利用MCP、酒店内容和人工智能工具最大限度地提高可见性和直接预订

S T R AT E G I C B R I E F Leveraging MCP,Hotel Contentand AI Tools toMaximize Visibilityand Direct Bookings VERSION 2|PUBLISHED: JUNE 8, 2026 This document was developed by the Global Technology 100. AHLA/HTNG DISCLAIMER ANDANTITRUST REMINDER AHLA/HTNG DOES NOT PROVIDE LEGAL ADVICE OR REPRESENTATION TO ANYINDIVIDUAL PERSONS OR MEMBERS. TO THE EXTENT THIS RESOURCE CONTAINSINFORMATION ABOUT THE LAW THAT IS DESIGNED TO HELP THE RECIPIENTUNDERSTAND OR ADDRESS HIS OR HER OWN LEGAL NEEDS, SUCH INFORMATIONDOES NOT CONSTITUTE LEGAL ADVICE OR REPRESENTATION BY AHLA/HTNG. EACHINDIVIDUAL AND BUSINESS IS RESPONSIBLE FOR ASSESSING, UNDERSTANDING, ANDPREPARING FOR USING AI TOOLS AS THEY DEEM APPROPRIATE. MEMBERS SEEKINGSPECIFIC LEGAL ADVICE WILL FIND ADDITIONAL RESOURCES AT WWW.AHLA.COM/ALLIEDATTORNEY. AHLA/HTNG DO NOT FACILITATE AND MEMBERS SHOULD NOTDISCUSS OR AGREE ON ROOM RATES, PRICE FORMULAS, DISCOUNTING PRACTICES,CAPACITY/INVENTORY DECISIONS, CHANNEL RESTRICTIONS, COMMISIONS, OR OTHERCOMPETITIVELY SENSITIVE TERMS. ALL COMMERCIAL DECISIONS REGARDING PRICING,AVAILABILITY, AND DISTRIBUTION MUST BE MADE INDEPENDENTLY BY EACH COMPANY. Guidelines to Success:Framing MCP and the Path Ahead As hotels prepare for the AI-driven future of search and booking, it’s important to take a balanced,strategic approach. The Model Context Protocol (MCP) is a promising development that may eventuallyenable direct, real-time connections between hotel systems and AI platforms. However, it’s still early —current implementations are limited, and key questions about how AI assistants route booking requests(direct vs. OTA) remain unresolved. The way travelers search, evaluate, and book hotels is rapidly changing. Large Language Models (LLMs)and AI-powered agents — like ChatGPT, Perplexity, and emerging digital assistants — are quickly becomingthe first stop for trip planning. This shift represents both a challenge and an opportunity for hoteliers. In this environment, success means staying informed, participating in industry dialogue, and preparingyour data and systems for integration, to remain nimble as the technological landscape changes. Bystrengthening your content, ensuring discoverability, and collaborating through groups like AHLA/HTNGto advocate for fair and transparent distribution practices, brands and hotels can help shape the nextphase of AI-driven bookings rather than simply react to it. AHLA/HTNG efforts are limited to technicalstandards, education, and lawful advocacy, and do not include commercial coordination. To stay competitive, hotels must take control of how their content, rates, and availability data arepresented to AI systems. This requires a dual strategy: 1. Open Access to Crawlers:Ensuring hotel websites can be crawled by reputable bots(e.g., Common Crawl, a nonprofit) so LLMs can learn from your content and include yourproperty in their “knowledge base” 2. Real-Time Data via MCP:Feeding accurate, live data directly from your systems into AI toolsso information is always up-to-date and reliable TAKE ACTION NOW:PLEASE ENSURE THAT YOUR TEAM WALKS THROUGH THE ACTIONCHECKLIST IN SECTION 8 TO OPTIMIZE YOUR HOTEL FOR AI-DRIVEN DIRECT BOOKINGS. Why Hotels Should Care Combined with strong content strategies and ARI (Availability, Rates, Inventory) optimization,this approach can help hotels protect visibility, drive direct bookings, and build stronger guestrelationships in the age of AI. AI AS THE NEW “FRONT DOOR”:Travelers increasingly ask AI assistants forrecommendations on stays and experiences within their budget parameters. CRAWLING SHAPES AWARENESS:If bots cannot access your site, LLMs may not“know” your property exists — or may rely on incomplete third-party data. LOSS OF CONTROL WITHOUT INTEGRATION:Without both crawling and directfeeds, hotels risk being misrepresented or overlooked. A PIVOTAL MOMENT Hotels that move quickly can shape their presence in AI-driven travel search before lessfavorable monetization options become entrenched. AI-Native Hospitality CommerceEcosystem Diagram The Distribution Architecture diagrams areintended to provide both a business andtechnicalframework for understanding howemerging AI-driven commerce and distributionmodels may interact with the hospitalityecosystem. The diagrams are designed tobe reviewed progressively. We recommendstarting with the simplified business view tounderstand the major participants, workflows,and relationships across the ecosystem, thenmoving to the technical view and associateddrill-downs, which provide additional detailfor each functional area. From a business perspective, the diagrams are important because they illustrate how agentic AIand emerging consumer AI platforms may fundamentally alter how travelers discover, evaluate,book, pay for, and experience hospitality products and services. The architecture highlights thepotential risks and opportunities associated with this shift. These include impacts to distributionreach, attributio