"China Food Service AI Application Research Report 2026": Food Service AI Accelerates Transformation, Driving Industry "Intelligent" Leap

Ask AI · How Can Restaurant AI Help Restaurant Companies Cut Costs and Increase Efficiency?

To gain a deeper understanding of the current state of AI in China’s restaurant industry, the Hongcan Industry Research Institute, together with the Hongcan Growth Society, released the “China Restaurant AI Application Research Report 2026.”

Currently, China’s restaurant industry has entered a new phase of competition based on existing market share. Core costs such as labor, ingredients, and rent continue to rise, making cost reduction and efficiency improvement the central development issue for the industry. The rapid iteration and deep application of artificial intelligence technology provide key support for the restaurant sector to break through efficiency bottlenecks and respond to homogeneous competition—AI is shifting from an optional choice to a required one for restaurant companies.

So what is the current state of restaurant AI in China? What development trends are emerging? What opportunities and challenges will future development face? To this end, the Hongcan Industry Research Institute, together with the Hongcan Growth Society, released the “China Restaurant AI Application Research Report 2026.”

From March 24 to 26, 2026, the 2026 China Restaurant Industry Expo and the 35th HCC Global Restaurant Industry Expo, hosted by the World Federation of Chinese Catering (World Chinese Catering Federation) and the Hongcan website, was held grandly at the Hangzhou Convention and Exhibition Center. On March 26, Li Bang, Chief AI Officer of Hongcan and co-founder of the Hongcan Growth Society, delivered a speech and shared insights on the report on-site. The following shows part of the report’s content.

△ Li Bang, Chief AI Officer of Hongcan and co-founder of the Hongcan Growth Society

1. Restaurant AI is in a phase of accelerated development, and leading restaurant companies play an important role in推动ing AI application implementation

In recent years, after experiencing rapid growth, the restaurant industry has gradually entered a stage of competition over existing market share. According to Qichacha data, in 2025, the number of restaurant-related companies registered nationwide was 2.4 million, down 14% year over year, but the stock of companies is still over 16 million.

At the same time, core costs such as labor, ingredients, and rent continue to climb, squeezing profit margins. For example, in terms of labor costs, according to Boss Zhipin, in Q4 2025, the average monthly compensation for chefs and service staff in the restaurant industry reached RMB 6,777 and RMB 4,884 respectively, up 6.1% and 1.6% year over year.

Against this backdrop, cost reduction and efficiency improvement has become a core issue for the survival and development of restaurant companies. From upstream and downstream supply chain links to downstream restaurant brands, everyone is pushing forward with real-world applications of AI large models, which has also given rise to many new technologies and new strategies.

Among them, the rapid maturation of AI technology provides new solutions for Chinese restaurant companies to cut costs and increase efficiency and to respond to competition. In particular, with the arrival of the big model and generative AI era, AI technology will no longer be a “choice” for restaurant companies, but a “must-have.” Deep application of AI technology is an important strategy for restaurant companies to break through the efficiency ceiling under competition for existing market share and to respond to homogeneous competition.

This image is suspected to be AI-generated

Currently, the global restaurant AI market is in a period of rapid expansion, with both scale and growth rate high, demonstrating strong development momentum. According to publicly available information, in 2025 the global restaurant AI market size reached USD 15 billion, up 38.9% year over year, and is expected to exceed USD 20 billion in 2026.

In terms of regional landscape, the global restaurant AI market shows a development pattern of “North America leading, Asia closely following.” North America holds an overwhelming leading position with a 58% share; Asia is the second-largest growth engine, accounting for about 24% of the market size.

Specifically regarding restaurant AI applications in China: AI in the restaurant industry is developing rapidly, but adoption still needs to improve. For example, based on AI application penetration rates, research by the Hongcan Industry Research Institute found that the current AI application penetration rate in China’s restaurant industry is only 15%. However, with further application of restaurant AI technologies, the Hongcan Industry Research Institute projects that by 2028 this figure will further rise to 50%.

At the same time, the application scenarios of restaurant AI are also viewed favorably by the capital markets. Although the total financing amount across China’s restaurant industry has been declining in recent years, the restaurant AI segment has shown a counter-trend growth pattern—both the financing amount and the number of financing events have increased. In 2025, the restaurant AI sector saw 18 financing events in total, with cumulative financing amount of about RMB 2.8 billion, representing a year-over-year increase of 55.6%, and capital is further concentrating.

In terms of market participants in restaurant AI: the main participants in China’s restaurant AI market include leading restaurant giants, SaaS service providers, vertical AI solution vendors, and cross-industry internet giants.

Among them, restaurant companies represented by McDonald’s, Haidilao, Luckin Coffee, Mixue Bingcheng, Juewei Food, Ban Nu Hotpot, Xin Rongji, and Hofudian Noodle House—leveraging their extensive chain store networks, large-scale data accumulation, and strong capital—play an important role in promoting the implementation of restaurant AI applications. However, given concerns about data security, development costs, and the application closed loop, leading companies’ AI applications are difficult to scale across more restaurant companies.

That said, a group of outstanding restaurant AI suppliers has also emerged in the restaurant AI ecosystem. For example: the interactive robots launched by Zhiyuan Technology; the delivery robots built by Qilang Intelligent; the AI cooking robots developed by Topbon · Chu Ji; the restaurant AI smart voice “card” released by Yuhu Xiao Honghua; the AI omnichannel marketing tool “Pong Pong” launched by Zhipengbao; the Xiao’ao enterprise-level intelligent operations platform released by Ochiwei; the AI smart review services provided by Huipingdian; and AI smart private-domain operations solution by Icc Grow—each of them is pushing forward the implementation of restaurant AI applications.

Although the development momentum of restaurant AI in China is strong right now, there are still many difficulties in its implementation. For instance: low accuracy of general-purpose models, rampant “pseudo-AI,” and imbalances between the supply and demand for compound talent, etc.

And through continuous observation of cases from major large-model platforms both at home and abroad, the Hongcan Industry Research Institute has found that there are very few AI application cases in vertical catering industries. More restaurant companies’ AI applications still remain at basic levels such as copy generation, video editing, and automated customer service responses.

2. With a four-layer architecture of perception, decision-making, interaction, and execution, restaurant AI has penetrated every step of restaurant operations

At present, restaurant AI technology has formed a coordinated four-layer architecture of perception, decision-making, interaction, and execution, providing core support for intelligent development in restaurant companies. The perception layer completes the collection and recognition of scenario data, laying the data foundation for intelligence; the decision layer relies on algorithms to output refined operational decisions, enabling optimal allocation of resources; the interaction layer optimizes service experience and operational efficiency through human-computer interaction technologies; and the execution layer uses intelligent robots to implement standardized operations.

1. The restaurant AI perception layer, centered on computer vision, enables scenario data collection and intelligent monitoring

As the “five senses” of restaurant AI, the perception layer relies primarily on computer vision (CV) technology. Using algorithms such as image recognition and object detection, it converts physical scenario data into structured information, laying the data foundation for restaurant intelligence.

In practical applications, perception-layer technology is suitable for both front-of-house and back-of-house scenarios. In front-of-house scenarios, CV technology can accurately analyze customer traffic and customer emotions, optimize service routing, and enhance the dining experience; in back-of-house scenarios, it can enable hygienic standards monitoring, dish quality control, and ingredient loss management, ensuring standardized output and avoiding food-safety risks.

For example, the AI smart inspection system independently developed by Haidilao applies CV technology deeply. It currently achieves 100% coverage across all stores nationwide. The system relies on computer vision and edge computing technologies to enable a two-hour closed-loop management process, with recognition accuracy exceeding 95%. It effectively ensures consistent implementation of service standards and helps keep store review rates stable at over 98%.

2. The restaurant AI decision layer uses algorithm-driven intelligent decision-making to empower the full supply-chain and operations to improve efficiency

The decision layer is based on data from the perception layer and a company’s historical operational data. It leverages algorithms such as big data analysis, machine learning, and time-series forecasting to enable deep analysis and intelligent decision-making, providing references for enterprise operation and management.

Decision-layer technologies can be applied to operational and supply-chain scenarios. In operational scenarios, its core applications include precise demand forecasting, dynamic pricing, and intelligent scheduling, which can effectively optimize labor allocation and improve operational efficiency; in supply-chain scenarios, AI runs through the entire process of procurement, warehousing, and delivery, enabling intelligent procurement and inventory management and reducing ingredient loss.

For example, Juewei Food, relying on its massive database and big data analysis algorithms, built an AI agent matrix that includes three intelligent agents. Among them: the AI ordering assistant “Xiao Huoya” optimizes decision-making pathways and enhances emotional value through personalized recommendations and interactive features; Juewei’s AI store manager “Juezhi” stores 143k pieces of “golden store manager” experience, supporting staff to learn and do simultaneously, as well as sales practice drills; the AI membership intelligence agents break down processes such as audience targeting, benefits, product selection, and content into multi-agent collaboration, improving the effectiveness of campaigns.

3. The restaurant AI interaction layer relies on natural language processing to achieve smooth human-computer interaction, significantly improving ordering efficiency and service experience

As a connection bridge between restaurant AI and people, the interaction layer primarily relies on natural language processing (NLP) technology, combined with capabilities such as large language models (LLM) and speech recognition (ASR), to enable natural and smooth human-computer interaction.

Its applications cover front-of-house, back-of-house, and operational scenarios: front-of-house can use AI voice ordering and multilingual intelligent customer service; the back-of-house can use AI to assist with intelligent menu design; and the operations side can use AI to generate personalized marketing copy.

For example, McDonald’s is one of the earliest restaurant companies to deploy AI intelligent ordering. After deploying AI ordering, its average check size increased by 4.5%; order accuracy and ordering accuracy increased by 17 and 13 percentage points respectively; and reductions in customer waiting time and equipment downtime reached 50% and 40% respectively, significantly improving business performance and operational efficiency.

4. The restaurant AI execution layer relies on intelligent robot technology to enable task automation— the market scale of restaurant robots is expanding rapidly

The restaurant AI execution layer primarily relies on intelligent robot technology. By integrating collaborative robots, SLAM navigation, precision force control, and other technologies, it converts decision instructions into physical operations. Its applications cover scenarios such as back-of-house cooking, front-of-house service, and supply-chain warehousing.

Data shows that from 2020 to 2030, the market size of China’s restaurant robotics is expected to grow from RMB 500 million to RMB 32 billion. Among them, cooking/cooking robots and delivery/food-to-table transfer robots are the core categories. Execution-layer technologies are rapidly driving automation and standardization of restaurant operations, providing solid support for cost reduction and efficiency improvement across the industry.

For example, in the cooking robot field, the AI cooking robot—F3—developed by Topbon · Chu Ji is equipped with an AI system. The AI has self-learning capabilities and can automatically optimize cooking parameters based on ingredients and user feedback. The product integrates six major modules, enabling functions such as precise temperature control, autonomous stirring, automatic ingredient dispensing, cleaning, and precise dispensing of various seasonings including powders, liquid ingredients, viscous ingredients, and lard, among others. It is perfectly suited to multiple application scenarios such as sit-down meals, fast food, and group-meal counters. The product repurchase rate is as high as 90%, and it has been sold to 30 provincial-level administrative regions nationwide.

In the delivery robot field, Qilang Intelligent Technology has already formed multi-scenario adaptation solutions covering Chinese full-service dining, Japanese catering, and hotel delivery. It introduces customized machine models for different scenarios, achieving breakthroughs in key capabilities such as millimeter-level obstacle avoidance, cross-floor delivery, and multilingual interaction, providing standardized capacity solutions for chain restaurant brands that are mature, stable, and can support large-scale expansion.

In the interactive performance robot field, Zhiyuan Technology has built a robot product matrix that supports multi-scenario adaptation. The scale of its equipment exceeds 1,000 units. It has already reached deep cooperation with multiple well-known restaurant companies, with services covering more than 50 cities.

Conclusion

In the future, as technology matures and scenarios deepen, restaurant AI will evolve toward four major directions: autonomous intelligent operations, deep GEO applications, an AI specialized job role system, and the building of dedicated knowledge bases. To better respond to this trend, restaurant companies need to focus on three key tasks: first, select mainstream large models with strong usability, and promote full員 adoption through systematic training and incentive mechanisms; second, customize dedicated agents adapted to business scenarios and establish a continuous iteration and optimization mechanism; third, comprehensively sort out the company’s knowledge assets, complete knowledge base construction and model “taming,” and form a data feedback closed loop.

Author: Hongcan Industry Research Institute