The Boutique Hotel AI Index

Why We Ran the Boutique Hotel AI Index

For hotel marketers and agencies like ours, visibility in AI responses has become an increasingly important part of discussions with hotel owners and management teams. Yet there is still very little practical data about how AI platforms select, rank, and cite hotel recommendations, especially in the boutique and independent hotel space.

There is, however, a cacophony of online discussion circulating daily through blog posts and Linkedin opinions declaring the death of traditional online visibility practices and the ‘must-dos’ if you want your hotel to be found in the new AI visibility age. Do it now or your hotel will forever be left behind.

To be fair, there is plenty of great research and discussion conducted by well known marketing experts who we respect a lot. But more often than not the nuances and takeaways that are discussed are not hospitality-specific, and we feel that the hospitality vertical, and the boutique hotel segment in particular have their own specific set of challenges and opportunities that are often difficult to completely correlate with wider studies.

And so the idea for the Boutique Hotel AI Index was born: Take the best of the work currently being done in AI visibility research and apply it to a dataset gathered specifically for our area of interest and expertise.

Disclosure (it should come as no surprise): Kollective is a hospitality marketing agency. We sell hotel SEO and AI-visibility services, and this research informs that work.

The Study Setup

Our aim was to track hotel visibility, volatility, and discernible trends in AI-generated hotel recommendations. Which hotels are recommended? Which ones appear repeatedly? Which platforms are more consistent? Which sources are cited? And how much does the answer change when the same question is asked repeatedly?

We ran the same set of hotel recommendation prompts for 100 destinations worldwide. The destinations include a mix of cities, islands, and well-known boutique travel regions.

Each run included three hotel prompt categories: boutique, luxury, and romantic, chosen because they reflect the type of independent properties Kollective works with most often and where our spheres of interest and experience lie.

We chose ChatGPT, Google Gemini, Microsoft Copilot, Google AI Mode and Google AI Overviews, all on their consumer web interfaces, US locale, with web search enabled. These were chosen because they allowed us to capture the complete consumer-facing experience, including the interface, hotel rankings, citations and presentation of results. We deliberately excluded API-only models, as API responses do not necessarily reflect what a traveller sees when using consumer AI products and therefore were not appropriate for this study.

Put together, that gives 1,500 individual queries per run: 300 questions asked to five engines.

The Prompts We Chose

Our prompt setup was chosen specifically with the intention of not leading the AI engines with any type of persona identity beyond the boutique, romantic and luxury modifiers. The prompts were also worded specifically to try to ensure a list of hotels was returned each time. The prompts were:

What are the best boutique hotels in [location]?
Recommend some romantic hotels in [location]
Which luxury hotels in [location] would you recommend?

Can our prompt selection be debated? Of course. While we did slightly change the wording between the three bucket types we feel that they are minor modifications around a central theme (recommend some hotels) and we did not change the wording at all throughout the entire data collection period.

The Methodology

AI answer data was collected via Rankscale and verified against full page captures. Hotel names are matched through a normalisation process so that spelling and branding variants of the same property count once, and a blocklist removes non-hotel entities (booking sites, map providers, review platforms) that sometimes show up in AI answers. Where we report anything involving chain brands, we will publish the exact brand list used to classify them.

One methodology point should be stated up front.

We ran the identical 1,500-query panel twice on one day. The two runs agreed on the top recommendation 54.7% of the time. Runs conducted three days apart agreed 54.3% of the time. In other words, most of the day-to-day movement we see in AI recommendations is the engines generating fresh answers, not the market changing. We will report visibility share across repeated runs and never single-run rankings.

The usual caveats, stated plainly: fixed English phrasings, US locale for the main run, logged-out sessions, each run collected within a single day.

The Initial Dataset

From 18 June to 19 July 2026 we ran the same setup 15 times: 300 hotel-recommendation queries, put to five AI engines, 1,500 answers per run.

We also ran three supporting arms: UK, Singapore and Australia, to see whether user location changes the recommendations. A same-day repeat, the identical panel run twice in one day, to measure how much the engines vary their answers on their own. And a phrasing arm, the same question asked ten different ways, to measure how much the wording matters.

28,600 logged answers later, initial data collection is complete. This page is where the analysis will live.

The Study in Numbers

• 28,600 AI answers captured. 22,500 in the main panel, 3,600 in the UK, Singapore and Australia sidebars, 1,500 in the same-day repeat, 1,000 in the phrasing arm.
• 156,074 hotel naming events. One event is one hotel named in one answer on one run day.
• 9,895 distinct hotels after entity resolution to account for AI engines referring to the same hotel using different variations of its name.
• 252,032 cited-source records. Every citation URL the engines leaned on: 58,664 distinct pages across 11,811 distinct domains.

Every answer is captured in full, and every published number can trace back to this frozen 15-run dataset.

Here is the whole study in one line: the pool of hotels the engines drew from, run by run:

Fifteen runs in, the engines had not run out of new answers. The pool of recommended hotels kept growing until the last day of the study. That fact alone should change how we all read a single AI visibility screenshot.

What Happens Now

Analysis is under way, and we will publish findings on this page progressively: how the five engines differ, which sources they draw on, and what the recommended hotels may have in common. In the coming weeks we will also start checking the recommended properties against their structured data in Google’s hotel ecosystem and specific on-site and off-site factors. We look forward to comparing our data to previous studies both in the hospitality sector and the wider AI visibility space and to lively discussion and debate where it is due.

The main study now runs weekly as a continuing series, so the Boutique Hotel AI Index stays alive rather than becoming a snapshot of one month in 2026. Published findings will always cite the frozen 15-run dataset unless otherwise noted.

Please note: we will not name individual hotels in published findings. The Boutique Hotel AI Index aims to analyse the behaviour of the platforms, not report a league table of properties.