Managing local visibility across hundreds or thousands of hotel properties has always been a data challenge. AI search is turning it into a recommendation challenge.
A hotel brand can have a strong digital presence across its portfolio and still struggle to get individual properties recommended by AI. An airport hotel, beach resort and mountain lodge may share the same brand standards, but they serve different guests, answer different questions and compete on different local experiences.
Many hoteliers are asking: “Will my hotel get recommended by ChatGPT?”
The better question is: “Does AI understand my properties well enough to recommend them?”
Recommendation is the outcome. Understanding is what earns it. That starts with managing local visibility at scale: accurate property data, differentiated local content, current amenities, consistent facts and a reputation that gives AI engines enough evidence to know what makes each hotel relevant.
Hotels are investing in AI. Guests are using it to choose.
Hotel leaders have embraced AI. In the Fall 2026 State of AI in the Hotel Industry survey of 107 hotel company leaders, 73% say they feel excited about AI at work. Not one respondent plans to spend less on AI next year.
At the Destination AI Summit in Washington, D.C., leaders from Hyatt, IHG, Hilton, Wyndham, Marriott and Choice Hotels agreed that hospitality is moving from AI experimentation to AI execution.
As my colleague Benu Aggarwal, President & Founder, Milestone Inc., observed after the session, most brands are building AI from the inside out, through data, governance and workflows.
AI engines choose hotels from the outside in.
Guests are already asking ChatGPT, Gemini, Perplexity and Google AI Mode where to stay. Instead of showing a list of blue links or websites as search results, these AI engines figure out what the traveler is looking for – the intent behind the prompt. Then they recommend two or three hotels and explain why each one is a good fit.
IHG described AI on stage as the new front door to travel, where recommendations matter more than listings.
For multi-location brands, a centralized AI strategy does not automatically create local visibility. Every property needs accurate, accessible and differentiated information that AI can use when answering a specific guest question.
Five gaps the research reveals
The State of Hotel AI Fall 2026 report surveyed 107 hotel company leaders on how they use AI, where they see results and what holds them back. Its findings show where hotels are investing in AI today and where guest discovery is being overlooked.
1. AI is working on costs, not guests
Operational efficiency is where 38% of leaders see AI’s strongest results. Only 13% name new revenue as their main AI goal, and 9% name guest experience. How hotels get found and recommended by AI is receiving far less attention.
There’s an irony here: 72% of leaders reach AI mainly through general-purpose tools like ChatGPT, Gemini or Copilot. Those are the same tools their guests increasingly use to choose where to stay.
2. Returns are hard to prove
Only 24% say their AI investment has paid for itself, while 28% say it is too early to tell.
AI visibility creates a concrete commercial question: Are your properties being recommended when travelers ask for hotels like yours?
That can be measured property by property, market by market and prompt by prompt.
3. The data isn’t ready
Just 42% of leaders feel confident their data can support their AI goals. At Destination AI, Wyndham called unified data the accelerator for everything else.
For guest discovery, data quality isn’t just an internal issue. It affects what AI tells your next guest.
Hotel facts live across the PMS, booking engine, website, Google Business Profile, OTAs, directories and review platforms. Across hundreds of properties, even small inconsistencies become visibility problems at scale.
4. Execution is fragmented
Among companies with a defined AI strategy, 70% still describe execution as fragmented.
Guests see the consequences: a pet fee differs between the website and OTA, parking information is outdated, or breakfast hours don’t match.
AI has to decide which source to trust.
5. The blocker is process, not technology
Two in three leaders cite change management, data quality or systems integration as top barriers. Only 8% blame the AI models.
AI engines are ready to recommend hotels. The challenge for multi-location brands is giving them clear, accurate and up-to-date information about each property, so they can tell what makes every hotel a good fit for a guest.
What AI needs before it recommends a hotel
Hotel search has moved from getting listed, to showing up on the map, to winning direct bookings, to AI answering guest questions.
We call the next stage Local 5.0. AI reads what the guest wants, evaluates what it knows about nearby properties and recommends the hotels it believes best match the request.
AI doesn’t see a hotel portfolio as one entity. It evaluates individual properties against individual guest needs.
Four things matter most.
- Details AI can confirm
AI needs to verify your pet fee, pool hours, airport shuttle, parking policy, accessibility features or breakfast options. If it can’t confirm an important detail, it may move on to a competitor or surface your hotel through an OTA.
- A distinct story for each property
An airport hotel and mountain lodge under the same brand serve very different guests. If both rely on generic amenity language, AI has little reason to match either property to a specific traveler.
Hilton’s phrase from Destination AI fits here: freedom within a framework. The brand sets the standards. Each property needs enough freedom to tell its own local story: who it serves, what is nearby and what makes its location useful.
- Fact parity, not just rate parity
Hotel information lives across the PMS, booking engine, website, Google Business Profile, OTAs, directories and review sites.
When parking costs or breakfast hours differ between channels, AI has conflicting evidence. Every important property fact needs an approved source of truth and a process for keeping downstream channels current.
- Specific answers to specific questions
Guests don’t always search for “hotels in Orlando.” They ask: “We’re flying into Orlando with a toddler and my mom, who uses a wheelchair. We need a resort with a park shuttle, a roll-in shower and a crib.” AI breaks that request into requirements and may recommend only two or three properties.
Facts come mainly from hotel websites and structured sources, while opinions such as “great for families” are reinforced by reviews. Rates and availability matter too. The more specific the query becomes, the more important accurate local data becomes.
Six steps to get your properties recommended
In September, the AI Hospitality Alliance published its Hotel AI Visibility Criteria and KPIs guide, with practical guidance for improving how hotels are found, represented, recommended and verified in AI-mediated discovery. Milestone Inc. contributed to it.
For multi-location brands, the framework can become an operating model for managing AI visibility across the portfolio.
Step 1: Start with the guests you want to win
Decide which guests each property genuinely suits and what the hotel can prove sets it apart. Use PMS and CRM data, reviews and guest questions to create a fixed set of real prompts. A downtown convention hotel, airport hotel and beach resort should not all be tested against the same questions.
Step 2: Make sure AI can reach every property
Confirm that AI engines can crawl your pages and that important facts appear in readable page content, not images or PDFs. Each property should resolve as one clear entity, with structured data matching what the page says. Also publish useful neighborhood context. Guests ask where to stay near stadiums, convention centers, beaches, airports and attractions.
Step 3: Give AI content it can trust
Each property page should answer real guest questions on its own. Let recurring review themes and guest questions identify content gaps. Every important fact should have one approved value, matched across your website, Google Business Profile, OTAs and directories. Back claims with proof, not superlatives.
Step 4: Check what AI actually says
Run priority prompts across ChatGPT, Gemini, Google AI Mode, AI Overviews and Perplexity several times. Record the date, prompt, AI engine, properties mentioned and sources cited. Answers change, so never judge a property on a single response.
Step 5: Measure presence, accuracy and recommendation separately
Track:
Presence: How often does the property appear?
Accuracy: Does AI get the facts right?
Recommendation: How often does AI recommend the property over nearby competitors?
Keep branded questions separate from open questions such as “best family resort in Orlando.” They test different things.
Step 6: Fix the source, then test again
If AI keeps quoting last year’s resort fee, find the page or listing it cites and fix that source first. Correct the website, update third-party profiles and rerun the same prompts. AI agents can help monitor listings, identify inconsistencies and flag changes across large portfolios while hotel teams approve important updates.
From being present to being recommended
For years, local visibility meant making sure every hotel could be found. AI raises the bar.
Now every property needs to be understood, trusted and selected.
Hotel companies are already increasing their investment in AI. The opportunity is to point part of that investment outward; toward the moment a guest asks an AI engine where to stay.
Start small, as Benu Aggarwal, President & Founder, Milestone Inc., recommends. Pick one business outcome. Identify the data it needs. Apply AI to one workflow. Measure the impact. Then scale.
For a multi-location brand, that could mean starting with one market, property type or set of high-value guest questions before expanding across the portfolio.
What happens on property now shapes what AI engines say about you. A changed parking fee becomes data. A recurring complaint becomes data. A great guest experience becomes a review, which becomes another signal AI can use.
Your guest experience has become part of your AI visibility.
The hotel brands that win won’t just manage listings at scale. They will manage understanding at scale, ensuring every property has accurate facts, a distinct local story and enough trusted evidence for AI to know exactly when to recommend it.
Try this now: Write down ten real questions your guests ask before booking. Ask each one in ChatGPT, Gemini, Perplexity and Google AI Mode, and note what they say about your properties.
The gap between those answers and the truth is your AI visibility to-do list.
