Make Hotels Discoverable to AI Without a Website Rebuild

Hotel discovery is moving from search to agents

Take a Monday-morning check at a hotel. A reservations manager opens the Google profile beside the OTA listing. A guest wants a quiet room with a desk next Tuesday. The profiles disagree about the room, or the booking engine has the only current answer. Which source should an agent trust?

The useful facts are usually scattered: a room detail on the website, an address in a map record, a policy in the booking engine, and an event mentioned somewhere else. Bringing those pieces together is what makes a property understandable to an agent.

For most of the last three decades, hotel discovery started with a person opening a search engine. The traveler typed a destination and dates. Search results led to a hotel website or a comparison service.

That journey is changing. A traveler can now describe a trip to an assistant in ordinary language: a quiet design hotel in Lisbon, close to the old tram routes, with a proper desk, dependable Wi-Fi, and a bar where a solo guest will feel comfortable. The assistant can search several sources and compare what it finds before continuing toward a reservation.

The first search result is no longer the whole product. What matters is the shortlist an agent builds, and whether it can verify what the hotel offers before recommending it.

The brand website still carries the story and confidence of a direct relationship. A guest agent can find the hotel and complete a booking elsewhere when that better fits the journey.

The numbers show why the entry point is changing

Search behavior offers a useful signal, though the evidence is narrower than a headline about “zero-click search” suggests. Ofcom's discussion paper, published 4 November 2025, cites Similarweb data showing that the number of news-related searches resulting in no onward clicks rose from 56% to 69% after Google's AI Overviews launched. The figure describes news searches, not all search behavior. Ofcom also records that Google disputes third-party claims about traffic declines and attributes them to flawed methodologies. Treat the number as directional. Some answer-engine journeys can end with an answer or shortlist before a traveler visits a hotel domain. Read the Ofcom discussion paper for the source and its limitations.

In an IAB study of AI shopping behavior, 46% of AI shoppers said they use AI most or every time they shop, and 80% expected to rely on it more. Yet 78% still visited a retailer's website, while only about one in three clicked directly from the AI platform. For hotels, the practical response is to make the property legible before the website visit and keep a direct agent route available when the guest prefers it.

SOCi's adjacent data gives this problem some scale. Its 2026 Local Visibility Index covered 350,000 locations and found 1.2% recommended by ChatGPT versus 35.9% represented in Google's local three-pack. The percentages are directional because the study is not hotel-specific. Conventional listing presence and answer-engine selection are separate outcomes.

Where agents find the hotel

An AI agent may read structured data, query a catalog, resolve a physical place, call an API, or communicate with another agent. A website may be useful for one request; another request may finish without loading it.

That creates a broader distribution surface for hotels. A property can be represented through:

• its own website and structured property content;
• destination and travel hubs;
• trusted directories and partner catalogs;
• verified place graphs and open geospatial datasets that anchor the hotel's identity, entrance, nearby places, and travel context;
• an A2A Agent Card, published at a well-known URL and represented in agent registries or catalogs;
• a direct endpoint connected to the booking engine, CRS, or PMS.

Within A2A, the Agent Card is the required machine-readable description of an agent's identity, skills, endpoint, capabilities, and authentication requirements. A registry represents or indexes that card so client agents can find it. The Agentic Resource Discovery (ARD) specification, co-authored with Microsoft and Hugging Face, defines a complementary way to publish and locate machine-readable resources, including through /.well-known/ai-catalog.json.

Imagine a guest agent asking, “Can I find a quiet room with a desk for next Tuesday?” ARD can point it toward a hotel profile, a live-availability capability, an event or package resource, and the endpoint that can answer the question. The card explains the agent's abilities and contact method. The registry represents the card. Direct configuration can point a client to it. See the A2A agent discovery guidance and the A2A specification.

For a hotel, the equivalent of a good storefront is a consistent set of facts and capabilities available wherever a guest agent might look. The questions are ordinary ones: what kind of property is this, which traveler does it suit, what does a room actually offer, what is available on the requested dates, and which policies apply? The harder question is whether the hotel can answer or complete a booking through a trusted interface.

For a hotel team, the website is one expression of the property. Other sources carry their part of the discovery work.

The same pattern applies beyond one property: destinations also need connected, current facts across places, events, businesses, and practical details.

Why OTA catalogs miss experiential hotel intent

OTAs are good at scale. They aggregate inventory and standardize room and rate fields. Their strength is breadth.

Their weakness is the shape and freshness of the data. A traveler may care about whether a hotel feels calm at night, whether a room supports focused work, whether the lobby is welcoming for a solo traveler, or whether the walk to a particular neighborhood is pleasant. Those preferences do not fit neatly into a standard amenity filter, and they can change from week to week.

An OTA may list Wi-Fi without telling an agent whether the connection holds up for a video call, whether the desk fits a laptop and notebook, or whether the room faces a lively street. A central-district label also leaves out the quiet side-street entrance two blocks from the busiest pedestrian route.

In a real booking conversation, that distinction can decide the recommendation.

It is even less likely to know what is happening at the property right now. The hotel may be organizing an evening with live music, extending restaurant hours, offering a limited promotion, or changing access to a hotel facility. An OTA usually cannot represent those changes with enough speed or nuance. It also cannot know that the hotel is willing to negotiate a special package, such as a room with breakfast, spa access, a concert ticket, and late checkout, unless the hotel exposes that offer and the rules for making it.

That gap creates a matching problem. The property that best fits the traveler can disappear from the shortlist because its distinctive qualities are absent, vague, or flattened into a checkbox. The hotel then pays an intermediary to reach a guest who was already looking for what the hotel offers.

Direct agentic distribution creates another route. A hotel's own description, local context, live event and promotion data, package rules, and booking capability can be available to the traveler’s agent without first passing through an OTA's interpretation of the property.

An OTA may know that a room exists. The hotel knows why a guest should choose it.

OTAs remain useful for reach. Hotels also have a reason to build a second route that rewards richer, more current, and more direct property knowledge.

The economics reinforce the case. OTA commissions are often around 15% to 25%, depending on the platform, property, program, and commission terms, according to Mews' 2026 overview. Integration and operating costs remain part of a direct agentic route. The route can still give the hotel another way to compete on fit and retain more control over the guest relationship.

Research shows richer evidence can change visibility

The research supports the distribution argument, with limits. In the Generative Engine Optimization study by Aggarwal and colleagues, a benchmark of about 10,000 queries reported relative improvements of roughly 30–40% on the Position-Adjusted Word Count metric. In Table 5, Quotation Addition = 32.1 and Statistics Addition = 33.9 are absolute Subjective Impression scores on Perplexity, compared with 24.7 for the no-optimization baseline. The paper separately reports a 32.1% average improvement for Statistics Addition in its combined-strategy analysis, so that percentage is real but it is not the Table 5 score. The 115.1% result belongs to Table 2: it is the improvement from Cite Sources for sources ranked fifth in search. These are benchmark results, not a promise that a hotel will receive the same lift. They do show why specific facts and citations are more useful than generic brand copy.

Hotel-specific research points in the same direction, within a narrow frame. The 2026 preprint The End of Rented Discovery audits 1,357 grounding citations from Google Gemini across 156 hotel queries in Tokyo. In that sample, experiential hotel queries drew 55.9% of their citations from non-OTA sources, compared with 30.8% for transactional queries. That is a 25.1 percentage-point difference. The finding says nothing about the authority of every non-OTA source, or about hotel discovery beyond that engine, city, and query set. It does support a practical conclusion: when the request is about atmosphere, context, or fit, a hotel needs discoverable evidence beyond a standardized inventory listing.

Five myths slowing hotel readiness

Before a connection goes live, settle the parts the hotel will have to own: the operating interface, the booking route, the evidence an agent can use, and the response time the guest journey actually needs. If nobody on the team owns one of those answers, the project is not ready for more tooling. Five assumptions tend to get in the way.

MCP is the only route

MCP can be useful in some agent architectures. Other routes include standard REST or GraphQL APIs, OpenAPI descriptions, a booking-engine integration, and an intermediary that normalizes tools for agents. See inHotel's AI Agent API, Tool and Connector for Hospitality^[1] for a hospitality-specific treatment of the options.

The useful question is operational: can an authorized agent retrieve and act on the hotel's live data through an interface the team can secure and operate? That matters more than choosing the protocol that is fashionable this month.

A website rebuild must come first

A visual redesign can improve the human guest experience. It does not automatically make a hotel legible to an external agent. Around the existing site, teams can add a richer property knowledge layer, synchronized partner profiles, accurate place data, an agent catalog entry, and a secure connection to live inventory. The website can evolve on its normal brand and commercial timetable.

Direct booking means sending the guest to the brand website

The direct route stays under hotel control. Direct does not mean website-only. An agent-mediated direct booking can happen without a browser session on the hotel domain. The guest's agent can discover a hotel through a hub, registry, catalog, or Agent Card. It can verify the hotel's identity and capabilities, ask for live availability, compare room-level fit, confirm policies, and send the reservation through an authorized endpoint connected to the hotel's booking engine, CRS, or PMS. The guest may see a clear confirmation inside the assistant while the hotel owns the relationship, rate logic, policy communication, and transaction.

The website still matters to guests who want to browse, research the brand, or complete a booking themselves. It is one channel among several. A hotel that publishes richer facts can also compete for natural-language requests where fit matters most, such as a particular atmosphere, neighborhood relationship, seasonal event, or room configuration.

OTAs will automatically own AI-driven travel

OTAs bring reach, inventory, established booking flows, and repeat demand. Those advantages matter, but they do not guarantee the best match for every natural-language request.

Most hotels are not operating sophisticated booking agents yet. An Aven Hospitality + Skift sponsored white paper reporting research from Aven Hospitality and h2c says only 11% of hotel organizations had deployed what its source defines as true AI agents capable of completing bookings, orchestrating loyalty programs, pricing inventory dynamically, and responding in real time. In that sponsored research context, the figure concerns AI deployed in hotel operations and guest-facing brand or digital channels. The study says nothing about whether those hotels expose an A2A Agent Card, discover another hotel's agent, or negotiate across agent systems. Agent-to-agent interoperability is newer and sits outside the study's measure. The other 89% had not reached the reported operational-AI stage.

Hotels can keep both OTA reach and a direct agentic route. Building the direct route now gives the team more control before an intermediary becomes the default path.

Agents need extreme response times

Let the guest question set the target. Hotel teams sometimes assume that an AI booking path requires trading-system latency and an enterprise-grade platform rebuild. Travel planning is usually asynchronous. An agent can compare options, ask follow-up questions, verify details, and wait for a reliable answer.

For a safe recommendation, completeness matters more than raw response time. Explain the room, policy, location, and current availability. Begin with completeness, deterministic rules, monitoring, and graceful fallbacks; improve speed where the guest journey shows a real delay.

The HotelQuEST benchmark tested 214 hotel-search queries and found that LLM agents could be more accurate than traditional retrievers, but incurred higher costs because of redundant tool calls and suboptimal routing. That points to a useful engineering target: reduce unnecessary calls while preserving the evidence needed for a good match.

A practical starting point for agent discoverability

Do not begin by trying to describe the whole hotel. Pick one property and one real question a traveler might ask. Follow that question through the public listings, the website, the map record, and the booking systems. The gaps usually show up quickly; a platform audit alone can hide them.

Do that check before commissioning a wider audit. Most teams can do it in an hour with the tools they already have. It is mundane, but it exposes the problem quickly.

I would start there, not with a schema validator. Nobody needs a perfect ontology for this first test. The boring answer is often the useful one: two systems disagree about the same room.

The public footprint comes first

Check the hotel's Google Business Profile, other business-profile systems, OTA entries, Tripadvisor, destination directories, local press, map records, and partner sites. That includes destination hubs, travel catalogs, local place graphs, partner networks, and agent registries where they are relevant. If the name, address, category, descriptions, policies, or links disagree, fix that first. An agent sees separate mentions and has to infer that they refer to one place. You want those mentions to reinforce each other.

Use the same underlying property facts everywhere, then let each channel present them in its own way. Independent citations help with that: a local article, a trusted directory, or a consistent profile can confirm facts that the hotel's own site asserts.

It is unglamorous work. It is also what lets an agent join the dots.

Once the property has one recognizable identity, ground it in the physical world. Overture Maps' Global Entity Reference System provides stable GERS IDs for geospatial entities and is designed to help organizations join their own data to shared map data across releases. The GERS documentation explains how those identifiers support consistent entity references. For a hotel, this can mean confirming the right property record, entrance, building relationship, nearby places, and travel context. See Overture Maps' article on moving from discovery to action for the travel-industry context.

Describe the stay in terms a traveler can use

Start with the details a front desk colleague would check before making a promise: room layout and accessibility, whether the Wi-Fi is good enough for a video call, how noise varies by room, where guests can eat or work, and which policies or offers change over time.

Suppose someone asks for a quiet hotel in Lisbon with a proper desk and an easy walk to the old tram routes. The answer may depend on the room's position in the building, a tested connection, the street outside, and the route from the entrance. A “quiet” amenity field cannot capture all of that. That is the level of detail an agent needs if it is going to recommend a property with confidence.

Here is the part generic CMS advice tends to miss: schema markup belongs in this same modeling exercise. To capture AI search intent, a hotel site may need a connected, multi-tier JSON-LD graph linking the property as @type: Hotel to individual room types as @type: HotelRoom, an amenity-feature layer, and dynamic @type: Offer data for dates, occupancy, prices, restrictions, cancellation, and availability. In Schema.org, those amenity details are commonly represented through amenityFeature and LocationFeatureSpecification nodes.

The practical task is to connect the property's claims to the features a room or stay actually includes. Standard CMS templates and basic schema setups do not usually emit or maintain this nested entity mapping natively. The graph needs stable identifiers, the same facts found in external profiles, versioned content, and a way to stay in sync with the booking and content systems. This is why hotel teams often bring in a hospitality SEO or structured-data specialist. The job is entity modeling and synchronization, not simply adding a Schema.org snippet.

Give an agent somewhere to ask

Once the public identity and stay description are reliable, give a guest agent somewhere to ask. An A2A Agent Card describes the hotel's endpoint, capabilities, skills, and authentication requirements. The A2A discovery guidance covers well-known URLs, registries, catalogs, and direct configuration. Agentic Resource Discovery (ARD) adds a complementary way to publish machine-readable resource catalogs, including the /.well-known/ai-catalog.json convention.

Keep public descriptions separate from protected actions. A public resource can explain that the hotel supports availability lookup; authentication should decide who can request guest-specific data or create a reservation. The content behind that capability should describe the audience, experience, feeling, and fit of the stay, alongside the facts that make those ideas credible.

Then connect the answer to hotel systems

The live part comes next. Connect one useful capability, such as availability questions, to the booking engine or the CRS/PMS. Validate property identity, dates, occupancy, rate conditions, cancellation terms, and payment or guarantee requirements. Log actions, enforce scopes, protect credentials, and send ambiguous or high-risk cases to hotel staff.

Try the whole journey with a real question from a guest. If the answer cannot distinguish one room from another, explain a restriction, confirm an opening time, or reflect a current rate, fix the missing fact or stale connection before adding more copy. The best first test is usually small enough for a hotel team to follow from source to answer.

What direct agentic distribution changes for hotels

The payoff shows up in matching first. A traveler who wants a quiet, design-led, work-friendly hotel close to transit can be matched against facts that standard catalogs rarely capture. The hotel can explain its own experience, update its own policies, and decide which capabilities are public or protected. When a guest agent already understands the request, the conversation can move from intent to availability without making the traveler navigate several pages and forms.

Room features need evidence. When the data is incomplete, the safer response is a staff handoff. Put live data, clear ownership, access control, monitoring, and human review into the design from the beginning.

Start with the question a guest asks, then trace the answer back to the system that owns it.

A hotel can be direct before a guest sees a website

The next generation of hotel discovery will combine search, websites, OTAs, and agent catalogs into several routes through which a traveler's agent finds, evaluates, trusts, and contacts a property.

If a hotel prepares only the visual front door, part of that journey will remain with intermediaries. Publishing reliable property intelligence across the web, connecting it to the physical world, exposing a secure direct capability, and keeping a human escalation path lets the hotel meet guests through the agents they already use.

Start with one property. Map the facts an agent would need to recommend it. Identify the gaps between the hotel's own description, partner listings, map data, and live booking systems. Then connect one high-value capability, such as availability lookup or reservations questions, and measure where the agent still needs human help.

The website can keep doing its job for people; the hotel also needs a way to be found when no one opens it.

For a practical next step, read inHotel's recent guidance on usage policies for autonomous hospitality AI agents^[1]. It is a useful reminder that agent access needs clear capabilities, boundaries, and operational ownership.

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Disclosure: GoVisit.ai is an inHotel product, and inHotel sells the agent-discovery and agent-connection capabilities discussed in this article. The inHotel links above are self-citations included for product-specific context; readers should evaluate those recommendations alongside the independent sources cited throughout the article. ↩ ↩ ↩