{"id":434,"date":"2026-10-14T08:00:00","date_gmt":"2026-10-14T07:00:00","guid":{"rendered":"https:\/\/www.cardonet.co.uk\/insights\/?p=434"},"modified":"2026-10-08T11:28:49","modified_gmt":"2026-10-08T10:28:49","slug":"ai-hospitality-hotels-restaurants-real-gains-hype","status":"publish","type":"post","link":"https:\/\/www.cardonet.co.uk\/insights\/ai-hospitality-hotels-restaurants-real-gains-hype\/","title":{"rendered":"AI in hospitality: how to tell real operational gains from hype"},"content":{"rendered":"\n

Much of the AI that hotels and restaurants are buying in 2026 will be hard to judge, because few businesses set a baseline, a named owner and clean data before they start.\u00a0Put those three in place first and you can measure the return. Without them, you are paying for a subscription on trust.<\/strong><\/p>\n\n\n\n

AI use is running ahead of governance<\/h2>\n\n\n\n

The Office for National Statistics (ONS) found that\u00a0around 35% of UK businesses with 10 or more people<\/a>\u00a0report using at least one AI technology. The average adopter uses around 1.6 tools, and only 10% of adopters use AI extensively.<\/p>\n\n\n\n

Individual use is much higher. The same ONS analysis finds that 55% of workers in Great Britain use AI for work or education, and it lists accommodation and food services among the industries reaching AI mostly through free software rather than a paid, managed tool.<\/p>\n\n\n\n

Research commissioned by Microsoft<\/a>\u00a0and run by Censuswide in October 2025 found that, of 2,003 UK workers surveyed across sectors, 71% had used an unapproved consumer AI tool at work and 51% still did so every week. Only 32% were concerned about the privacy of the company or customer data they put into it.<\/p>\n\n\n\n

Why that makes spending hard to judge<\/h2>\n\n\n\n

Taken together, those figures suggest AI use in hospitality is running well ahead of the rules around it. Most of that use has no baseline, no named owner reading the result and no record of what data went where, so nobody can say whether the tool made anything better.<\/p>\n\n\n\n

There is a security cost too.\u00a0IBM’s Cost of a Data Breach Report 2025<\/a>, based on 600 breaches worldwide, found that breaches involving unsanctioned AI use added around $670,000 USD to the average cost. They exposed customers’ personal data in 65% of cases, against 53% across all breaches. The study is global and covers many sectors, although a hotel holding payment and identity data is the kind of business those figures describe.<\/p>\n\n\n\n

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Where guest data goes<\/h2>\n\n\n\n

A consumer AI tool takes whatever someone pastes into it. In a hotel that can be a booking export, or a complaint reply carrying a guest’s name and room number, going into a personal account under consumer terms with no log you can read afterwards. Your property management system (PMS) and point of sale (POS) permissions stop at the edge of each application, so they never see it happen. Where guest records go after they leave the PMS belongs in your\u00a0General Data Protection Regulation (GDPR)<\/a>\u00a0compliance review.<\/p>\n\n\n\n

What the best published evidence shows<\/h2>\n\n\n\n

The strongest support for this argument comes from a\u00a0reinforcement-learning pricing system tested at a budget hotel chain<\/a>, reported in the peer-reviewed Journal of Operations Management. It is one of the few published tests of an AI pricing tool run against a control group, rather than a supplier’s own before-and-after comparison. The system read booking and cancellation data and recommended discounts, which were turned into room allocations across guest segments.<\/p>\n\n\n\n

Measured against a synthetic control across a pilot of five hotels in Shanghai, revenue per available room rose by 11.80% on average, which the researchers found statistically significant. Average daily rate and occupancy also rose, although neither was significant on its own.<\/p>\n\n\n\n

Why the results varied by hotel<\/h2>\n\n\n\n

One hotel gained mainly through occupancy and another mainly through room rate, and the size of the gain differed from hotel to hotel. The researchers trace that spread to differences in each hotel’s demand pattern and decision-making, since the algorithm was identical at all five sites.<\/p>\n\n\n\n

The pilot ran in 2015 at a Chinese budget chain, so it is old, outside the UK and outside the segment most of our hotel clients operate in.\u00a0It is still the best published evidence I know of. It suggests that a tool given a baseline, a control group and live data can produce a measurable gain, and that the size of the gain depends on how each hotel acts on its recommendations.<\/p>\n\n\n\n

Start with a baseline<\/h2>\n\n\n\n

If you cannot say what a task costs you today, in a unit you already track, a tool has nothing to be measured against. Our guide to\u00a0measuring AI return<\/a>\u00a0sets out one way to write that baseline down before you spend anything.<\/p>\n\n\n\n

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Who answers for what a chatbot says<\/h2>\n\n\n\n

Skipping ownership also creates legal exposure. In 2024, a Canadian tribunal\u00a0held an airline responsible<\/a>\u00a0for what its website chatbot told a customer about bereavement fares, and rejected the argument that the chatbot had made its own, separate statement. The ruling comes from a small-claims tribunal in British Columbia and does not bind UK courts, although its reasoning is easy to follow: a business answers for what its own website says, whoever built the model behind it.<\/p>\n\n\n\n

A booking assistant that misquotes your cancellation policy falls under the same reasoning, and so does one that tells a guest with a nut allergy that a dish is safe when it isn’t. In both cases, nobody was assigned to check the output before a guest saw it.<\/p>\n\n\n\n

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Joined-up data comes before AI<\/h2>\n\n\n\n

Tools sold on a single view of the guest run into a similar gap. Guest history sits in one system and spend in another, and each holds a different idea of who the guest is. A tool asked to reconcile those records on the fly will return confident answers built on mismatches nobody has caught, because matching the records is integration work.<\/p>\n\n\n\n

Our\u00a0hotel IT support<\/a>\u00a0and\u00a0restaurant IT support<\/a>\u00a0teams do that work directly, getting your PMS and POS to exchange data as designed\u00a0before anything AI-branded goes live on top.<\/p>\n\n\n\n

Five questions to ask before you sign<\/h2>\n\n\n\n

Before you buy, a supplier should be able to help you answer these five questions. If they can’t, the tool isn’t ready to buy.<\/p>\n\n\n\n