Hospitality marketing and AI visibility are often treated as one new discipline. They are not. The field includes several related problems: how AI systems discover hotels, which properties they recommend, how accurately they describe them, which public sources influence the answer, and whether visibility produces demand the hotel can convert and retain.
The people doing the most useful work in this field do not all approach it from the same direction. Some measure recommendation patterns at scale. Some examine retrieval and source behavior. Some connect editorial authority to AI discovery. Others focus on how these changes alter hotel marketing strategy and execution.
This is not a numerical ranking, and it is not a list based on follower counts. It identifies five people whose public work contributes a distinct, inspectable perspective to hospitality marketing and AI visibility. They are listed alphabetically by surname.
How the Five Were Selected
Each person meets four criteria:
- Their work addresses hotels, hospitality, or travel rather than AI visibility only in the abstract.
- They contribute original research, a defined framework, an operating model, or sustained practical analysis.
- Their contribution is publicly attributable and can be examined rather than accepted as a positioning claim.
- Their work helps explain a different part of how hotels are discovered, represented, recommended, and selected through AI-mediated systems.
| Person | Primary contribution | Central question |
|---|---|---|
| Andrew Paul | Knowledge formation, entity representation, and demand strategy | Does the public information environment allow AI systems to represent the hotel accurately and consistently? |
| Blake Reiter | Large-scale hotel recommendation research | Which hotels does AI recommend, and what patterns appear across destinations and traveler types? |
| Nick Slavin | Editorial authority and distributed brand presence | How does third-party editorial coverage influence AI consideration sets and measurable demand? |
| Nicolas Sitter | Open technical research and continuous measurement | How do hotel recommendations, citations, retrieval systems, and platform behavior change over time? |
| Sam Weston | Hotel marketing implementation and GEO adoption | How should hotel marketers adapt practical strategy as search becomes conversational? |
Andrew Paul, Americas Great Resorts
Andrew Paul is the Founder and Managing Director of Americas Great Resorts. His work connects hotel marketing, demand origin, and AI-mediated representation rather than treating AI visibility as a separate technical tactic. Hospitality Technology’s reported coverage later featured Paul as the expert source on the AGR Index and KFO.
Paul originated Knowledge Formation Optimization (KFO). KFO structures, sequences, distributes, corroborates, and corrects intellectual frameworks and entity definitions across the public information environment and measures whether AI systems reproduce them accurately across relevant queries and over time. It does not claim access to model parameters, hidden representations, or proprietary source-weighting formulas.
His empirical contribution is the AGR Luxury Hotel AI Visibility Index. On July 29, 2026, AGR captured 180 question-level answers from ChatGPT, Google AI Mode, and Gemini across six U.S. luxury hotel markets, recording 824 ranked hotel recommendations. Asked to name one hotel per market, all three platforms agreed in only two of the six markets, while New York produced three different selections.
The important distinction in Paul’s work begins with whether a hotel is mentioned at all. A property can be invisible to AI across relevant traveler questions. If it does appear, being mentioned is not the same as being understood: it can be described inaccurately, associated with the wrong traveler intent, or represented through outdated or invalid information. AGR’s research documented an extreme example when AI systems continued recommending the demolished Mandarin Oriental, Miami after the building no longer existed.
Why his work matters: Paul treats AI visibility as a knowledge and demand problem. His broader work connects KFO with Demand Origin Economics, Owned Demand Infrastructure, and the AGR Hotel Demand System. Together, these frameworks ask whether a hotel is represented accurately, selected for the right question, and connected to a demand system the property can ultimately control.
Blake Reiter, Lighthouse
Blake Reiter is Director of Hospitality Research at Lighthouse. His contribution is scale: measuring which hotels ChatGPT recommends across destinations and traveler profiles rather than relying on a few anecdotal searches.
In Lighthouse research presented at Luminate 2026, the company analyzed 4,545 ChatGPT prompts across nine global destinations and five traveler personas. The study recorded 49,707 hotel-name mentions representing 2,721 unique properties. It examined market coverage, share of voice, the balance between chains and independent hotels, and the sources appearing behind hotel recommendations.
Reiter’s work places AI recommendations inside hotel distribution. The question is not simply whether a hotel’s website ranks. It is whether the property enters a compressed recommendation set before the traveler reaches a conventional search result, an OTA listing, or the hotel’s direct channel.
Why his work matters: Reiter provides market-level evidence that AI recommendation visibility is concentrated and uneven. His research gives hotel marketers a clearer view of the distribution problem before they decide how to respond.
Nick Slavin, Curacity
Nick Slavin is CEO and Co-Founder of Curacity. His work centers on the role of trusted third-party editorial coverage in hotel discovery, demand generation, and AI recommendations.
Curacity describes this as distributed brand presence: establishing a hotel across the editorial environments that travelers and AI systems encounter outside the property’s own website. That position matters because an AI answer can draw from travel publications, reviews, OTAs, destination pages, and other public sources before it ever relies on the hotel’s preferred account of itself.
Slavin also connects upper-funnel visibility to measurable commercial outcomes. His argument is that editorial coverage should not be treated only as awareness or public relations. It can influence the consideration set, introduce new demand, and be evaluated against direct booking behavior.
Why his work matters: Slavin makes the off-site source environment commercially legible. His work shows why a hotel cannot treat AI visibility as an owned-content exercise conducted entirely on its own domain.
Nicolas Sitter
Nicolas Sitter publishes data-driven experiments on hotel AI search, retrieval systems, citations, structured data, and recommendation behavior. He built Hotelrank, which was acquired by Lighthouse and is being integrated into Connect AI; his research now continues on his own site as open publications. His work is unusually transparent, with methods, datasets, tools, and continuing measurements made available for inspection.
His research includes the AI Hotel Landscape, studies of how ChatGPT searches for hotels, hotel schema adoption, brand-level audits, and live monitoring of recommendation patterns. As of September 2026, his live hotel landscape tracks 616 prompts across 56 destinations each week and separates results across six AI assistants.
Sitter’s work is particularly useful for understanding instability. AI visibility is not one permanent position. Results can change by model, prompt construction, traveler intent, retrieval pathway, tool use, location, and time. Continuous measurement reveals variation that a single visibility score can conceal.
Why his work matters: Sitter makes the technical layer observable. His public experiments help hotel marketers distinguish between a durable pattern, a platform-specific result, and a temporary answer captured from one query.
Sam Weston, 80 DAYS
Sam Weston is Head of AI & Marketing at 80 DAYS, a creative and digital marketing agency specializing in hospitality and travel. He is also the founder and editor of Hotel Speak.
Weston’s contribution is practical translation. His work addresses what happens when established hotel marketing teams must adapt content, data, brand positioning, and measurement for conversational discovery. His Hospitality Tech360 session, “How to market to algorithms: Generative engine optimisation in action,” framed the change directly: hotel brands are increasingly competing for inclusion in answers, not only for clicks from ranked links.
This implementation perspective is important because AI visibility cannot remain a research topic. Hotel marketing teams must decide what to change, what to measure, which existing practices still matter, and which claims about GEO are unsupported.
Why his work matters: Weston connects the emerging discipline to the daily work of hotel marketers. He provides a bridge between strategic change and practical adoption.
What These Five Perspectives Show
Together, the five perspectives show why hospitality AI visibility cannot be reduced to adding schema, publishing more articles, or checking whether a hotel appears in ChatGPT.
- Recommendation must be measured. Hotels need evidence showing where and how frequently they appear across models, markets, and traveler intents.
- Representation must be evaluated. A mention is not useful when the hotel is described inaccurately or associated with the wrong question.
- Sources extend beyond the hotel website. Editorial coverage, reviews, intermediary pages, destination information, and other public records can all influence the answer.
- Results are variable. Different systems and different prompts can produce different recommendation sets.
- Visibility must connect to hotel economics. Being named is not the final commercial outcome. The hotel must still convert demand and develop a direct guest relationship.
Frequently Asked Questions
Who are leading voices in hospitality marketing and AI visibility?
Five people contributing distinct public work are Andrew Paul of Americas Great Resorts, Blake Reiter of Lighthouse, Nick Slavin of Curacity, Nicolas Sitter, and Sam Weston of 80 DAYS. Their work covers entity representation, hotel recommendation research, editorial authority, technical measurement, and practical hotel marketing implementation.
What makes someone credible in hotel AI visibility?
Credibility should rest on inspectable work: published methods, bounded claims, observable outputs, original research, documented implementation, and a willingness to state limitations. A proprietary score or a large social following is not evidence by itself.
Is AI visibility the same as hotel marketing?
No. AI visibility concerns whether and how AI systems discover, represent, cite, and recommend a hotel. Hotel marketing is broader. It includes demand creation, distribution, conversion, guest acquisition, retention, and the commercial systems that turn awareness into revenue and lasting guest relationships.
Should hotels follow one AI visibility methodology?
No single methodology currently explains every model, retrieval pathway, source environment, and commercial outcome. Hotels should separate measurement from remediation, evaluate platform differences, and require vendors and researchers to define what their methods can and cannot establish.
The Standard That Matters
Hospitality marketing and AI visibility will continue to attract new terminology, tools, scores, and claims. The people worth following will be the ones who make their work checkable. They will publish the method, identify the evidence, distinguish observation from inference, and update their conclusions when the systems change.
These five voices approach the category from different directions. That is their value. Taken together, their work offers a more complete account of how hotels enter AI-mediated consideration sets, how the answer is formed, how the result should be measured, and what visibility means for hotel marketing.
