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AI in F&B: from demonstration to supporting menu and operational decision-making

AI in F&B is now being viewed more pragmatically: not only generating dish ideas, but also helping businesses close the gap between creativity, data and execution.

AI in F&B is moving beyond the show

AI in F&B is being viewed more practically: as a way to help industry professionals make decisions rather than simply put on a display of technology. At Future Menus 2026, artificial intelligence was incorporated into the process of developing and managing menus, with the aim of narrowing the gap between creative inspiration and practical implementation.

According to information from Future Menus 2026, this tool is applied to the same challenges that restaurants, cafés and ice cream brands deal with every day: understanding customers, controlling costs, adapting to trends and turning ideas into workable operations.

The noteworthy point is not the number of food ideas AI can generate, but how well each idea fits the customers, ingredients, staff and business model.

Operational pressure makes data an important input

The F&B industry is under simultaneous pressure from ingredient, staffing and operating costs, as well as the pace of market change. The Vietnam F&B Market Report 2025 recorded a slowdown in the industry’s expansion, while businesses still had to control costs and maintain real value for customers.

The report by Horeca School was produced using data from F&B businesses, in-depth interviews and surveys of restaurant and café owners. It also stated that AI is used to collect and analyse market data. This shows that AI is not only appearing in marketing, but is also being incorporated into the process of reading and processing information.

For operators, data can come from revenue, sales by time period, customer feedback, booking cancellation rates or ingredient consumption. However, AI can only provide effective support when the input data is sufficiently clear and users have identified the business question that needs answering.

Which decisions can AI support in F&B?

From food inspiration to a workable menu

AI can support the early stages of menu development by suggesting combinations, grouping needs or organising information from multiple sources. Even so, the final idea still needs to be reviewed by chefs and operators against specific criteria:

A manager and chef reviewing data to decide on a menu — Effective use of AI still requires collaboration between managers and chefs.

Whether the target customers are genuinely suited to the new dish.
Whether the ingredients can be sourced consistently and with consistent quality.
Whether the team has the skills, equipment and time to prepare it.
Whether the dish suits the shop’s pricing, setting and positioning.

For an ice cream brand, the process could begin by analysing customer needs, then comparing them with the product range and service capabilities. Businesses can refer to Baby Boss Gelato Products or Gelato Knowledge to connect product ideas with specialist information and a suitable business model.

Reading feedback and identifying recurring issues

In F&B community discussions, many restaurant owners are interested in recurring tasks such as responding to reviews, handling messages, taking bookings and compiling customer feedback. AI can help classify information or suggest ways to respond, but messages sent to customers still need to be checked by people.

This approach is useful when feedback comes from multiple channels. Instead of reading each comment in isolation, managers can use a tool to group recurring themes and then identify the issues that should be prioritised for improvement.

Supporting stock control and purchasing plans

Stock control, purchasing and demand forecasting are often challenges with clear operational value. AI can help identify trends in sales data or provide suggestions for further checks, but it should not make decisions on behalf of the operations team.

This is particularly important for food, where the final decision also involves quality, storage conditions, suppliers and safety requirements. According to the FAO, scientific advice from the FAO and WHO provides a foundation for developing international food safety standards, guidelines and codes.

From AI tools to decision-making processes

F&B businesses do not necessarily need to begin with a large system. A more cautious approach is to choose a measurable issue, such as compiling customer feedback or analysing sales by product group. The business can then assess whether the AI tool helps reduce time or improve the quality of decisions.

Challenge	What AI can support	What people still need to decide
Developing new dishes	Suggesting combinations and compiling demand data	Flavour, quality, selling price and service capability
Customer feedback	Classifying themes and drafting suggested replies	Tone, context and how to handle each case
Stock control	Identifying consumption trends from available data	Checking quality, supply and storage conditions
Menu operations	Comparing sales data with the dish range	Deciding whether to retain, adjust or discontinue a dish

People who are opening a new business often ask which tool they should invest in first. The more practical answer is to start with the data already available, identify a specific bottleneck and establish evaluation criteria before rolling it out more widely.

A dashboard tracking sales, customer feedback and ingredients — Organising sales, feedback and ingredient information is a foundation before using AI tools.

Limitations that cannot be overlooked

AI can process information quickly, but it does not automatically understand the full context of a shop. Missing, incorrect or outdated data can lead to unsuitable suggestions. A clear analysis table cannot replace tasting, checking ingredients and observing customers’ actual reactions.

For decisions relating to food safety, AI is even less suitable as the final source of approval. The FAO and WHO emphasise the role of scientific advice, risk assessment and food control systems. Businesses should therefore cross-check information against regulations, standards and suitably qualified professionals. The international food safety standards developed by the Codex Alimentarius Commission are also an important reference in this field, according to information from the WHO.

Implementation perspective: AI should be viewed as a layer for analysing and organising information, not as a replacement for the chef, shop manager or quality control team.

Where should restaurants and ice cream brands start?

For a small shop, the first step could be to standardise how revenue, feedback, sales volumes and ingredient status are recorded. Once the data is structured, it becomes easier for the business to assess which AI tools genuinely help save time or clarify a decision.

An ice cream brand developing its business model can combine data analysis with product training, preparation procedures and shop model design. Content such as the F&B Business Guide and Gelato Ice Cream Setup Consultancy can provide a reference point for placing technology within the right process, rather than adopting it simply as a trend.

AI in F&B is therefore shifting from a story designed to attract attention to a question of effectiveness: does the tool help the business understand customers better, make decisions more quickly and operate more consistently? The answer depends on the data, processes and control capabilities of each business model.