美团智能掌柜升级“主动管店”功能 预计8月全国铺开

This article covers the latest upgrade to Smart Store Manager, Meituan's AI-powered in-restaurant dining operation tool. The new proactive store management feature is currently being tested with selected merchants in Beijing and is expected to launch nationwide in August. Key takeaways are as follows:

1. Core functional change: The interaction logic has flipped from merchant-initiated "query and response" to system-driven proactive service. The tool now conducts daily automatic inspections of store operations, summarizes operational issues and competitor activity, pushes targeted improvement suggestions with actionable plans, and tracks results for iterative adjustment.

2. Real-world performance: For both independent stores and chain brands, the tool drastically cuts time spent on data screening, clears operational bottlenecks, and supports data-driven decision-making. To date, the system has served more than 1 million merchants cumulatively.

3. Unresolved open questions: The accuracy of AI recommendations relies heavily on the platform's data volume, and algorithmic bias could lead to misleading guidance. The appropriate boundary between merchant operational autonomy and algorithmic decision-making also remains to be defined through practice.

This article offers valuable insights for catering brands on digital transformation and operation upgrade, with key takeaways below:

1. Value of the new digital tool: The upgraded proactive store management feature enables brands to rapidly aggregate and analyze operational data, organize customer reviews and track competitor activity in their business districts, cutting down data processing time significantly. It also delivers actionable improvement plans, tracks results, and conducts automatic iterative adjustments.

2. Implementation for chain brands: Chain brands can integrate this tool into data analysis across store, regional, and financial operational lines to clear operational bottlenecks, accurately identify customer demand, and support brand-level strategic decision-making.

3. Risks to watch for: The accuracy of AI recommendations depends heavily on the platform's understanding of specific catering formats and the completeness of business district data. Incomplete data or algorithmic bias can mislead operations. Brands also need to clearly define the boundary between their own operational autonomy and algorithmic influence to avoid over-reliance on AI.

This article outlines new operational tool opportunities and risk warnings for catering sellers, with key insights below:

1. New growth opportunity: Against the backdrop of catering industry online penetration, small and medium-sized sellers struggle with low-efficiency data asset access and high barriers to manual data processing. Meituan's new proactive store management feature directly solves this pain point: it helps sellers save time on data processing, quickly identify operational issues, access competitor information, and get targeted improvement plans.

2. Suitable for sellers of all scales: Independent small and medium-sized stores can access professional operational analysis services at low cost, while chain brands can embed the tool into multi-dimensional operational workflows to boost overall operational efficiency.

3. Clear risk warning: AI-generated recommendations are not 100% accurate. Incomplete platform data or algorithmic bias can lead to misleading guidance. Sellers need to balance their own operational judgment with algorithmic suggestions to retain full operational autonomy.

This article provides actionable insights for digital transformation and business expansion for factories across the catering upstream and downstream supply chain, with key takeaways below:

1. New business opportunities to capture: As online penetration in the catering industry continues to rise, merchant demand for intelligent, digital operation tools has grown sharply. More than 1 million merchants already use tools like Meituan's Smart Store Manager, proving that the catering B2B service market holds significant untapped space. Catering supply chain and supporting equipment factories can expand B2B digital supporting businesses to match current market demand.

2. Reference for product design: Market demand for digital tools has shifted from passive response to proactive service. Factories developing internal production management and customer-facing service tools can adopt this logic to add automatic inspection and proactive recommendation features.

3. Insights for internal digital transformation: Factories can draw on this proactive operational diagnosis model to replace traditional passive problem-finding workflows and boost overall production and operational efficiency.

This article shares key insights on industry trends, customer pain points and product direction for B2B catering service providers, with core takeaways below:

1. Current core customer pain points: Following rising online penetration in catering, both small and medium-sized merchants and chain brands struggle with inefficient use of their data assets. Traditional passive tools require manual merchant input and have high adoption barriers, leaving large volumes of operational insights untapped. Merchants now need more low-friction, efficient operational services.

2. New industry development trend: AI-powered catering operation services have evolved from pure data analysis tools to proactive operational agents, with interaction logic shifting from query-and-response to automatic problem identification. This is a key development direction for the future B2B services industry.

3. Reference for product development: Providers can replicate the proactive store management model to build products that conduct automatic operational inspections, generate analysis and actionable plans, and track results for iterative adjustment. Providers must also prioritize improving recommendation accuracy, and balance algorithmic support with customer operational autonomy to mitigate product risks.

This article provides insights on product direction and risk mitigation for platform operators offering B2B catering services, with core takeaways below:

1. Core merchant demand for B2B platforms: Today's merchants are no longer satisfied with basic query tools from platforms; they demand lower-barrier, more proactive operational support that identifies and solves problems for them to reduce operational burden.

2. Reference for platform product upgrades: Platforms can follow Meituan's upgrade example by flipping the interaction logic of AI tools and adding proactive operational diagnosis features. The tool can automatically conduct operational inspections for merchants, output analysis and improvement suggestions, and adjust strategies based on result tracking. This approach boosts merchant retention and proves the value of a platform's B2B services.

3. Industry risks to avoid: Platforms need to build up domain knowledge for different segmented catering formats and enrich business district data in advance to avoid algorithmic bias that turns proactive recommendations into misleading guidance. Platforms also need to clearly define the boundary between algorithmic services and merchant operational autonomy to avoid controversy that harms platform growth.

This article provides research material on new industry trends and open questions for researchers focused on catering digitalization, with core insights below:

1. New industrial development trend: Meituan, a leading local life platform, has upgraded its AI-powered catering operation tool to add a proactive store management feature. This shifts AI's role from a data analysis tool to an operational agent, flipping interaction logic from passive response to proactive service. The tool already serves more than 1 million catering merchants and is expected to roll out nationwide in August, representing the latest development direction of B2B digital catering services.

2. New research questions to explore: This upgrade raises two new industry-wide questions: First, since the accuracy of AI proactive recommendations heavily depends on platform data accumulation and domain knowledge, what negative impacts can algorithmic bias cause? Second, as platforms evolve from tool providers to operational managers, how should the boundary between merchant operational autonomy and algorithmic decision-making be defined? Both are new topics worthy of in-depth research.

3. New business model exploration: This upgrade also represents a new exploration of B2B platform business models, shifting from selling tools to selling deep operational services. This approach helps platforms strengthen merchant retention and unlock more value from B2B services, making its future development a worthy research focus.