AGR Research | Luxury Hotel AI Visibility | 2026 Edition
Five hotels, on average, captured half of the AI recommendation slots in the six US luxury markets studied. The Index measures which properties appeared most often, how concentrated those recommendations were, and where the answers contained outdated or incorrect information.
Basis: 824 recommendation slots in 180 question-level answers from ChatGPT, Google AI Mode, and Gemini, captured July 29, 2026. The capture dataset is available upon request.
Download the original July 29, 2026 PDF edition. This web page includes the September 5 editorial revision.
At a glance
- A small group captured half of the recommendations. The number of properties needed to reach half averaged five across the six markets. In Chicago, Maui, and Napa Valley, four properties were enough.
- Recommendations were concentrated across the tested question set. Across ten traveler intents and six markets, each market’s top three properties together received 41 percent of the study’s recommendation slots.
- Two platforms recommended a demolished hotel five times. Mandarin Oriental, Miami closed in May 2025 and was imploded in April 2026. It still appeared in the July 29 answers, including a top-five list.
- A few cited documents recurred across many answers. ChatGPT’s Los Angeles answers cited two Michelin Guide pages; its Chicago answers cited two Tripadvisor pages. Visible citations do not reveal every source or influence behind an answer.
- The platforms did not always choose the same winner. Asked to name one hotel per market, all three agreed in only two of the six markets.
What the Index measures
When a traveler asks an AI assistant where to stay, the answer can shape the initial shortlist. The AGR Luxury Hotel AI Visibility Index measures how often properties appear in those answers and how tightly the recommendations concentrate around a small group. It also records visible citations, agreement between platforms, and specific factual errors.
The 2026 edition tested ChatGPT, Google AI Mode, and Gemini in six US luxury markets: New York City, Los Angeles, Chicago, Miami, Maui, and Napa Valley. AGR used ten traveler-intent questions per market on each platform’s logged-out consumer web surface. The July 29 fieldwork produced 180 question-level answers and a total of 824 recommendation slots. The underlying captures are available upon request.
This is a snapshot of the specified markets, prompts, platforms, and capture conditions. Recommendation frequency is a measure of visibility within this sample. It does not measure bookings, revenue, market share, hotel quality, or the probability that a traveler will choose a property.
The 2026 Index
Exhibit 1
Recommendation concentration in six markets. In three markets, four properties reached half of the slots.
| Market | Top-three share of market slots | Hotels needed to reach half | Distinct hotels named | Most recommended property |
|---|---|---|---|---|
| Maui | 47% | 4 | 14 | Four Seasons Resort Maui at Wailea, 26 of 136 slots |
| Napa Valley | 46% | 4 | 20 | Auberge du Soleil, 22 of 135 |
| Chicago | 46% | 4 | 25 | The Peninsula Chicago, 24 of 142 |
| Miami | 39% | 5 | 31 | The Setai and Four Seasons at The Surf Club, 19 each of 138 |
| Los Angeles | 36% | 6 | 31 | The Beverly Hills Hotel, 21 of 137 |
| New York City | 29% | 7 | 31 | Aman New York, 16 of 136 |
Source: AGR’s July 29, 2026 capture dataset, 824 recommendation slots. Percentages are displayed as whole numbers; Napa Valley and Chicago both display 46 percent. The table does not distinguish their order within that rounded percentage.
Exhibit 2
The top three properties received 29 to 47 percent of each market’s slots, pooling the ten tested intents
On smaller screens, swipe or scroll horizontally to view the full chart.
Exhibit 3
Properties needed to reach half of a market’s slots, compared with all distinct hotels named in that market
On smaller screens, swipe or scroll horizontally to view the full chart.
Across the six markets, AGR recorded 152 distinct properties. In the combined dataset, 23 properties captured half of the 824 recommendation slots. Appearing at least once and appearing frequently are different measures of visibility.
AGR tested that second question in a follow-on study published September 8, 2026. Among 148 luxury hotels already recommended at least once in the Index, the Luxury Hotel AI Recommendation Study found no detectable association between the measured website AI-readiness variables and recommendation frequency. A model containing Forbes Travel Guide rating, Michelin Key count, and market accounted for 54.7 percent of the variance in log recommendation slot count. The study addresses how often already-recommended hotels appeared; it does not test what determines initial inclusion or establish that credentials cause recommendations.
The six markets
Maui had the highest concentration in this sample. Fourteen distinct hotels appeared. Four Seasons Resort Maui at Wailea took 26 of 136 slots, approximately 19 percent, the highest single-property share in the Index. All three platforms also selected it for the single-choice question.
Hotel Wailea (22 slots) and Andaz Maui (16) completed a top three with 47 percent of Maui’s slots. These results describe the returned recommendations; they do not establish the number or share of eligible luxury properties that were absent.
Napa Valley showed concentration within one brand collection. Auberge du Soleil led with 22 of 135 slots. Together, Auberge du Soleil, Stanly Ranch, and Solage, grouped in the study under Auberge Resorts Collection, received 41 percent of Napa’s recommendation slots.
That is approximately two in five slots associated with the same collection. It is a brand-affiliation finding, not a statement that the three properties share the same real-estate owner.
The Peninsula Chicago led Chicago. It appeared in 24 of 142 slots, approximately one in six, and won the single-choice question on all three platforms. With The Langham (22) and Four Seasons (20), the top three accounted for 46 percent of the market’s slots. Four properties reached half.
Miami had two joint leaders and a clear reliability failure. The Setai and Four Seasons at The Surf Club tied at 19 slots each, followed by Faena at 16. The answers also recommended Mandarin Oriental, Miami five times, although the hotel had closed and its building had been demolished.
Los Angeles named a broader field but still concentrated recommendations. The Beverly Hills Hotel led with 21 of 137 slots and received two of the three single-choice answers. Hotel Bel-Air followed with 17 slots. Thirty-one properties appeared, but six reached half of the market’s slots.
New York City had the lowest concentration in the Index. Aman New York led with 16 of 136 slots. The platforms gave three different answers to the single-choice question on the same day: The Fifth Avenue Hotel, The Plaza, and Aman New York.
Seven properties were needed to reach half of New York’s slots, the highest number among the six markets. The split in single-choice answers illustrates disagreement between platforms; it is separate from answer stability over repeated runs.
The demolished hotel
The fieldwork also identified specific recommendation errors. The most striking involved a property whose building had already been demolished. These examples document failures in the captured answers; they are not a measured error rate for every recommendation in the dataset.
On July 29, 2026, ChatGPT recommended Mandarin Oriental, Miami three times: for entertaining clients, for best service, and as an under-the-radar choice. Google AI Mode recommended it twice, including in its top-five luxury hotel answer for Miami. The hotel’s closure notice gives May 31, 2025 as its permanent closing date. The building was demolished by controlled implosion on April 12, 2026. Local 10’s reporting on the demolition documents the demolition and redevelopment context. A replacement development was planned for around 2030; it was not an operating substitute for the demolished hotel at the time of capture.
Exhibit 4
July 29 recommendations appeared 108 days after demolition and almost 14 months after closure
On smaller screens, swipe or scroll horizontally to view the full chart.
Timeline is schematic, not to scale. April 12 to July 29, 2026 is 108 days. Source: Local 10’s April 12 demolition report. AI recommendations are recorded in AGR’s capture dataset.
Renovation closure. ChatGPT also recommended The Ritz-Carlton Bal Harbour, Miami as an under-the-radar choice. Forbes Travel Guide’s property notice lists a renovation closure from April 7 to December 7, 2026. September 5 status update: the hotel’s official website now gives January 2027 as its planned reopening. This later update does not change what the July answers said.
Outdated brand name. Gemini recommended Montage Kapalua Bay after the property had become The Resort at Kapalua Bay. Marriott’s March 13, 2026 announcement states that its management began March 14, that the resort remained open, and that it was slated to join St. Regis Hotels & Resorts in 2027 following renovation. This example concerns an outdated name for a continuing property, not a demolished hotel.
Wrong property type. Google AI Mode answered one hotel question with a standalone restaurant on the 71st floor of an office tower.
AGR’s separate July 15, 2026 Miami capture recorded recommendations for Mandarin Oriental, Miami and The Ritz-Carlton Bal Harbour while both were closed. The July 29 Index fieldwork found the same type of error. The two studies used different protocols, so this recurrence does not establish a controlled trend in model accuracy.
Which sources the answers cited
AGR logged the sources visibly cited in the answers. A small number of documents recurred across multiple questions in several market/platform combinations. This is evidence about citation patterns. Citations do not expose a model’s complete training history, retrieval process, or every influence on its recommendations, and the study does not establish that the cited pages caused a hotel to appear.
Exhibit 5
Recurring cited sources in the July 29 answers
| Platform | Pattern observed in the July 29 captures |
|---|---|
| ChatGPT | Across the ten Los Angeles answers, two Michelin Guide list pages recurred as cited sources. Across the ten Chicago answers, two Tripadvisor list pages recurred. The ten Maui answers cited Travel + Leisure’s Maui coverage. |
| Gemini | The New York, Los Angeles, and Miami answers each repeatedly cited one listicle from a lifestyle publisher. Maui and Napa Valley each repeatedly cited an article from another publisher, appearing in as many as eight of ten answers. |
| Google AI Mode | Google Maps appeared as a source on nearly every answer, alongside property websites. The captured answers displayed fewer editorial sources than the two chatbots. |
| All three | All three platforms cited material from r/chubbytravel. Its appearance establishes that this community was among the visible sources, not that it controlled the resulting recommendations. |
Source: cited-source logs in the AGR Luxury Hotel AI Visibility Index 2026 capture dataset.
Publisher disclosure. AGR produced this Index and also publishes luxury hotel rankings and sells AI visibility services. In two of the six markets, AGR material appeared among the cited sources, including in one market’s top-five answer. The Index measures market recommendation patterns, but AGR also participates in the information environment being measured. The retained capture records and source logs are available upon request so readers can examine that overlap.
Even the winners are not agreed upon
Concentration and agreement measure different things. Asked to select one hotel per market, all three platforms agreed in only two markets: Four Seasons Resort Maui at Wailea and The Peninsula Chicago. New York’s three platforms selected three different properties. Across the ten questions, the number of properties appearing on all three platforms ranged from six in Los Angeles to eleven in New York. A property’s visibility therefore depended in part on which platform was queried under the study conditions.
What this means for a luxury hotel
AI tools are becoming part of travel discovery. Skift Research reported that 30 percent of American travelers used AI extensively for trip planning in 2025, up from 13 percent in 2024. That is a separate survey finding, not an outcome measured by this Index. The Index shows that the hotel recommendations returned in its own sample were concentrated among a small group of properties.
For a hotel, the practical questions are whether it appears for relevant traveler needs, whether the description is accurate, and which public sources the answer cites. Checking closure dates, current names, amenities, positioning, and source consistency can identify errors that deserve correction. This observational study does not prove that any particular content change will improve rankings, generate bookings, or guarantee inclusion.
AGR’s playbook on how to get a hotel recommended by AI sets out the ordered sequence for that work: confirming crawler access, correcting the property record, and identifying the third-party sources cited in a market’s answers. It draws on this Index and does not claim that any step guarantees inclusion.
AGR’s AI Visibility Audit examines a property’s presence and representation in AI answers. Knowledge Formation Optimization (KFO) provides AGR’s discipline for work on the public information environment. Its definition and scope are published in the KFO canonical reference.
KFO definition. 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.
Hotels comparing outside firms can also consult AGR’s luxury hotel AI visibility agency comparison. It is an AGR-published evaluation and should be read alongside each firm’s evidence, methods, and commercial terms.
Methodology
Design. Six markets, three platforms, and ten traveler-intent questions per market/platform produced 180 question-level answers. The markets were New York City, Los Angeles, Chicago, Miami, Maui, and Napa Valley. The platforms were ChatGPT, Google AI Mode, and Gemini. Prompts covered ranked lists, unconstrained budgets, single-choice recommendations, romance, multigenerational families, client entertainment, wellness, dining, service, and insider discovery.
Prompt format. Each platform received the ten questions as one block for a market, with the location name changed across markets. The instructions requested independent treatment of each question, complete answers, ranked recommendations of up to five hotels where applicable, and sources. The 180 figure counts answers to individual questions, not 180 separate conversations. An instruction to treat questions independently does not establish statistical independence between answers in the same submission.
Capture conditions. Fieldwork was conducted manually on July 29, 2026, using logged-out consumer web interfaces and fresh private browsing windows from a New York origin. The protocol did not use an API or substantive follow-up questions. Three Gemini recaptures following clipboard truncation are disclosed below. Private browsing and logged-out use reduce account-based personalization; they do not eliminate influences such as IP location, language, session context, or platform changes. Holding the origin constant does not remove a possible New York location effect.
Platform inclusion. The fieldwork required logged-out access at the volume needed for the benchmark. AGR excluded Perplexity and Grok because of the logged-out query limits encountered, and Claude and Copilot because the tested access routes required accounts.
Scoring. Each recorded recommendation appearance contributes one slot; a higher list position does not receive additional weight. The published exhibits report two concentration measures: the share of a market’s slots held by its three most frequently recommended properties, and the minimum number of properties needed to reach half of its slots. Distinct-property counts and cross-platform agreement provide additional context. The 41 percent headline is the share of recommendation slots held by the market-specific top-three properties across the combined dataset. The five-property headline is the average of the six market-level half-share counts.
Property normalization. Sub-brand and villa products were merged into their parent resort, and rebranded properties were grouped under their normalized property identity, with merges documented in the dataset notes. The returned names remain important evidence when discussing stale branding or operational-status errors. A recommendation’s inclusion in the recorded results is not an endorsement of its accuracy or the property’s availability.
Fieldwork exceptions. ChatGPT declined the requested plain-text URL source format partway through fieldwork. The source-format instruction was amended; the traveler questions were unchanged. The logged-out Gemini surface identified its model as Flash-Lite during fieldwork. This records the displayed or reported model label, not independent verification of the underlying serving model. Google AI Mode gave four-hotel answers to some five-hotel questions and once returned a standalone restaurant. Answers were recorded and scored as given under the original study’s counting rules.
Recaptures and redaction. Three Gemini answers were recaptured in fresh sessions following clipboard truncation and were dated and marked in the dataset. These are exceptions to the single-run collection approach. One recommended Chicago property is included in the totals but is not publicly named under AGR’s standing editorial policy; the dataset explicitly marks the redaction.
Limits. Results apply to this one-day sample. Ten question intents do not represent every traveler, and answers may change with wording, location, account status, product updates, or repeat runs. Pooled slot counts give more weight to answers containing more recommendations. The study does not estimate national market shares, isolate the causal effect of a source or content intervention, or calculate a complete factual-error rate. It also does not yet report an invisibility rate against a defined universe of credentialed luxury hotels.
Planned additions. AGR plans a repeat New York capture to examine answer stability and a comparison with defined Forbes Travel Guide, Michelin Key, and AAA Diamond property lists to measure non-appearance. These are planned analyses, not findings in this edition. Any addition will identify its capture dates, eligible-property rules, and methodology at this address.
Prior work
The Index follows AGR’s July 2026 pilot: 300 question-level captures across the same six markets, using 25 traveler questions on the logged-in research modes of ChatGPT and Gemini. The pilot recorded concentrated recommendations, same-hour answer differences, and Reddit-sourced avoid lists that conflicted with other answers. Together, the pilot and this Index contain 480 question-level records collected under different protocols. The datasets are not pooled. The pilot’s New York City findings cover 50 answers to 25 questions on ChatGPT and Gemini, with final runs completed July 9, 2026.
A separate Miami study on July 15, 2026 tested one top-ten hotel question across ChatGPT, Gemini, Copilot, Perplexity, and Grok from South Florida. A second Copilot run brought the total to six lists and 60 ranked positions. That capture documented recommendations for closed hotels. It is separate from both the 300-record pilot and the July 29 Index dataset.
How to cite this Index
Andrew Paul, Americas Great Resorts. The AGR Luxury Hotel AI Visibility Index 2026. Published July 29, 2026; editorial revision September 5, 2026. Fieldwork: July 29, 2026. Permanent Index page.
Suggested short citation: “In AGR’s July 29, 2026 study of six US luxury markets, five hotels per market, on average, captured half of the recorded AI recommendation slots.”
Alternative short citation: “Each market’s three most recommended properties together received 41 percent of the slots in AGR’s six-market AI recommendation study (2026).”
The capture dataset and notes, covering the 824 recommendation slots with market, platform, question, rank, and date, are available upon request. Journalists and researchers may reproduce the exhibits above with attribution. Dataset and media inquiries: info@americasgreatresorts.net.
The AGR Luxury Hotel AI Visibility Index is published by Americas Great Resorts, operating in independent luxury hospitality since 1993. AGR plans annual editions and dated revisions at this permanent address. No hotel paid to appear in this Index, and inclusion cannot be purchased. Cite the edition year and fieldwork date with its findings.
Revision note, September 5, 2026: This editorial update clarifies the scope, terminology, methodology, and source interpretation; updates hotel-status references; and improves page structure. It preserves the original study’s reported results and July 29 fieldwork date.
