Innovation

📑 In 2015, a group including David and Daria created a chart depicting hotel technology vendors. This chart aimed to visualize how data moves through the hotel industry, with the PMS at its center. Despite having over a thousand PMS solutions globally, the chart highlighted integration points and dependencies in hospitality tech. The author is proud of the chart's evolution, noting that the role of research is to organize information for others to draw conclusions. Hotel tech continues to grow, influenced by data flow.

🤖 2026, HITEC conference highlighted AI’s role in hospitality. Ajay Aluri and Scot Campbell discussed Nylo, a socially intelligent robot, aiding in guest interactions and repetitive tasks. Examples include Oto at Otonomus Hotel, Tulse at Marriott Tulsa, and José at San José Airport. Collectively, they completed 371,135 guest interactions and 22,657 conversations, supporting 70+ languages. Emphasis is on experience amplification, not job replacement, balancing ROI and ROE.

What happens when travelers stop searching with keywords and start asking AI for the “right” hotel? How will properties stay visible when discovery, distribution, and booking are increasingly shaped by conversational search? Inspired by a recent webinar, AI & Hospitality: How hotels get found in 2026 , this article explores what that shift means for hoteliers, and why Model Context Protocol (MCP) could play a role in the next phase of the booking journey. The front door to hotel discovery is changing For years, hotel discovery followed a familiar pattern. Travelers typed in a few keywords, scanned a list of results, compared ratings and prices, and eventually chose a booking path. That pattern is now beginning to shift. More travelers are turning to AI assistants and conversational tools to ask more specific questions that reflect intent, context, and personal preferences. Rather than browsing broadly, guests increasingly describe what they want and expect the system to interpret it. This shift matters because it changes the rules of visibility. Instead of competing only for broad phrases like “boutique hotel in Barcelona,” hotels can surface for requests such as a rooftop stay for a Sunday night, a family‑friendly property near a theme park, or a hotel with a distinctive experience that matches a guest’s travel style. Jason Cincotta summarized this shift simply: “The long tail is now mainstream.” Traditional search is not disappearing, but AI‑driven discovery creates new opportunities for hotels to appear in response to richer and more specific guest needs, rather than being grouped into broad, undifferentiated categories. Watch the full conversation: AI & Hospitality: How hotels get found in 2026 What LLMs really mean for hoteliers Large language models sit behind the conversational tools many travelers already use, including ChatGPT and Gemini. For hoteliers, the technical mechanics matter less than the practical outcome: these systems increasingly influence which properties are mentioned, recommended, or excluded before a guest ever clicks on a website. In this environment, AI visibility becomes the next evolution of search visibility. Discovery is shifting away from ranked links and towards curated answers, where structured and trusted information is essential to being surfaced at all. As Jason noted during the discussion, LLMs are already approaching the point where they can “name hotels and the website of that hotel without ever using a web search.” That does not mean traditional search behavior disappears overnight, but it does make AI discoverability an immediate distribution consideration. AI visibility is not separate from search strategy. It is the next stage of it. The same discipline that once focused on crawlers and ranking signals now needs to account for machine‑readable content, accurate descriptions, availability, amenities, policies, and the details that make a property worth recommending. If a hotel cannot be clearly understood by an AI system, it becomes far harder to surface in an AI‑driven journey. MCP is more than a buzzword One of the most important concepts discussed in the webinar was Model Context Protocol, or MCP. At a practical level, MCP

🌐 Cendyn has launched Wayfinder, a GEO analytics and LLM monitoring platform, in Austin, Texas, to help hoteliers maintain visibility across AI models like ChatGPT and Perplexity. Wayfinder detects content drift and analyzes hotel web content, enhancing search visibility with a GEO health score. It assists in direct website performance and competitor analysis. Kevin Duncan and Nicki Graham of Cendyn emphasize the importance of AI accuracy and brand control for market visibility and revenue protection.