"AI is not a tool for showing off technology, but a way to make 'Haidilao-style service' a replicable operating system."
The restaurant industry seems easy to enter but is fiercely competitive. Haidilao, a leader among them, now has thousands of stores worldwide. But as it scaled, problems followed one after another. On efficiency, store operations involve staffing, ingredient prep, and serving speed—any slip affects overall operation. During peak hours, servers are busy both taking orders and serving, while also attending to customers, stretching staff thin. From the experience angle, customers demand ever-higher service—taste, environment, attitude, every element must be nailed. How to raise operational efficiency while guaranteeing service quality became an urgent problem for Haidilao.
In March 2025, a set of data disclosed in Haidilao's 2024 financial report shook the industry: its self-developed AI smart inspection system now covers 100% of stores, with over 95% recognition accuracy, zero negative reviews for the year, and store praise rates above 98%. This marks the hot-pot giant's complete departure from traditional manual inspection and entry into a new era of "AI standardization."
01 The Industry Essence: The "Standardization Paradox" of Chain Restaurants
The expansion of the hot-pot industry always faces a core contradiction: the inherent conflict between scaled replication and service personalization. Traditional restaurant management relies more on manpower and experience. When Haidilao was small, this model coped well. But as store counts surged, problems surfaced.
Delayed and distorted information transmission makes management decisions hard to execute effectively. Unreasonable staffing causes service to lag at peaks and idle at troughs, keeping labor cost high. Moreover, service levels vary across stores, making consistent customer experience hard to guarantee. The particularity of hot-pot makes it face more complex standardization challenges than other categories:
1. The Lag in Service Monitoring Becomes a Quality Hazard.
Manual inspection relies on regional managers' judgment, suffering serious "sampling bias." Haidilao once had a store where missed checks of "incomplete tableware disinfection records" let food-safety risks accumulate—a typical case of the periodic flaws of manual checks.
2. The Drift in Standard Execution Causes Uneven Experience.
Differences between veteran and new staff in understanding "smile service," and deviations across stores in executing "soup-refill frequency," create a gap between brand promise and customer experience. Data shows that before the AI store-inspection system, Haidilao's service compliance rate varied by up to 40% across stores.
3. Fragmented Feedback Response Constrains Improvement Efficiency.
Traditional paper comment cards had less than 30% return rate, and vague feedback like "poor service attitude" was hard to quantify and analyze.
These pain points are precisely the value starting point for AI. Haidilao's AI layout is not scattered trial but a systematic solution built around "service quality," forming a replicable business logic that makes "Haidilao-style service" a stable, replicable operating system across 5,300 stores.
02 Haidilao's AI Store Inspection: An Intelligent Partner for Service Standardization
Haidilao positions AI as an "intelligent partner for service standardization." Its core solution revolves around "filling gaps," with each function precisely matching an operational pain point, while preserving flexible room for "human-machine collaboration."
1. Filling the "Lag" Gap: An Automated Closed Loop of Real-Time Monitoring
The AI store-inspection system processes video locally via edge-computing gateways, building an automated flow of "anomaly detection – warning push – rectification tracking." When it detects "staff not washing hands per rules" or "abnormal fryer temperature," it immediately pushes a warning with the original video clip to the store manager; after rectification, a proof photo must be uploaded to close the loop. This mechanism cut problem-response time from the traditional 24 hours to 2 hours.
2. Filling the "Drift" Gap: Data-Driven Standard Unification
To solve the "thousands of stores, thousands of faces" problem, the AI system turns fuzzy service standards into quantifiable parameters: "add 1 server per 20% rise in peak traffic," "bone-broth base simmered ≥8 hours," "tableware disinfection water temperature ≥85°C." Monitored in real time via IoT devices, these parameters ensure consistent execution across 5,300 stores. The intelligent scheduling system further strengthens standard adoption: combining historical traffic data and staff skill tags, it auto-generates schedules, improving both work efficiency and employee satisfaction.
3. Filling the "Fragmentation" Gap: Mining Value from Full-Chain Data
The IKMS (Intelligent Kitchen Management System) builds a "farm to table" data loop, giving each dish an "electronic ID" via RFID to trace shelf life, processing time, and delivery temperature across the whole chain. When the system detects a batch nearing its shelf life, it auto-pushes a "use first" reminder. Meanwhile, the front-end AI review system uses NLP to auto-classify keywords like "too spicy" and "too slow" from negative reviews, generating improvement tickets.
From tabletop cameras to the food-safety cockpit, Haidilao turns data into actionable improvement orders rather than mere number piles. Haidilao's AI application is not simple tech stacking but follows a clear underlying logic.
Data-driven decisions: Haidilao has accumulated massive operational and customer data—a gold mine. AI deeply mines and analyzes this data to ground operational decisions. By analyzing ordering habits and taste preferences, it offers personalized dish recommendations; by time-of-day traffic, it arranges staffing rationally.
Human-machine collaborative work: AI takes over repetitive, regular tasks, freeing staff to focus on warmer service. Smart food-delivery robots handle 30% of dish running, giving servers more time to interact with customers; the AI pot-base system ensures consistent broth flavor, while chefs fine-tune for special customer needs.
Continuous optimization and iteration: AI adoption is not one-and-done; Haidilao keeps optimizing and iterating its AI system based on real operations. The AI smart-inspection system raised recognition accuracy through multiple algorithm upgrades; the intelligent forecasting system keeps learning new data to improve prediction precision.
Haidilao's AI system always leaves ample room for humans: after AI inspection flags an anomaly, the store manager decides the response; the smart schedule keeps 30% manual-adjustment authority; the auto pot-base machine lets chefs fine-tune flavor from on-site customer feedback. This model of "machines hold the baseline, humanity delivers experience" ensures standard uniformity across 5,300 stores while preserving the warm service of "remembering regular customers' dietary restrictions," ultimately achieving a 98% store praise rate.
03 Lessons from the Case: How to Build a "Service Precision" Model?
Haidilao's AI practice reveals the underlying law of chain-restaurant digitalization: in services, the ultimate goal of technology is not maximum efficiency but building "personalized service on a standardized base." Haidilao's AI application is not showmanship but makes "Haidilao-style service" a stable, replicable operating system across 5,300 stores. This practice offers three major revelations to the industry:
Precise gap-filling rather than full replacement forms the benchmark of AI value. Haidilao lets AI solve "the standardization humans cannot achieve" and humans focus on "the humanity machines do poorly," a division giving service both precision and warmth.
Data penetrating business processes determines digitalization depth. From tabletop cameras to the food-safety cockpit, Haidilao turns service data into actionable improvement orders, cutting average daily loss by 15 dishes per store and saving over RMB 10 million a year.
Organizational adaptation secures technology adoption. Through mechanisms like a digitalization committee and employee innovation submissions, Haidilao ensures AI is not an IT-department island but a company-wide capability tool. This organizational design lets 50 AI applications quickly reach store fronts, avoiding the industry-wide ailment of "technology floating in the air."
As the AI inspection system lifts service-standard execution precision to 95%, and smart evaluation delivers customer voice to headquarters within 2 hours, Haidilao is redefining the core dimension of restaurant competition: not who is warmer, but who can make warm service stable and replicable. This silent standardization revolution may be exactly the AI-transformation paradigm Chinese chain restaurants should learn from most: use technology to fortify the baseline, let humans release warmth, and ultimately find, on the scale-experience balance, the AI solution belonging to the service industry.
