Agilysys, Inc. (Nasdaq: AGYS), a leading global provider of next-generation hospitality software and services, today announced that its Book with S.P.E.N.D. (Stay Package Entitlement New Dynamic) booking technology has received the 2025 HTNG TechOvation Award, one of most respected honors for technology advancement in the hospitality industry.
The American Hotel & Lodging Association (AHLA) and Hospitality Technology Next Generation (HTNG) today announced the recipients of the 2025 HTNG TechOvation and AHLA Tech Acceleration Awards – two prestigious technology awards recognizing excellence and innovation in hospitality technology.
Online personalisation is easy, as it's mostly a pattern-matching algorithm between known guest likes and available services, and the results are pumped out via emails or into a guest portal. It isn’t personalisation, rather it is just mass customisation.
We all talk about personalization, but I worry that we lack a common and agreed definition of the term - a definition that is crucial if we are to deliver on the personalization promise.
The problem isn't that personalization fails on property. The problem starts much earlier: we still don't speak the right language of guest experience in the digital space. Take booking. A traveler may want a big bed, a walk-in shower, and a quiet location.
Online personalisation is easy, as it's mostly a pattern-matching algorithm between known guest preferences and available services. It isn't personalisation, rather just mass customisation. On property, a guest walking around is essentially anonymous, even after checking in.
This is a question at the heart of hospitality’s digital transformation. Hotels excel at digital personalisation (curated offers, smooth booking, and tailored communications) because online, data is structured, accessible, and actionable.
Mews, the leading operating system for hospitality, today announced the opening of a new commercial hub in Dallas, Texas. The hub will house a portion of Mews’ inside sales team, ranging from entry level Sales Development Representatives (SDRs) and Business Development Representatives (BDRs) to inside sales reps, and is designed to support Mews’ fast expanding U.S. operations.
The debate over proprietary vs. open AI is a symptom of a deeper issue: whether hospitality is willing to collaborate to truly move forward together. At Alliants, the path is open—not about building walled gardens. Distrust of tech vendors has pushed many large hotel brands to build in-house, often because people weren"t listening to one another openly. Innovation rarely happens in a closed room.
When it comes to where our Distribution landscape is headed - it's not even a question. Of course uniting our efforts is the only way to go. Otherwise we'll lose another OTA battle. But there's much more to the world of AI functionalities, it has its tenticles in every part of our tech ecosystem, not distribution alone.
I think we are in danger of running before we can walk here. MCP is Anthropic’s pitch for an open standard to let "agentic AI" bots built on next-word LLMs share data, for example, an OpenAI bot talking to a Gemini bot. Unless you are Marriott or Hilton with hundreds of in-house developers, or a big OTA like Expedia or Booking.com, this really isn’t something you need to worry about.
You could establish your own protocols instead of MCP, A2A, or other widely endorsed standards for agentic interactions. But that would be like creating your own version of HTTP. Not a great idea. The good news is I don't think companies feel the need to change that basic agent-friendly plumbing.
Overall, there is no difference between preparing a dataset for AI and preparing a dataset for the myriads of other tech tools and business uses. By AI here, we mean Generative (Gen) AI-next-best-word, probability & correlation-based tools. Traditional analytical tools need normalised data (third normal form etc.) using Structured Query Language (SQL) with potential ambiguities removed in the design. Gen AI is useful to analyse text documents which are, by definition, unstructured, but it still acts as a glorified "word cloud" linking items together by frequency and probability. The main hospitality systems - PMS, POS, Spa, RMS, CRS, CRM - were built on normalized databases precisely to prevent mismatches, duplication, and gaps. Even then, duplicate guest profiles remain common, eg when a frequent guest changes email or surname. The better PMSes use fuzzy matching to ease de-duplication, but this remains a governance issue.
It so happens I have a deep background on this topic. Over almost a decade, I deployed enterprise-level customer data consolidation for many of the world's leading luxury brands. In this capacity, developing the methodology and guiding execution of this specific desired outcome scores of times. The process always begins with the standardization of data and the rollout of those standards, followed by the cleanup of information sets once the standards are in place. Only after that point is true consolidation a reality.
I believe priority #1 for independent hoteliers, midsize and smaller hotel brands is to create true two-way APIs among three crucial technology pieces: PMS-CRS-CRM. This is the only way to prepare the property for the agentic AI, expected to take over hotel bookings, guest relationships and personalization over the next years.
I’ve probably been asked about a hundred times in the last two years to list the “main” use cases of AI in our industry. And while I understand what drives this request (we’re all just trying to wrap our heads around AI while drinking from the firehose of unlimited possibilities it presents), I’m not a big fan of boxing people in with a static list of examples.
There is really no difference between preparing data for Gen AI and for other tech tools: structured data must be normalised, deduplicated, and governed, while Gen AI is best applied to unstructured sources like reviews, transcripts, or free-text notes.
AI in hospitality cannot be reduced to a fixed list of use cases, because the technology and its applications evolve too quickly and vary too widely across companies. Each organisation runs on its own mix of operations, tech stacks, data flows, and structures, which means their AI opportunities will always be unique to their context and business challenges.
Revinate, hospitality’s leading direct booking platform, today announced the appointment of Ankur Bhandari as Chief Financial Officer. Bhandari brings over two decades of finance and operational leadership experience from Fortune 500 companies and leading private equity-backed firms across SaaS, healthcare, and financial services.
This is part of a bigger question, I think. As a marketing guy, let me explain it through distribution, using Booking.com as the example. Booking spends billions on advertising, dominates search, builds the user experience that has become the industry template, and runs a multilingual customer service that often outperforms independent hotels.
Yes. It's not limited to franchises and is a pretty long standing clause in agreements. There largely because it was not as prominent a consideration back in the day from a data and technology perspective - harder to get data out of a peice of on premise technology that the owner paid for.
Having spent over 25 years at the intersection of hospitality operations and technology—most recently as Founder of EIP MGT GmbH, a vendor-neutral consultancy—I have seen the conversation around guest data shift from “what can we collect?” to “how do we use it to create real value?” The promise of a centralised, real-time guest profile is compelling, but only if it is leveraged to enhance—not replace—the human touch that defines our industry.
RestAssure, the industry’s first non-insurance guest protection platform, has recently launched–and is looking for partners in the industry exactly like yourself. We are a transformative solution built to empower hotel owners nationwide to take back control of operational costs and protect guests in real-time, and restore profitability and peace of mind in an evolving hospitality landscape.
In the past, legacy technologies in hospitality used to be closed systems, reluctant to open up to third-party integrations, applications and solutions. This type of technology prevented property and its guests from adopting some very innovative and much-needed applications and services.
We are already witnessing the emergence of the Personal AI Agents. ChatGPT Operator, Google Gemini AI Agent, Microsoft Copilot AI Agent, Claude AI Agent, etc. are already a fact and their travel research, planning and booking capabilities are growing by the minute.
In my writings over the past decade, I have frequently returned to the notion that artificial intelligence is not merely an additional layer in the technological stack, but rather a reconfiguration of the Internet's ontological architecture. It redefines not only how we book, but also how we come to know, how we choose, and ultimately how we experience. AI is not simply the new interface, it is the new epistemology.