How to Make Your Business Legible to AI Travel Agents

I keep seeing confident claims on LinkedIn about how AI will change travel discovery. Some are plausible. Others do not match what we are seeing as we build GoVisit.

We do not yet have enough evidence to settle every question. But the gap between those claims and the systems we are actually working with made me want to look more carefully. I reviewed research from organizations whose methods and findings I could defend, then used it to test and refine my own view of how AI agents discover, evaluate, and clarify travel businesses.

That research led me to a practical question: when an agent describes your business, where did the description come from, and can you correct it?

An AI travel agent may encounter a business before a traveler begins a hotel search. The traveler may ask for a quiet place to stay, a restaurant near the venue, or an accessible afternoon activity. The assistant will assemble an answer from whatever it can find.

The answer is not that AI now controls travel. Current evidence is more useful than that headline. Travel decisions often form before the booking moment, AI is becoming a planning and discovery channel, and the quality of a business's public representation is becoming part of distribution.

An AI travel agent is a software system that interprets a traveler's request, searches or retrieves information, compares options, and can take actions such as monitoring, asking follow-up questions, or starting a booking. It may be built into ChatGPT, Gemini, Claude, Perplexity, Google AI Mode, or a travel platform; it is not simply a chatbot that summarizes pages.

When does a travel decision begin?

Accommodation booking is a visible transaction, so it attracts most of the measurement and optimization effort. But a traveler first decides what kind of trip to take, which destination fits, and what they want to do there. Booking confirms a choice that may have been forming for days or weeks.

Research presented by Travel South Dakota places destination consideration before accommodation booking in the traveler journey. That is destination-sponsored research, so it should not be generalized to every market. The strategic point still holds: a hotel is often competing for attention before a traveler has searched for a room.

The same sequence applies across the visitor economy. A traveler may choose a concert before choosing a nearby hotel. They may decide on a neighborhood before looking for a restaurant. They may ask for a family activity and only then discover the attraction, route, or local service that makes the afternoon work.

GoVisit already describes the established version of this problem in its discovery-to-clarification model: public information helps an agent find and compare a business, while a clarification path can answer questions that a static page cannot.

What does current evidence show about AI travel agents?

The evidence summarized below is strongest on usefulness in planning, not autonomous purchasing. Amadeus and Opinium's Travel Dreams 2026 report surveyed 6,000 travelers across six markets in late 2025. Respondents reported AI as more useful for planning and inspiration than for booking. That is self-reported usefulness. It does not show that AI formed a shortlist, changed a reservation, or completed a booking.

Publication-date evidence makes the picture less tidy. Phocuswright's latest travel research reports that 56% of travelers used AI for planning, booking, or in-destination assistance for at least one trip in the past 12 months. That combines several stages and measures self-reported use; it is not a 56% booking rate. Expedia Group's AI Trust Gap, based on more than 5,700 adults in the U.S., U.K., and India, found that 68% preferred booking with a trusted travel brand over an AI chatbot or agent and 66% would not trust an AI assistant to book on their behalf. The survey was commissioned by Expedia and measures attitudes, not behavior, but it shows why adoption and transaction trust must remain separate questions.

Other studies add necessary friction to the story. A 2025 U.S. survey from Beach.com reported AI influence most often for attractions and stops, followed by restaurants and lodging. Its sample covered 1,003 Americans who take at least one trip each year, and its results measure reported influence and trust rather than observed behavior. The category pattern is still important: the opportunity may extend well beyond hotel inventory.

The Miles Partnership and Future Partners 2025 technology research summary provides a second qualification. Search-engine websites remained the leading destination-discovery channel in its U.S. data, while reported AI trip-planning use was 16.8% in April 2025. The dates and measures differ from Amadeus, so the results are not a clean contradiction. Together they show a mixed market: AI is growing, while search and other channels remain central.

One peer-reviewed experiment by Kang, Kim, Kim, and Olya offers a plausible reason the channel could matter. The researchers studied 578 participants and found that richer AI travel information was associated with trust, perceived value, and booking intention. That is a mechanism and an intention measure, not evidence of completed bookings in the wild.

One recent product development adds a different kind of evidence. On August 7, 2026, Google confirmed to Skift that it had started a limited U.S. test of agentic hotel booking in Search's AI Mode. Google's current help documentation says the feature is available in the U.S. and in English for signed-in users with a valid Google Pay method and U.S. address, and only for reservations completed with a booking partner. The documented flow lets a user select a property and room, confirm details, and pay through Google Pay; the merchant processes the reservation and handles changes. That is a live transaction path, but it remains a bounded test rather than evidence that Google controls every hotel shortlist or booking.

The honest conclusion is narrower and more durable: AI is becoming a meaningful input across travel planning, discovery, and, in limited cases, booking. It is still one channel among several, and trust, partner coverage, market availability, and source quality constrain what happens next.

This is a narrower question than whether a business should “do GEO.” The useful test is representational: which parts of an agent's description can a business measure, correct, and keep current, and which parts remain outside its control?

How do AI agents represent a travel business?

When a traveler asks an agent for a business, the agent has to construct a representation. It needs to decide which hotel, restaurant, attraction, or experience a source refers to; what that business offers; where it is; when it is available; and whether the facts are current enough to use.

Those facts rarely live in one place. A hotel may keep room details on its website, policies in its booking engine, location data in a map record, and event information in a social feed. A restaurant's opening hours may change before its directory listings do. An attraction may have the right address everywhere but outdated accessibility information.

The sources also have different technical shapes. A business might publish a schema.org LodgingBusiness description as JSON-LD, maintain its Google Business Profile, and keep place references consistent across Wikidata and OpenStreetMap. An OTA such as Booking.com or Expedia may hold another version of the same facts. For live clarification or actions, a team might expose tools through MCP or an A2A agent. These are different surfaces, not one universal optimization switch.

This is a reasoned risk, not a universal finding that every agent gets every business wrong. But fragmented information leaves an agent with an inference problem. It may fill a gap from a third-party page, combine facts from different dates, or omit a business because the relevant relationship is not machine-readable.

The issue is larger than visibility. A listing may mention “central location” without explaining the entrance, the walk, or the nearby route that matters to a particular traveler. A room may list Wi-Fi without saying whether the connection supports a workday. A restaurant may appear open without exposing holiday hours or dietary details.

What can a travel business control?

No travel business can dictate what every model will recommend. Ranking, retrieval, model behavior, and traveler preference remain outside the business's control.

What a business can control is the quality of the source it publishes and the route through which an agent can ask for clarification. That means making identity, location, relationships, services, policies, availability rules, and evidence available in a form that systems can retrieve and interpret.

The distinction matters:

• Model-output control would mean deciding what an agent says. A business does not have that control.
• Source-of-truth control means maintaining the facts, provenance, freshness, and correction path that an agent can consult. A business can build toward this.

An authoritative source is not just a longer marketing page. It connects the property or place to the facts that determine fit: who it serves, what it offers, how it relates to nearby places, which conditions apply, and when each claim was last checked.

For destinations, this is the same connected-information problem described in GoVisit's article on stale destination information. The destination website is one surface; the underlying network includes businesses, events, maps, transport, booking systems, and local partners.

What would a first-party AI-readable profile add?

GoVisit's public Places documentation provides one company example of a factual profile layer for specific businesses and places. This article uses that idea as a category example, not as a claim about current product coverage.

For any provider, the model addresses two separate jobs. The first is representation: publish the business's own facts with enough structure and context for travelers and agents to understand them. The second is clarification: give an authorized agent somewhere to ask when a published profile cannot answer a live or specific question.

If a team turns this model into a product, its owners should confirm which protocols and interfaces are supported, how freshness and provenance are exposed, what access controls apply, how corrections work, and which outcomes can be claimed. The research in this article is evidence for the market problem, not independent proof of any provider's efficacy.

How can you audit a business's AI-readiness?

Before adding another layer of promotional copy, a travel business can audit the representation an agent would need to make a safe recommendation.

1. 1

   Identity

   Check whether the canonical name, address, phone, website, and place identifiers are consistent across owned and partner surfaces.

2. 2

   Fit

   Check whether an agent can tell which travelers, occasions, constraints, and preferences the business suits.

3. 3

   Relationships

   Check whether nearby places, routes, venues, services, events, and packages connect to the business rather than remain isolated mentions.

4. 4

   Freshness

   Check whether the business can show when opening hours, policies, amenities, availability rules, or event details were checked.

5. 5

   Provenance

   Check whether each important claim has an accountable source and whether a team can correct it without chasing every copy.

6. 6

   Clarification

   Check whether an agent has a safe path to ask a question that static content cannot answer.

The checklist does not promise inclusion in every answer. It makes gaps easier to find and repair. Start with one high-value request, such as “Is this room suitable for a work trip next Tuesday?” or “What is open near the hotel after a late arrival?” Trace the answer back to its source. Then fix the missing relationship, stale fact, or unavailable clarification path.

The durable advantage is not controlling the agent's conclusion. It is making your own facts current, connected, and answerable before the agent has to guess.