How AI Agents Could Transform Your Next Travel Booking—and Threaten Big OTAs

AI is quietly gearing up to change the way millions of travelers book their trips—and it could shake up the giants of online travel agencies (OTAs) in the process. Sam Shank, co-founder and former CEO of HotelTonight, laid out this emerging disruption in a Good Morning Hospitality bonus episode recorded at the Skift Global Forum on October 20, 2026.

The core idea is simple: if travelers stop typing searches into OTA sites and instead ask an AI agent to plan, compare, and book for them, the economics of online travel distribution could change fast. That matters because the current system has rewarded the companies best positioned to buy attention. An agent-led system would reward the companies best positioned to deliver the right answer.

The Problem with Sponsored Search

For years, major OTAs like Booking.com and Expedia have thrived on a sponsored search model. They earn hefty commissions by bidding for prime placement when travelers search for hotels, flights, or packages. This model shapes what travelers see first, and often what they ultimately book.

Shank’s argument is that AI-driven booking agents could weaken that advantage. If a traveler no longer scrolls through pages of listings, ads, and filters, then paid placement becomes less powerful. The decision point moves from a search results page to an AI recommendation layer.

That is a meaningful threat to big OTAs because sponsored search is not just a marketing tactic; it is part of the business model. The more consumers rely on an agent to narrow choices, the less valuable it becomes to simply appear at the top of a list.

What AI Agents Actually Change

Imagine telling an AI agent your travel dates, budget, airline preferences, hotel standards, and trip priorities. Instead of returning a long list, the agent could assemble a short set of options, explain the tradeoffs, and potentially complete the booking.

That changes travel shopping in several ways:

- discovery becomes conversational rather than list-based - personalization becomes the default rather than an optional filter - comparison can happen across multiple providers in the background - payment and booking can become part of one continuous workflow

In that world, the traveler may care less about which OTA they used and more about whether the agent found a better fit, saved time, or avoided a bad choice.

Why This Could Be Better for Travelers

The consumer upside is obvious. Travel booking today often means opening multiple tabs, comparing cancellation policies, checking loyalty benefits, and trying to figure out whether the cheapest option is actually the best one. AI agents promise to compress that work.

If they work well, travelers could get:

- faster trip planning - recommendations tailored to real preferences - fewer irrelevant listings - clearer tradeoffs between price, convenience, and flexibility

That could be especially useful for travelers who do not enjoy the research process or who book under time pressure.

But there is also a less obvious benefit: AI agents could reduce the influence of whichever supplier or platform paid the most for visibility. In theory, that makes the booking process more merit-based.

Why the Shift Is Not Guaranteed

There are real caveats. An AI agent is only as good as its access to inventory, pricing, and booking rails. If it cannot see the full market, or if it cannot complete the transaction reliably, it becomes more of a planning assistant than a true booking replacement.

Trust is another hurdle. Travelers may accept AI help for inspiration, but handing over payment details and final booking authority is a bigger leap. A missed cancellation rule, a wrong room type, or a bad connection can quickly erase confidence.

There is also a conflict question: if agents are monetized through commissions, preferred partnerships, or paid placement of their own, they could recreate some of the same incentives that shaped OTA search pages. The interface may look smarter, but the business pressure would still exist underneath.

New Opportunities and Challenges

Shank also pointed to the upside for new entrants. If the old advantage was scale in traffic acquisition, the new advantage may be specialization.

That opens the door for niche AI agents built around specific traveler needs, such as:

- business travel with policy-aware booking - luxury travel with concierge-style curation - budget travel focused on total trip value - family travel optimized for convenience and flexibility

This is where new booking infrastructure becomes important. An agent that can recommend but not transact has limited power. An agent that can search, compare, explain, and book in real time is much more disruptive.

That means the next battleground may not just be consumer apps. It may also be the behind-the-scenes systems that connect hotels, airlines, and payment flows to AI tools.

What Established OTAs Can Still Do

This is not automatically a story of OTA collapse. Big incumbents still have major assets: brand recognition, supplier relationships, loyalty programs, customer service operations, and large pools of booking data.

Shank’s thesis creates pressure, but it also creates a path for adaptation. OTAs could respond by:

- building AI agents into their own platforms - using loyalty perks to keep customers inside their ecosystems - improving post-booking support, where trust matters most - leaning on exclusive inventory or packaging advantages

In other words, AI may not eliminate OTAs. It may force them to compete on something more durable than search placement.

Why the Timing Matters Now

The timing is notable because the consumer backdrop still supports experimentation in travel. The effective federal funds rate stood at 3.75 in the latest FRED data, while retail sales rose about 0.8% in September. At the same time, unemployment was 4.2.

That mix suggests a consumer economy that is still active, even with borrowing costs elevated. Travel companies therefore have an incentive to improve conversion and reduce friction while demand remains resilient.

There is also a tension in the broader macro picture. CPI rose to 336.298 in September from 334.131 in August, and consumer sentiment was 51.7 in August, down from 55.2 in July. That combination matters for travel booking behavior: people may still spend, but they are likely to care more about value, convenience, and confidence before they commit.

An AI agent that can save time and surface better-fit options becomes more attractive in exactly that kind of environment.

The Real Tradeoff for Travelers

The practical question is not whether AI can recommend a trip. It is whether travelers will prefer delegation over control.

Some people enjoy browsing and comparing. Others want a trusted tool to do the work. The likely near-term outcome is a hybrid model: travelers use AI to narrow the field, then verify the final choice themselves.

That would still be enough to pressure the OTA model, because even partial delegation reduces the value of traditional search-page merchandising.

What to Watch Next

The clearest watch point is whether AI tools move from trip inspiration into actual booking and payment. That is the line between a helpful assistant and a true distribution threat.

Also watch how incumbents respond. If Booking.com, Expedia, and similar platforms start emphasizing agent-like experiences, that will be a sign the threat is being taken seriously. If startups begin winning users through narrow, high-trust use cases, that will suggest the market is fragmenting in the way Shank described.

For travelers, the near-term test is practical: does an AI agent save enough time, money, or hassle to become the first stop for planning a trip? If the answer becomes yes, the balance of power in online travel could shift away from the platforms that dominate search today.

For those interested in the evolving landscape of travel and finance, comparing broker access and fees across platforms like eToro can offer insights into how technology is reshaping consumer choices across sectors.

Related reading

A useful background piece for this story is Best Brokers.

Readers who want the wider market context can also use What is CPI.

Sources

• GMH Bonus: Sam Shank on How AI Could Reshape Online Travel
• CPI — Consumer Price Index (FRED official data)
• PCE — Personal Consumption Expenditures Price Index (FRED official data)
• UNRATE — Unemployment Rate (FRED official data)
• PAYEMS — Nonfarm Payrolls (FRED official data)
• FEDFUNDS — Effective Federal Funds Rate (FRED official data)
• DGS10 — 10-Year Treasury Yield (FRED official data)
• DGS2 — 2-Year Treasury Yield (FRED official data)