In this article, you will not read about whether AI can replace chefs, but about how it can help with kitchen bottlenecks, while protecting your business identity and the heart of hospitality: humans.
The question of whether to use AI in restaurant kitchens is hardly up for debate anymore, because the hospitality industry is already experimenting with these tools, from forecasting and workflow optimization to menu development.
Projects such as the Restaurant “WOOHOO” in Dubai are catching the industry's attention as they are using a large-scale culinary language model, an AI chef called “Chef Aiman” that has been trained on decades of food science research, molecular composition data, and thousands of recipes from cooking traditions around the world, helping to design people's menus.
But can AI replace chefs?
The answer is no. It can't taste, smell, or work with dishes and staff like a chef normally would. Its purpose is not to replace the human element, but to complement it.
By making kitchens more efficient, it ultimately frees up time for creativity and can contribute to greater sustainability improvements, particularly through food waste management.
The growing visibility of projects like these helps explain why the debate often revolves around the idea or fear of robotic chefs replacing humans, because they make technological change so easy to imagine.
However, most real adoption looks very different. Projects involving AI in restaurant kitchens are still experimental, and most establishments prioritize investing in tools that improve forecasting, workflow planning, and consistency, which highlights the gap between perception and reality.
According to a survey by the Restaurant Association, 26% of operators use AI-related tools, primarily for marketing and administration, as well as for ordering systems and back-office functions. Food preparation, for now, remains the least affected area.
Everyone who has ever worked in a restaurant kitchen knows that the constant pressure is a reality: People operate under continuous pressure, and teams are expected to deliver consistent plates at speed while absorbing unpredictable demand, tightened margins, delivery delays, and uncompromising safety requirements.
A large share of that work happens away from the stove: tracking prep, managing stock, adjusting forecasts, and keeping operations steady during service. It is essential, but it rarely leaves space for creative focus.
This is where AI is currently strongest: processing operational data at a scale that kitchen teams cannot realistically manage manually. It focuses on areas where accuracy, repetition, and data processing are most critical, easing pressures on teams and helping to avoid last-minute shortages and service failures at peak times.
In more detail:
AI models using weather and meteorological data and restaurant sales to forecast daily demand at the menu-item level, increasing inventory and operational effectiveness.
Machine‑learning demand forecasting in restaurants (RNNs, Random Forest, XGBoost, etc.) improves sales prediction, supporting better staffing and stock decisions.
That said, operators' expectations for AI in restaurant kitchens vary, as they deal with different challenges. Professionals in casual dining restaurants, for example, are hoping to achieve an enhanced customer experience, whereas this is less of a priority in quick-service segments.
How AI delivers depends entirely on the circumstances under which it is being deployed.
In other words, its effectiveness depends less on the technology itself than on how well a kitchen is structured to support it: through clear processes, reliable data, and consistent execution. If kitchens aren't adopting these fundamentals before introducing AI, the technology will only reinforce inefficiencies rather than fix them.
Thomas Bissegger, program manager and lecturer at EHL Hotelfachschule Passug, also emphasizes the importance of distinguishing between tasks that should be fully automated, those that benefit from AI-assisted decision support, and those that must remain human-led. In many professional environments, the most effective use of AI is not replacement, but targeted collaboration. But where do you start?
One practical approach is to map kitchen operations to a priority matrix, categorizing tasks by repetition and required judgment to determine the right level of AI involvement: from Automate first to Human-led but AI-assisted, Decision support, or simply Keep human.
While artificial intelligence can support structured and repeatable operations, it reaches clear limits in areas where context, perception, and interpretation matter. This is why tasks that are variable, context-dependent, and require real-time judgment should stay human.
As Patrick Ogheard notes:
Delays, missing ingredients, equipment issues, or unexpected demand: usually, nothing unfolds exactly as planned in kitchen environments. This requires immediate adaptation. Human teams can improvise, re-prioritize, and find workable solutions in ways that are difficult to predefine or automate.
Menu creation is not purely a technical process. Food is a fundamental part of culture, and the different ways of preparing it are rooted in traditions around the world. Chefs create dishes based on intuition, cultural context, creativity, and personal perspective. These aspects, taken together, are shaping how a concept is perceived.
Not all situations fit a pattern, which is crucial for AI to work in restaurant kitchens.
Special requests, dietary constraints, or unexpected disruptions require human judgment that balances operational feasibility with guest satisfaction. These decisions often involve trade-offs that go beyond predefined rules and patterns.
In many concepts, the kitchen is part of the brand. The way food is prepared, presented, and adapted decides how guests experience the concept. This dimension of hospitality, where food carries meaning beyond function, remains closely tied to human presence and interpretation.
AI adoption in restaurant kitchens varies depending on operational complexity and challenges they face, for example: menu breadth, team size, and day-to-day demand volatility.
The most active adopters are casual dining restaurants that usually have a rather complex operational reality with their broader menus, larger teams, and stronger reliance on coordinated workflows. Here, AI is being applied across multiple applications, particularly for managing inventory and improving the customer experience.
Applebee’s and IHOP are two casual dining restaurants that use AI to streamline operations. Their AI-powered tech support system allows staff to interact with company databases by simply asking questions in a conversational manner. No more digging through manuals and documentation, which is intended to improve staff productivity and operational efficiency.
Quick-service, fast casual, and café segments are also actively investing in AI, prioritizing operational improvements such as efficiency, marketing, and loyalty.
The large restaurant chain “Yum! Brands,” for example, is currently advancing in AI Adoption with their own digital AI-driven restaurant technology platform, “Byte by Yum!”, helping reduce bottlenecks in high-volume restaurants.
Regarding AI in core kitchen execution, we are still at an early stage of the AI adoption timeline, as these systems are still limited and experimental, with less than half of respondents currently using or testing them.
And there is a recurring pattern: AI delivers the most immediate value in environments where operations are complex, data-rich, and require coordination at scale. Where kitchen work becomes less predictable and more dependent on real-time judgment, adoption tends to be more cautious.
The question any foodservice leader should ask starts with the word "Where":
Where in restaurant kitchens can AI bring the most value in creating meaningful, measurable operational value without compromising hospitality's identity?
The answer is to clearly separate operational bottlenecks, such as forecasting and managing inventory, from the parts of the kitchen that define the brand and guest experience, not the brand identity. This would lead to the use of AI that stabilizes the system around the chef, not replacing the role within it.
In practice, that means:
Automating where variability creates cost and complexity,
supporting decision-making where data improves outcomes,
and reserving human control where judgment, creativity, and brand expression define the experience.
So before beginning to consider investing in kitchen AI, ask yourself these three questions:
What are the tasks that are repetitive and measurable in my business?
Does reliable operational data already exist?
Does automating it affect my business identity or guest perception?
It is not about aiming for a fully automated kitchen, but a better-balanced one. One where technology handles what can be standardized, and people remain responsible for what cannot.
This matters because food is never purely functional.
When asking Patrick Ogheard, one of our foremost culinary craftsmen at EHL, about his favorite meal, he shared:
A reminder that cooking is not only about the final plate, but also about memory, emotion, and context.