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

AI in F&B is moving beyond the demonstration stage

AI in F&B is now being viewed in more practical terms: helping industry professionals make decisions rather than simply creating a technology showcase. At Future Menus 2026, artificial intelligence was incorporated into the process of developing and operating menus, with the aim of narrowing the gap between creative inspiration and practical implementation.

According to information from Future Menus 2026, this tool is being applied to the same challenges that restaurants, cafés and ice cream brands have to address every day: understanding customers, controlling costs, adapting to trends and turning ideas into activities that can be put into operation.

What matters is not the number of food ideas AI can generate, but how well each idea suits 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 speed at which the market is changing. The 2025 Vietnam F&B market report recorded a slowdown in the industry’s expansion, while businesses still had to control costs and maintain real value for customers.

The Horeca School report was produced using data from F&B businesses, in-depth interviews and surveys of restaurant and café owners. The document also states 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 volume by time slot, customer feedback, the cancellation rate for bookings or ingredient consumption levels. However, AI can only provide effective support when the input data is sufficiently clear and users have identified the business question that needs answering.

What decisions can AI support in F&B?

From food inspiration to a menu that can be implemented

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 assessed by the chef and operator against specific criteria:

• Whether the target customers are genuinely suited to the new dish.
• Whether the ingredients can be sourced consistently and maintain uniform quality.
• Whether the team has sufficient skills, equipment and time to prepare it.
• Whether the dish fits the outlet’s price point, setting and positioning.

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

Reviewing feedback and identifying recurring issues

In F&B community discussions, many restaurant owners are interested in repetitive 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 a person.

This approach is useful when feedback comes from multiple channels. Instead of reading each comment in isolation, managers can use a tool to group frequently occurring topics 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 areas to check, but it should not make decisions on behalf of the operations team.

Especially when food is involved, the final decision also relates to quality, storage conditions, suppliers and safety requirements. According to FAO, FAO’s scientific advice, together with 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 have to start with a large-scale 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 the time required or improve the quality of decisions.

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

Limitations that cannot be overlooked

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

For decisions related to food safety, AI is even less suitable as the final approval authority. 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 WHO information.

Implementation perspective: AI should be viewed as a layer that supports analysis and information organisation, not as a replacement for the chef, shop manager or quality control department.

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 save time or clarify a decision.

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

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

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