How Hotels Use AI in Food and Beverage Operations

Hotels now use AI in most departments. The Mews Hotelier Survey 2026, which polled more than 500 properties between December 2025 and March 2026, found that 98% had used AI somewhere in their operations in the previous six months, with use spread across the front office, commercial teams, executive functions and food and beverage. Most of the attention goes to guest messaging, pricing and booking. Food and beverage gets less of it, even though it is the department where a hotel buys, prepares and discards a physical product every day.

In hotel F&B, AI is mostly used for four jobs: forecasting how much food each outlet, buffet and banquet will need, planning production against that forecast, watching food quality and freshness during service, and showing chefs and F&B directors where food cost is being lost. This article covers each one, what independent research has found in hotels, and what a property needs in place before any of it works.

Forecasting demand for outlets, buffets and banquets

A hotel kitchen plans for several kinds of demand at once. The breakfast buffet follows occupancy and the mix of guests in house. Restaurants and room service follow their own patterns. Banquets are planned from the event order and a guaranteed headcount, which often differs from how many people attend and how much they eat. Most kitchens still plan all of this from the forecast sheet, last year's numbers and the chef's judgment, then add a margin so nothing runs out.

AI forecasting sets that quantity from what guests ate before. The models take in past consumption by menu item, day of week, occupancy, weather, events on property and where a dish sits in the menu cycle, and return a suggested quantity for each item at each meal period. The forecast is only as good as the data behind it. One built on sales or covers knows how many people came. One built on measured consumption also knows how much of each dish they ate, which is the number a buffet or banquet kitchen plans from.

A pilot run by the World Wildlife Fund and the American Hotel & Lodging Association shows the same idea done by hand. In 2017, with funding from The Rockefeller Foundation, the two groups ran 12-week food waste projects at 10 hotels and published the results as the Hotel Kitchen toolkit. At Hyatt Regency Orlando, WWF staff audited a large buffet lunch, and the kitchen then lowered its standard production rate by 25% per event. The team also started using what attendees of a multi-day meeting ordered on the first day to plan the days that followed. Forecasting software applies that logic to every item at every service.

Planning production and watching food during service

A forecast is useful once it reaches the prep sheet. In hotel kitchens that use AI for production planning, the forecast becomes a quantity for each item at each meal period, along with batch sizes and a point at which to cook the next pan. This matters most on buffets and banquets, where food is cooked ahead and set out before anyone knows how much will be taken. Cooking in smaller batches and replenishing against what guests are taking is the standard advice for buffet production planning, and it is hard to do without a live count of what has left the line.

Buffets and banquets are also where hotel food waste concentrates, according to the Hotel Food Waste Report published in March 2026 by the Cornell Center for Hospitality Research. Its authors, from the sustainability firm Greenview and WWF, worked from the 2025 Cornell Hotel Sustainability Benchmarking Index, which collects environmental data from more than 31,500 hotels and resorts. Among U.S. properties with usable food waste data, resorts reported a median of 0.38 kg of food waste per occupied room against 0.22 kg at non-resort hotels, about 75% more. Full-service hotels reported about 2.2 times as much as limited-service hotels. The authors link both gaps to buffet service, banquets and guests eating more of their meals on property.

During service, AI is used to watch the food itself. Systems record how long each pan has been on the line and at what temperature, and alert staff when an item is close to its holding limit, so it can be replaced before quality drops. The same record shows which items are still nearly full late in a meal period, which tells the kitchen to stop replenishing them. Metafoodx calls this a Freshness Alert.

Finding where food cost is lost, and what guests thought

After service, the question for an F&B director is which items cost the most to overproduce. AI tools answer it by comparing what was produced, what was eaten and what was left for every menu item, then ranking the gaps by cost. A short weekly list of the most expensive overproduced items gives the executive chef something specific to change, and the same consumption record gives purchasing a better basis for orders than last month's invoices. For a hotel group, it also allows a like-for-like comparison of the same breakfast buffet or banquet menu across properties.

Banquets need their own record. Each event has its own menu, headcount and client, so a kitchen learns the most when production and leftovers are stored against the event order and can be pulled up the next time a similar group books. Metafoodx built Catering Mode for this, with each event tracked by its BEO number.

AI is also used to read what comes back from the dining room. Food left on plates shows which dishes guests did not finish, and when that is paired with a quick rating at the point of return, the kitchen can tell a portion problem from a recipe problem without sending another survey.

What the research shows so far

The research on hotels supports measurement more clearly than it supports AI on its own. The most cited study is The Business Case for Reducing Food Loss and Waste: Hotels, prepared for the Champions 12.3 coalition, which reviewed cost and savings data from 42 hotels in 15 countries. Those hotels cut kitchen food waste by an average of 21% by weight within a year and saved $7 for every $1 invested. The study covered food waste programs of all kinds, including manual measurement and staff training, and did not isolate AI. We go through it in more detail in What the Research Shows About Hotel Food and Beverage Cost Control.

Two newer findings explain why results vary. The Cornell report found that only 6% of the hotels in its benchmarking index submitted any food waste data, so most properties have no baseline to forecast from. And in the AI Opportunity Study 2026 from the research firm h2c, which gathered 122 responses from 113 hotel chains, 91% of chains said they use AI but only 13% reported a measurable return from it. The barriers they named most often were a lack of AI skills (56%) and integration with existing systems (38%).

One hotel example from our own customers: Mayan Princess Beach & Dive Resort reduced kitchen overproduction by 23%, from 27% to 21% across its buffet stations, and reported an 820% return in six months.

What a hotel needs in place first

AI in the kitchen depends on a few ordinary things being true. Menus and recipes need to live in a system that can be read by other software, because a forecast by menu item is only possible when the items are named the same way every day. The kitchen needs several weeks of its own production and consumption data before a forecast means anything, which is the baseline most hotels in the Cornell data do not have. Someone, usually the executive chef or F&B director, needs to own a weekly review where the numbers turn into changes on the prep sheet.

It also helps to start small. The breakfast buffet is a practical first outlet, because it runs every day and the menu repeats, so the data builds up quickly. Banquets and other outlets can follow once the team trusts the numbers. Integration is the step to ask vendors about early, since the h2c study found it was one of the two barriers hotel chains ran into most.

How Metafoodx supports hotel F&B teams

Metafoodx is a food operations intelligence platform used in hotels and resorts and other commercial kitchens. Chefs often cook for the busiest possible day instead of the likely one. Metafoodx shows what each outlet produces and what guests eat, so buffet and banquet plans start from real data.

In most hotels, restaurants, banquets, room service and staff dining draw from the same kitchen, and a standard food cost report does not separate food served from food thrown away. Metafoodx tracks buffets, restaurants and banquets separately instead of relying on one blended food cost figure. Its AI forecasts each menu item from consumption history, day of week, weather and special events, and shows the prep cost of each item so chefs can trim the plan before service. Prep is based on what guests ate the last time the menu appeared, adjusted for the expected guest count.

During service, Live View shows when an item is running low, so the next pan goes out when it is needed, and timers and Freshness Alerts flag food that has been held too long. After service, a weekly report ranks the highest-cost overproduced items for each venue, so the team can review production, consumption, leftovers, run-outs and guest feedback together before changing the plan.

Several parts of the platform were built with hospitality in mind. Catering Mode tracks each banquet by BEO number and keeps a searchable event history. The mobile app extends tracking to pool bars, satellite kitchens and temporary stations. Global View shows multiple properties in one dashboard. Menus are pulled from Jamix, Parsley and NetMenu by Illumia, so the culinary team does not maintain a second item list.

Metafoodx is a food operations intelligence platform that captures the food journey from prep to plate, so foodservice teams can lower food cost, produce more accurately, and prevent waste before it happens. Ready to see how we can help transform your food service operation? Book a demo with our team today.