Technology

If you are a revenue manager, your morning routine is a good place to start. Every revenue manager has one that sets up the week ahead: On Monday you open the usual reports and check how the hotel is pacing, how pickup compares with last year, where the forecast sits against budget and how your rates look against the market. Immediately you can see the problem, and the opportunity.

Improve guest communication What goes in: Ask the AI to read your entire website, your concierge mailbox (for tone of voice and context), and supplement it with additional information that only lives onsite (like your Wi-Fi password).

What goes in:The reservations or sales inbox, plus live rates and availability from the PMS and the hotel's own meeting-room PDF and website.What comes out:For every group or meeting request that arrived after the front desk closed, a drafted quote sits in the reservations inbox.

Yes, the existing piping is not ideal. Remember, a bunch of hotels in Europe still use Booking.com as their booking engine, because there's no piping at all (no booking engine of their own, or their PMS is premise-based and lacks APIs).

Yes, outdated ARI plumbing is a huge immediate obstacle, but it isn't the only one.Legacy hotel systems rely on slow, cached data and one-way connections that frequently crash when AI agents try to make real-time bookings. However, even if the technology is fixed, bigger business problems remain: hotels fear losing customer relationships and revenue margins to AI intermediaries, and payment liability for AI errors remains unclear.In short, while the technical pipes are broken, the deeper issue is a lack of trust, clear business models, and secure payment systems for autonomous bookings.

The main impediment isn’t tech plumbing; it is inherent in the concept of “agentic AI” itself. It is based on Gen AI LLMs, which are probabilistic, next-best-word engines. They have no “real-world” models, eg understanding higher / lower, before / after, logic, distance, numbers. They mimic very well. Super for wordsmithing and unstructured data / document analysis, poor for most everything else.

Having a human at the helm, not just in the loop, is key to setting AI agents' goals, limits, and escalation triggers, and then to being able to watch the audit trail for anomalies.First, delegate commercial tasks that already operate on written rules, reserving human judgment for those that don't.These tasks go first because the limits are already written.

Hotels have increasingly adopted AI for data analysis and recommendations, yet decision-making and execution remain largely manual. The next leap is moving from AI-powered insights to autonomous action, with humans focusing on governance, strategy and exceptions.In Revenue Management, agents can monitor demand and adjust rates, inventory and restrictions autonomously.

We've seen that significant revenue opportunities, both from groups and leisure guests, exist inside reservation teams' Outlook inboxes, but they often lack the time and capacity to systematically manage them.I think one of the first things hotels should delegate to AI agents is managing these opportunities from the initial inquiry all the way to a booking, although that is very hard to do correctly and responsibly.AI agents should be able to understand guest requests, check availability and rates, prepare relevant offers, and proactively follow up if the guest, travel agent, or whoever made the inquiry doesn't respond.

Over the years, I’ve seen that the most successful technology partnerships rarely start with technology. They start with a clear understanding that change is needed and a shared vision for where the organization wants to go. The hotel groups that get the greatest value from technology are usually those that are willing to challenge the status quo and bring the right people together to make informed decisions.Equally important is leadership.

This resonates deeply after decades in hospitality technology. The strongest outcomes come from genuine partnerships—not merely contracts.As a P&L owner, I’ve seen that distinction from both sides. Some clients generate meaningful revenue but create a disproportionate drain on resources or dominate a roadmap in ways that do not serve the broader customer community.

In my experience running a boutique technology firm, choosing the right client matters as much as the client choosing the right vendor. Our scarcest resource is not scale. It is attention.The clients we go the extra mile for are clear about the problem they are solving, give us the real decision-makers, decide with discipline, and treat the relationship as a partnership rather than a transaction.

Hoteliers have told us for years that they have huge amounts of data, but in separate systems. The core issue is that each tool runs on its own data set and doesn't play nice with others. Your PMS is working with one set of numbers and your channel manager with another.That's partly because connecting these systems is nobody's job.

The general malaise that the industry maintains when it comes to doing the hard work of technology and data structure.This is the one initiative that is almost always pushed down the road. Largely because people hope someone else will do it.

I spoke about this topic on HEDNA's stage in Lisbon a couple of weeks back, on how AI is redrawing hotel distribution. Knowing that 79% of what AI knows about a hotel comes from OTAs, metasearch, and editorial sources — and not from the hotel itself, which only accounts for 7.5% — I believe we can agree that AI is filling the gap with third-party data.

A good website/ digital marketing, a decent loyalty offer, and a solid booking engine are essential for all hotels to keep a meaningful share of guest relationships. The role of the OTA should be to bring new guests to a hotel.

The debate suffers from oversimplification. First-party data is strictly contextual to the operational stage being executed—no single entity holds complete ownership.Digital Discovery & Booking: OTAs capture top-of-funnel acquisition data for indirect channels.

It is neither myth nor luxury; it is the precondition for controlling a hotel's own commercial destiny.The "myth" argument conflates two failures. Yes, most hoteliers have underinvested in direct channels for decades, and that failure lies with the industry, not the OTAs.

After years of sitting on the buying side of these conversations, five things matter most to me. First, prove you understand hotel operations. Not in theory, but in how your solution performs at 2 a.m. when a system goes down and guests are waiting at the front desk.

Operational fit, clean integration, solid data protection, and measurable value across our properties and markets — that's the minimum expectation. Beyond those fundamentals, here's what, in my view, signposts the start of a genuine partnership: Show us the product.

From our perspective, before we seriously consider a technology vendor, we expect the vendor to demonstrate the following: Clear understanding of hotel operations and the business problem being solved.The solution must address a defined operational, commercial, guest-experience, or compliance challenge.

Fix your data first. Compset, segmentation, rate mapping, etc. AI is only as good as the data behind it and most "AI failed" stories are really mapping stories. The bottleneck moves from analysis to decisions. When analysis takes minutes instead of days, the question becomes who decides, how fast, and which old meeting you can delete.

I think we're making the same mistake we make with every technological evolution (again, NOT revolution): we OVERestimate what AI will do in the next six months and UNDERestimate what it will do over the next six years. Today, I don't think there's a single killer application that every hotel should rush to deploy.

There's no shortage of AI noise, but most of it won’t tell you where to start. Here’s what I’d advise someone tackling it for the first time: Start with the problem, not the technology - The wrong question to start with is "how can we use AI?" The right one is "where does my team spend time on things that shouldn't need a human?” Repetitive guest messaging, manual reporting, rate updates, back-office admin.

The reality is much more complex than black and white. There are already many humanless hotels (mostly in Asia). So there will be: - regional differences in approach to human vs machine balance - variations between different segments (short-term rentals adopted self-service from the get go, many limited service hotel properties followed, but we won't expect to see if in the luxury segment any time soon, maybe never) It will be a spectrum.

In the future, a purely automated, no-human operational model will increasingly become the way of things. For this to fully materialize, our industry and our customer will have accepted all forms of artificial intelligence—both soft and hard.

No. Humanless hotels will become more common, but they are unlikely to become the norm.Automation and AI are excellent for repetitive tasks like check-in, payments, and guest requests, helping hotels reduce costs and improve efficiency.

United States will never adopt the same ruling. It's a capitalist country where businesses needs (not the government) dictate business actions. Smart operators will implement automation where it makes sense and where it augments "human touch".

The classic philosophy in hospitality is that guests require services provided by super nice, smiling, well-trained humans. I believe this philosophy is greatly exaggerated, especially today. A great example of why guests do not care about human-provided services comes from the vacation rental sector.Today, 20% of roomnights are consumed at vacation rentals/ short-term rentals: houses, villas, condos and apartments.

So far I haven't personally seen AI actually cut jobs. I'm not saying it hasn't but I haven't personally seen it yet. It has however empowered people to do a whole lot more, so it might slow down future hires. But I haven't seen it replace anyone that was hired, unless the person utterly refused to learn how to use AI.

Bold prediction: The PMS isn't going anywhere by 2030 — but it will look nothing like it does today. It becomes the heart of the hotel, connecting and coordinating most AI tools, agents and decisions running across the property.

The death of the PMS was mooted back in 2014 when I joined hospitality from airlines. 14 years later, we still have PMSes. Their primary all-in-one functions were:- Handle check-in/out- Manage room key cutting- Take payments- Set up the hotel room inventory and types- Assign a price per room- Link to selling channels like OTAs- Manage housekeeping- Manage facilities management/maintenance- Handle on-site eventsSeparate RMSes now do pricing and push rates to PMSes.

The Enterprise PMS will still be around in 2030. Every hotel needs a system of record to manage reservations, inventory, rates, guest profiles, financial transactions, and operational controls. AI doesn't remove that requirement.

Generally, more people with access to AI will not mean better solutions, it will mean more noise in the market, more fragmented and badly deployed technology - just like the last decades, but at a bigger scale, and the people who suffer are hotel employees and guests. I do believe that to stand out from the pack and deliver something truly remarkable, you need genuine innovators to bring all these solutions together in a coherent ecosystem that is open, collaborative, and truly end-to-end in how we serve guests. The best technology partners will build closer partnerships at the backend layer, while hoteliers can build complete AI workflows on the frontend.

By HITEC 2030, AI will not have eaten established hospitality tech. It will have eaten the repetitive workflow layer around it. Hotels will still rely on PMS, CRS, RMS, payments, distribution, and other systems of record. Those systems are too operationally critical to disappear quickly.

2030 is a long time from now, especially in AI-years, which are only about 60 days long!If I think about the current trajectories and strategies of the bigger companies that will lead this movement, I see larger travel enterprises implementing foundational agentic backbones like Frontier from OpenAI or Claude Enterprise from Anthropic.

The AI platforms need to monetize their traffic, since AI computing is exceptionally expensive! The quickest way to do so is to partner with well-established travel brands and to start earning affiliate commissions from them. To achieve that, AI connectors play a crucial role, but what kind of connectors?The keyword here is “invisible B2B connectors” that service well established hotel and travel brands, but remain invisible in the background.Ex.

The question lands precisely where the industry tends to look away. Travellers do not wake up wanting orchestration. They want outcomes: the right option, at the right price, with minimal friction and maximum trust. The moment a connector introduces one extra decision — which system to link, whether it is safe, whether it will work — adoption collapses outside a narrow power-user segment.Where connectors genuinely solve a problem is on the supply and enterprise side.

At least in their current form, I think AI connectors risk solving more of an industry problem than a real user need.The idea of a unified journey makes sense, but it breaks when it assumes that travelers will actively connect tools, authorize systems, and manage infrastructure they’re not familiar with.

Specific to the thrust of Max's question, in this context we are referring to search/find from the booking/transaction perspective. In that light...For me it is the next generation of search.Depending upon the user, we can now be far more specific with the input to get more targeted output.We will be able to speak and converse, which is more natural and a next gen over multiple queries or filters.I do believe that when the buying time comes after the research phase the customer will still 'come' to the point of booking.

The Internet enabled you to find things yourself. Agentic AI will do everything for you. That’s why it’s: Much bigger than blockchain or the metaverse. And closer to how big the Internet was.

“When your grandmother can use a new technology, that’s when you know it will reach widespread adoption.” I’m paraphrasing this Kevin O’Leary here but the principle is nevertheless highly instructive for why blockchain and metaverse technologies didn’t have prominent adoption in the travel sector.

Guest preferences for automation vary significantly by region. Business heavy markets in Europe and North America lean toward automation, while the Gulf and Far East tend to favor human interaction, partly driven by lower trust in technology.Regardless of hotel tier, certain roles will always remain in human hands, e.g., chefs, spa therapists, maintenance engineers, duty managers, and any function centred on safety, emotional intelligence, or complex judgment.

Replace - very junior level "support" roles who mainly answer "how do I do this" type of questions. AI agents will be trained on SOPs and do this. Help desk staff can be reduced or upskilled for more complex work.Reshape - Contact Center Agents: As above, simple questions can be answered by AI Agents, and an AI powered PBX will answer the phone for high volume restaurants and make the bookings on a phone call via integration to table booking system, and send confirmation by SMS or WhatsApp.

AI won’t replace hospitality, but it will reduce the number of people needed to run it.In the next 3 years, we’ll see 30–60% productivity gains across call centers, guest messaging, revenue management, distribution, loyalty, back office, and even parts of development.This is not just automation, it’s a redefinition of roles.

OpenAI just admitted what Google already learned with "Book on Google" back in 2022: people use AI to discover, not to buy. I fully agree with Simone the reason is structural, not technical. A hotel booking is not a single transaction.

Conversational platforms will complete bookings, but not by simplifying hospitality's complexity. They will succeed when they can orchestrate it. Today a single reservation touches PMS, CRS, channel management, payment processing, cancellation logic etc.

I’d agree. A retail transaction, even a high value one, is much simpler than a hotel or flight booking. Shopify is a top retail IBE, but its funnel is much simpler (view products, select product, pick colour, select size SMLXL, add to basket, add an upsell box of chocs, set delivery address, etc), than the well-converting IBEs in hospitality like Journey, Net Affinity, P3, Profit Room, Allora etc.

Optimising AI literacy in F&B operations starts with a simple principle: buying technology is not the same as building capability.Too many operators still approach AI as a procurement exercise rather than an operational transformation discipline.

AI investments in F&B deliver value only when adoption is treated as seriously as the technology. The implementation gap is rarely caused by weak tools. It is usually caused by weak translation between strategy, tech implementation and frontline execution.

AI literacy in F&B does not start with tools. It starts with understanding what the operation actually produces.Most teams I see try to adopt AI before they can even describe what their workflow does step by step – mostly for the lack of SOP.

In a word, no. Websites were designed to entice humans to view, discover, consider, buy. They are heavy with images, HTML, JavaScript, etc. to entice us in and encourage us to dwell. APIs were invented for M2M, machine to machine, transactions.

Agents will be everywhere, but behind the scenes. Booking agents are a short-term solution; using AI to simulate human behavior is like asking for a faster horse. Agents that behave as people, visiting websites and simulating human behavior (clicking on links, filling forms) are an incredible technological feat, but are a poor solution to most real-word problems: - Agents are very slow, because they have to replicate user paths built for humans- They fail often, because every website they visit is differentThe future will be MCP- and API-based, with agents interacting with those MCP/API.

Crystal ball time. 5 to 10 years i think we will quite similar to where we are today. the slow pace of change will not change and traditional tech stacks will still be around. lack of motivation to change, contracts and low investment will hinder the change.