list covering national brands, mid-market players, organic challengers, specialists, and emerging entities.
Group prompts by recommendation, comparison, trust, alternative, informational, local, and high-value buyer intent.
Collect model outputs with consistent prompt wording, collection dates, country context, and search mode notes where available.
Review brand matches, citation URLs, sensitive claims, hallucination flags, and public wording before publication.
Downloads
Public downloads become active only when the underlying benchmark data and citations are ready for reuse.
Public report formatted for journalists, analysts, and internal stakeholders.
Summary table for verified benchmark metrics and public ranking fields.
Reusable PNG/SVG chart assets with attribution and canonical source links.
Collection window, prompt categories, model setup, scoring rubric, and limitations.
Resources
Service frameworks, authority playbooks, and implementation resources for hospitality and travel brands. Expand to see the full library, all reachable directly from this page.
Authority Specialist audits AI mention presence, recommendation gaps, citation sources, competitor visibility, E-E-A-T signals, and the 90-day authority roadmap required to improve visibility.