Agentic AI vs. OTAs: The Future of Hotel Distribution

Why is travel distribution changing now?

Consider a traveler who does not want “a hotel in Lisbon.” They want somewhere quiet but not isolated, close enough to walk to dinner, comfortable for a late arrival, and suited to two days of focused work before a weekend of exploring.

That request contains feelings, constraints, trade-offs, and a destination. An online travel agency (OTA) can return a useful shortlist from standardized fields. A more capable travel agent will try to understand the person behind the query, compare many kinds of evidence, and decide which options fit the whole trip.

The traveler still has an intention; the interface interpreting it is changing.

How the travel distribution model is changing

The market is unsettled, but operators can already see the components taking shape. Google has announced hotel booking inside AI Mode through integrated travel partners, while the Agent2Agent (A2A) project describes machine-readable agent identity, capabilities, endpoints, and authentication. The standards and interfaces are arriving before the market has chosen its final shape.

What did OTAs solve for the travel industry?

OTAs did more than put hotels on a screen. They turned fragmented supply into a usable market.

They standardized descriptions, collected reviews, handled payments, supported cancellations, and gave travelers a familiar way to compare unfamiliar businesses. They also paid heavily to acquire demand. Booking Holdings reported $8.2 billion in marketing expenses for 2025, with performance marketing focused primarily on search engines, affiliates, metasearch, and social platforms. Expedia Group reported $3.9 billion in advertising expense in the same year. Those filings describe individual companies and different accounting categories, not the whole OTA market, but they show the scale of the centralized demand machine.

Three figures put the economics in view:

These figures are not a universal OTA take rate or a like-for-like comparison across companies. They show why centralized discovery can be efficient for travelers while remaining expensive for suppliers and platforms to sustain.

The traveler benefited from that efficiency. So did the supplier that needed reach. An OTA is still a valuable distribution partner when it brings qualified demand, transaction infrastructure, and support that a small operator cannot build alone.

The limitation is the centralized catalog itself: it is usually better at describing what exists than why a particular option fits a particular human.

Why does an agent change the economics of intent?

Travel planning already consumes substantial attention. Expedia’s Path to Purchase research, conducted with Luth Research across seven geographic markets and a digital tracking panel of more than 70,000 travelers, found that travelers viewed an average of 141 pages over 45 days before booking and spent 303 minutes with travel content. The U.S. figure was higher at 524 minutes. The exact behavior varies by market and traveler, but the basic problem is familiar: people gather many fragments before they feel ready to choose.

The planning behavior is shifting toward AI as well:

An agent can compress that work into a single comparison: it turns “I need calm, local, and easy” into questions for hotels, restaurants, activities, maps, and booking systems while retaining constraints a traveler would otherwise repeat. It can then compare a room’s workability, a property’s arrival policy, the walk to a neighborhood, and the timing of an event as one trip rather than as separate searches.

Behind a traveler’s search, the agent interprets those words, preferences, constraints, and context and decides what to search, compare, and do next.

Big Tech platforms are well positioned to capture that interpretation. As an ex-Googler, I am convinced Google is unusually well positioned to capture more of the intent around travel planning. The user context developed through Search, Gemini, Gmail, Drive, and Maps, combined with the move into AI Mode, gives Google a powerful starting position. Gemini Spark is an example of how an agent can combine connected personal context with real-world information.

The routing decision then moves downstream. A traveler’s agent will not always send the traveler to the OTA catalog that bought the click. It can evaluate the supplier’s public identity and route the traveler to an OTA or direct booking system. The destination depends on what the agent can verify, which actions it is authorized to take, and where the transaction should occur.

That is a personal conviction, not a disclosed Google roadmap. The public evidence supports an expanding AI travel surface and an emerging set of agent protocols. It does not yet prove how much demand will bypass OTAs, which suppliers will receive it, or how the economics will settle.

Will OTAs disappear from the agentic journey?

No. The more plausible outcome is a hybrid market in which OTAs remain important fulfillment layers while discovery becomes more distributed.

Google’s current hotel-booking announcement makes that point visible. The integrated partner list includes Booking.com, Expedia, Hotels.com, Priceline, Trip.com, major hotel groups, and others. Google also says the hotel or booking platform remains the merchant of record and handles customer service. That is an OTA-compatible design, even if the traveler begins with an agent rather than a traditional search results page.

The strategic question is who continues to own the relationship with intent.

In a centralized model, the supplier competes to be selected inside the OTA’s environment. In a hybrid agentic model, a traveler’s agent can evaluate the supplier’s public identity and choose between an OTA and a direct booking system. That choice follows the available evidence, the requested action, and the platform’s commercial terms.

OTAs remain one doorway, but they no longer need to be the only one.

What does a supplier need to expose to an agent?

A standard listing tells an agent what a business is. A useful profile also explains when it fits, what conditions apply, and where the traveler can go next.

For the property-level version of this work, see how to make a hotel discoverable to AI without rebuilding its website. The distribution question is broader, but the operating principle is the same: start with the guest’s question and trace the answer back to the business that owns it.

A hotel, restaurant, tour operator, or destination business should make four answers easy to find:

• Who is this place a good fit for, and what does the experience feel like in practical terms?
• Which conditions or constraints change the recommendation?
• What can the business reliably provide today, and what evidence supports that claim?
• Where should the traveler go next for booking, clarification, or human help?

The data behind those answers needs a few properties that ordinary listings often lack:

1. Identity: a stable connection between the profile, the real business, its location, and its official channels.
2. Semantic detail: descriptions that connect traveler intent to observable characteristics such as quietness, social energy, family fit, workability, accessibility, or atmosphere.
3. Conditions: the dates, restrictions, service boundaries, and exceptions that keep a true statement from becoming misleading.
4. Freshness and provenance: a way to know who supplied the information, when it was checked, and what supports it.
5. A safe handoff: a clear route to availability, booking, contact, clarification, or human assistance without exposing unauthorized actions.

These profile properties complement live price, availability, room, policy, payment, and fulfillment data; they do not replace those systems.

The decentralized model becomes operational when each supplier makes its part of the experience legible enough for an agent to use. The supplier does not need to own the traveler’s entire planning experience; it needs to publish the facts, meaning, conditions, and next action that belong to its part of the trip.

How can GoVisit create a direct path to the supplier?

The connection between agentic discovery and direct demand is a public information layer. It has to connect identity, experience fit, operating conditions, evidence, and the supplier’s official next step.

GoVisit is one implementation of that model: a public hub where guest AI agents can meet hotels, restaurants, tours, experiences, and hospitality businesses for insights and direct bookings. Its approach combines a data-rich knowledge graph with proactive, A2A-capable agents so the meanings travelers use when planning can connect to the suppliers that provide the experience.

An AI Business Profile is the entry point. Claiming establishes the business relationship; GoVisit verifies it where requested. Connecting the business website gives agents and other systems a visible path back to the official source. These are separate parts of maintaining an accurate public record, not a ranking trick.

The GoVisit claiming guidance explains that claim, verification, and website connection are separate parts of the setup. Keeping those boundaries clear helps an operator understand what the profile establishes and what still requires a separate booking or service integration.

For an operator, the path is simple:

1. 1

   Connect

   Link your official website with your AI Business Profile to increase your visibility to AI crawlers and agents and let them develop a comprehensive understanding of your company's offerings.

2. 2

   Enrich

   Take control of your public record by claiming your profile and enriching it with facts, conditions, experience fit, and nearby context. Enhance your website with your own AI agent.

3. 3

   Maintain

   Review claims as the experience, policies, and operating conditions change, with proactive support from your AI Admin Assistant. Provide evidence to build trust with travelers' agents.

The useful outcome is narrower: give a traveler’s agent enough trustworthy context to recognize the right business for the right person, then reach the right next step.

What should hospitality operators do first?

Start with three questions your best guests already ask:

• Where can I stay quietly but still walk to dinner?
• Which property works for a late arrival?
• What is a good base for a family that wants local food without a car?

Trace each answer back to its owner. Check which sources answer the questions, where they disagree, and what information is missing. Then link the official website and test the route from discovery to clarification or booking. Keep a human in the loop for actions that affect payment, refunds, exceptions, or guest safety.

No profile guarantees that an agent will recommend a business. Google’s structured-data guidance explicitly says that correct markup does not guarantee an appearance, and the same restraint applies to richer business profiles. Readiness is the practical benefit. When a system looks for a place, a fit, a claim, or a next action, the supplier has made useful information available in a form that can be checked and connected.

Run those prompts against your current profile, fix the first missing or stale answer, then claim the AI Business Profile that owns the record.

If that is the demand you want to be ready for, claim your AI Business Profile on GoVisit and begin building the public record an agent can understand.