The OTAs Aren't Being Disintermediated by AI. They're Becoming the Infrastructure Under It.

Paper · Source
Agentic Commerce

Source: Rajeev Goswami, CEO of WWStay; WWStay subscriber brief (Substack) · 2026-08-31

A prospect asked me last week, in a discovery call for a project-housing deal, whether AI agents were going to make our OTA relationships irrelevant within two years. I gave the answer everyone in the industry gives right now: probably, eventually. Then I went and actually checked the 2026 data, and I don’t think that answer is right anymore.

Independent hotels’ RevPAR fell 5.4% in 2025 while their OTA dependence climbed to 63.4% of bookings — worse, not better, in the year AI was supposed to start fixing this. OpenAI’s quiet retreat from ChatGPT checkout in March turned out to be the tell: even when an AI agent books through Booking.com or Expedia’s API instead of their website, those platforms stay merchant of record and keep the commission. AI is removing the storefront, not the infrastructure underneath it.

I started digging into Expedia’s B2B numbers expecting a footnote and found the real story: their B2B segment — the part powering other companies’ booking tools, including TMC (travel management company) platforms your program may already sit inside — grew 24% year-over-year last quarter and is now 38% of total revenue. Branded chains’ OTA channel share has barely budged in a decade of “book direct” campaigns.

Nobody’s actually measuring whether an AI layer on a TMC platform changes the underlying OTA-supply relationship. The dashboards travel managers watch track booking behavior, not who’s still collecting the commission underneath it. I started asking which of my own client relationships run through exactly this kind of embedded OTA supply — and then I started worrying I already knew the answer.

If your OTA content sits inside your TMC’s platform today, is AI actually reducing that dependency — or just making it harder to see?

Lines of inquiry this paper opens 24

Research framings built by reading the notes related to this paper — the questions it feeds into.

Do AI shopping agents exhibit systematic bias in recommendations? How do AI-generated summaries affect traffic and engagement with original sources? How can merchant-funded agents remain loyal to consumer interests in digital commerce? How can AI agents earn consumer trust with financial decisions? How do inference costs, efficiency, and market competition shape AI adoption economics? How do confidence signals and expertise language mislead users' trust calibration? Should multi-agent systems use structured protocols or natural language communication?