If a travel-booking tool gets an AI assistant bolted on, does that actually shift who supplies the hotel and flight deals, or just the screen you click through?
Can AI layers on TMC platforms change underlying OTA supply relationships?
This explores whether adding AI assistants or agents on top of travel management company (TMC) platforms, the corporate booking tools businesses use, can change how those platforms get their hotel and flight inventory from online travel agencies (OTAs) like Expedia and Booking, or whether the AI just sits on top of existing supply deals.
This explores whether putting an AI layer on a corporate travel platform changes where its inventory comes from and who holds the power in those supply deals, or whether it only changes the interface. The corpus has nothing on TMCs specifically. It does have strong material on OTAs and on agent-mediated markets, and that material points to a clear default: the AI layer changes discovery much more readily than it changes supply.
The main evidence is that AI agents are pulling OTAs deeper into travel bookings rather than cutting them out. Are AI agents actually removing online travel agencies from bookings? reports that independent hotels' dependence on OTAs rose to 63.4% even as agents arrived. OTAs keep their merchant-of-record role, meaning they take the payment and carry the transaction, by connecting through APIs. One detail matters a lot for TMCs: Expedia's B2B segment grew 24% year over year and now makes up 38% of its revenue. That B2B channel is exactly how many intermediaries, TMCs included, get their hotel inventory. Are AI agents actually disintermediating online travel platforms? explains why. AI makes search cheaper, but payment, supply contracts, trust and completing the transaction all stay with the OTA. A TMC that adds a conversational booking assistant is therefore most likely sending more volume through the same OTA pipes.
The corpus does suggest some ways the relationship could change, mostly through bargaining power rather than replacing suppliers. Could agent-to-agent communication reshape market power? argues that agents redistribute market power by lowering switching costs. If a TMC's AI can compare several supply sources at no extra cost, sticking with one OTA becomes harder to justify, and the TMC gains leverage when terms are negotiated. Do AI agents actually benefit from network effects? makes a related point: when agents talk through standard APIs, nothing locks a buyer into one platform. Could agents redirect merchant ad budgets toward customer savings? adds a twist. Once agents rather than people do the shopping, suppliers have reason to move money from sponsored placement into better rates. Hotels might then offer net-rate concessions straight to a TMC's agent instead of paying for visibility on an OTA.
Whether that leverage turns into real change depends on who controls access. Will agentic commerce platforms adopt open or closed models? expects agentic commerce to split between open systems that admit any agent and closed systems that require their own, much as payment networks did. If the big OTAs go closed, a TMC's AI has to work through their terms. Could federal AI agent registries solve platform access unfairness? shows that regulators are already considering requiring large platforms to admit third-party agents, which would change that calculation. One caution: Could autonomous pricing agents dampen competition without explicit collusion? warns that when many pricing agents interact in a concentrated market, prices can drift toward tacit coordination. More agents on both sides of corporate travel doesn't automatically mean better rates.
There's also a practical point. Are enterprises abandoning custom AI apps for third-party solutions? finds enterprises moving away from building their own AI tools and toward buying them. TMCs may follow, licensing AI layers instead of building them, and those vendors could become a new middle layer with their own supply relationships. Overall, the corpus suggests an AI layer is unlikely to replace a TMC's OTA supply, but it may weaken any single OTA's hold on that supply. The bigger fight may be over who controls access to the agent, not who controls the inventory. Coverage of corporate travel specifically is thin, so treat this as an inference from the consumer and OTA evidence.
Sources 9 notes
Goswami's 2026 travel data shows independent hotels' OTA dependence rose to 63.4% even as AI agents emerged, with OTAs retaining merchant-of-record status through API integrations. Expedia's B2B segment grew 24% year-over-year, now 38% of revenue.
Expedia, Booking, and Airbnb posted strong 2026 gains despite disintermediation warnings. Prieto argues AI may cheapen search but leaves payment, supply, trust, and transaction completion intact—functions OTAs control. This implies value may shift rather than evaporate.
Microsoft researchers argue the deeper economic impact of agentic AI lies not in task automation but in reducing communication frictions between consumers and businesses. This shift could expand consumer options and reorganize markets, though the paper provides conceptual rather than empirical support for these claims.
Chen argues that because agents can communicate via standard APIs and swap underlying LLMs without losing continuity, they function as tools rather than networks. Unlike messaging platforms, agents face no pressure to converge on a single system.
Jaipuria argues that once agents replace human shoppers, merchants have incentive to convert ad budgets into lower prices and better terms rather than sponsored listings, since agents optimize for value rather than page position. Google's Direct Offers pilot demonstrates this shift in action.
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Waller argues agentic commerce platforms will divide between open systems allowing multiple agents and closed systems requiring proprietary ones, mirroring payment network structure. This choice will shape market concentration, vertical integration, and standards adoption.
Senator Warner's discussion draft would require large platforms to admit AI agents, establish an FTC registry of trusted agents, impose best-interest duties on agents, and direct NIST to set technical standards. The package aims to prevent platforms from blocking competing agents while protecting user privacy and ensuring agent accountability.
The CMA argues that when multiple businesses deploy autonomous agents optimizing pricing and commercial strategy in concentrated markets, interaction between these systems could reduce competitive pressure through learned mutual adaptation rather than explicit agreement.
a16z's 2025 survey of 100 enterprise CIOs found a marked reversal toward purchasing third-party AI apps over the past year. Enterprise leaders cite constant model optimization demands and high maintenance burden as reasons internally built tools no longer deliver business advantage.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- AI Agents Want to Shop for You: The Future of Agentic Commerce
- What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce
- Vertical tacit collusion in AI-mediated markets
- The Agentic Economy
- Magentic Marketplace: An Open-Source Environment for Studying Agentic Markets
- The Agent Economy: Who's Most at Risk?
- The OTAs Aren't Being Disintermediated by AI. They're Becoming the Infrastructure Under It.
- Agentic AI and consumers