千峰访谈预策科技：让AI真正参与经营，构建持续进化的AI电商经营体系

Summary 1: The article focuses on Yuce Technology's positioning in e-commerce and its effort to build a sustainably evolving AI business operating system. The emphasis is not on point tools but on multi-agent collaboration that can take on complete business tasks.

1. The system architecture combines capabilities, a business knowledge base, skills, workflows, and business applications, connecting data, intelligence, and business into a closed loop and continuously accumulating reusable intelligent assets.

2. Its three core capabilities are data understanding, industry expertise, and coordinated execution. With enterprise authorization, it completes real business operations through APIs.

Summary 2: The implementation cases and practical implications are noteworthy.

1. A children's apparel brand used AI business experts to fully automate more than 2,000 ad campaigns; after deployment at its flagship store, it generated seven-figure RMB incremental revenue in a single week.

2. Since 2026, token call volume has grown by an average of 16% month over month. Through skills, the company continuously optimizes model selection, pipeline stability, response speed, and cost per task, stressing that every token consumption is tied to a business goal.

3. Enterprises should first build a semantic layer and metric system, use AI workshops to identify high-value priority scenarios, and translate business experience into operational AI experts and workflows.

Summary 1: Brands should focus on how AI enters brand marketing, merchandise management, content production, advertising placement, and flagship-store operations.

1. Yuce AI covers core links across the full chain, including market analysis, merchandise management, content production, advertising placement, and daily operations, enabling mature operating strategies to be standardized and automatically replicated.

2. In the advertising case, a large children's apparel brand deployed AI business experts at its official flagship store, fully automated more than 2,000 ad campaigns, and generated seven-figure RMB incremental revenue in a single week. This shows AI can support brand operations at scale.

Summary 2: The data foundation and organizational support for brand operations are critical.

1. Brands should first build an e-commerce full-chain data semantic layer and metric system to address scattered data, inconsistent definitions, and unclear semantics across products, traffic, advertising, users, and transactions, so AI can understand brand operating status and market dynamics.

2. Multiple agents collaborate around the same business goal; combining data understanding, industry expertise, and API execution allows brands to delegate more specialized operating work to AI and iterate continuously.

3. Business models are shifting from pricing by AI usage volume to value-based pricing tied to business outcomes. Brands should evaluate AI by real operating uplift and long-term operating efficiency gains.

Summary 1: Sellers should pay attention to market opportunities, shifts in operating models, and risk warnings brought by AI e-commerce.

1. As sellers address more markets, platforms, and consumer demands, operating chains and coordination become more complex. AI can standardize and automatically reuse industry knowledge, operating experience, and cross-link tasks.

2. The opportunity lies in AI covering the full chain of market analysis, merchandise management, content production, advertising placement, and daily operations. Multi-agent systems can take on complete business tasks rather than only fragmented ones.

Summary 2: Implementation methods, cost control, and partnership approaches are worth learning from.

1. Sellers can start with a semantic layer and metric system, use a business knowledge base to codify e-commerce methodologies, package professional capabilities as skills, and use workflows to coordinate multiple AI experts around the same business goal.

2. Yuce's token call volume has grown by an average of 16% month over month since 2026, but it does not pursue volume alone. Instead, it uses skills to optimize model selection, pipeline stability, execution response speed, and cost per task.

3. Risks include inconsistent data standards and differing API openness across e-commerce platforms. Clients have stringent requirements for data security, permission control, and business operation risk, so companies need data standardization, cross-platform connectivity, fine-grained permissions, and security risk controls.

4. For partnerships, sellers can connect to e-commerce platforms through APIs and execute real business operations with enterprise authorization, supported by AI workshops and implementation services to identify high-value priority scenarios.

Summary 1: Factories can see the digitalization of merchandise operations and business opportunities in the full-chain implementation of e-commerce AI.

1. Yuce AI covers market analysis, merchandise management, content production, advertising placement, daily operations, and other links, indicating that merchandise management, content production, and advertising placement can be standardized and handled by AI.

2. In the partnership case, a large children's apparel brand fully automated more than 2,000 ad campaigns and generated seven-figure RMB incremental revenue in a single week. This shows mature professional practices can be replicated at low cost and run continuously to support business expansion.

Summary 2: The implications for advancing digitalization and e-commerce center on data, architecture, and feedback loops.

1. Companies should first build a data semantic layer and metric system to structure and semanticize scattered data on products, traffic, advertising, users, and transactions, so AI can participate in business analysis, business execution, and intelligent iteration.

2. Adopt an integrated architecture of capabilities, business knowledge base, skills, workflows, and business applications; turn business experience into callable AI experts; and have multiple agents collaborate around business goals.

3. Through APIs and enterprise authorization, complete real business operations; collect behavioral data and business results to form a feedback loop that in turn evaluates and iterates AI expert capabilities. Support this with AI workshops to identify high-value priority scenarios.

Summary 1: Service providers should focus on industry trends and new technology foundations in AI e-commerce.

1. AI is moving from the first year of agents into a phase of rapid scaling. AI OS is entering a critical window for iterative deployment and scaled commercialization, with security, stability, and controllability becoming baseline requirements for the underlying platform.

2. Business models are shifting from pricing by AI capability usage volume to value-based pricing tied to business outcomes, continuously accumulating reusable intelligent assets and creating an intelligence compounding effect.

3. Industrial AI evolves through three stages: skillization, workflowization, and intelligence compounding. The key to intelligence compounding is using business data and outcome feedback generated by AI execution to continuously feed the iteration of AI experts and workflows.

Summary 2: Client pain points and solutions deserve close study by service providers.

1. Client pain points have shifted from technical feasibility to deeply integrating AI into business processes and organizational mechanisms. What enterprises lack is directly deployable vertical professional intelligence and a long-term operating mechanism that supports AI's routine participation in operations. Efficiency gains from point tools are hard to replicate and scale across the organization.

2. The solution is a five-part integrated architecture, three core capabilities, and four data capabilities: industry data accumulation, data processing centered on ontology construction, cross-platform operating data access and business execution, and a feedback loop for AI execution chains.

3. Service delivery also includes AI workshops and implementation services to help enterprises identify high-value priority scenarios, translate business experience into operational AI experts and business workflows, and promote routine use and iterative optimization within the organization.

Summary 1: Platform operators should focus on enterprises' needs and challenges regarding platform data openness, API execution, and security risk control.

1. Enterprises need cross-platform operating data access and business execution. Through API channels and within the boundaries of enterprise authorization, they aggregate multi-source business data and support AI in generating executable business actions based on semantic understanding.

2. E-commerce platforms have inconsistent data standards and differing API openness. At the same time, clients have stringent requirements for data security, permission control, and business operation risk. These are platform-side issues that must be addressed for AI to scale.

Summary 2: Platform practices and risk-avoidance implications center on standardization, controllable execution, and risk control.

1. Platforms need to advance data standardization, cross-platform connectivity, fine-grained permission systems, and security risk controls to ensure AI makes decisions and executes based on real operating conditions.

2. Yuce emphasizes controllable execution channels on platforms and deep execution links at the platform end. AI experts can complete real business operations under enterprise authorization and collaborate around the same business goal to take on complete business tasks.

3. Platform operators should balance open interfaces and controllable execution, avoid data security and business operation risks, and also balance multi-platform data connectivity with industry data asset accumulation.

Summary 1: Researchers should watch new directions in industrial AI and changes in business models.

1. AI's capability boundary is upgrading from point tasks and simple Q&A to replicating, automating, and continuously optimizing complete enterprise business processes. AI OS has entered a critical window for iterative deployment and scaled commercialization. Industrial agents are pushing B2B from delivering tools to undertaking business tasks and delivering operating results.

2. Industrial AI evolves through three stages: skillization, workflowization, and intelligence compounding. Intelligence compounding depends on business data and outcome feedback generated by AI execution, which continuously feeds the iteration and evolution of AI experts and workflows.

3. Business models are shifting from pricing by AI capability usage volume to value-based pricing tied to business outcomes, continuously accumulating reusable intelligent assets.

Summary 2: New questions and moat implications are worth studying.

1. New questions include inconsistent platform data standards, differing API openness, data security, permission control, and business operation risk. The main challenge in enterprise AI implementation has shifted from technical feasibility to business processes and organizational mechanisms. Enterprises lack vertical professional intelligence capabilities and long-term operating mechanisms.

2. For vertical large models, not every industrial enterprise needs to train its own foundation model from scratch. The core value of industry players lies in deep industry knowledge, accumulated mature operating methodologies, compliant business data connectivity, deep execution links at the platform end, and a sustainable iterative closed-loop mechanism.

3. The long-term moat of industrial internet AI is a self-running closed loop of industry data, professional decision-making, controllable business execution, and outcome feedback, moving from a capability-enhancement mechanism to a capability-gap-widening mechanism. Data competitiveness lies not in existing scale but in enabling data to continuously participate in business analysis, business execution, and intelligent capability iteration.