NetEase Zhichi is already piloting AI Agents at medical aesthetics institutions across three stages: content planning, consultation preparation, and session handoffs. Our judgment is that the standard for enterprise AI implementation is shifting from “generating content” to “preserving working methods,” but medical judgment, privacy authorization, and human review remain irreplaceable.
What This Is
NetEase Zhichi’s enterprise AI Agent platform, King Crab (ClawHive, an intelligent agent platform that connects enterprise knowledge with business workflows), organizes information scattered across competitor accounts, brand materials, client profiles, and historical communications into specific tasks.
For example, the operations system can first analyze local competitors and account content, then combine the institution’s positioning and authorized case studies to produce a draft while flagging professional claims that require review. In consultation scenarios, AI organizes clients’ concerns, prior communications, and outstanding questions, then generates a preliminary plan for professionals to review. After a service session ends, the system extracts key information and produces a verifiable handoff summary.
The core problem it addresses therefore goes beyond copy generation. The system is designed to keep business rules and client context usable even when employees change roles or institutions replace staff, turning completed work into a foundation that can inform the next step.
Industry View
The notable change is that industries such as medical aesthetics, which depend heavily on employee experience, are beginning to shift AI purchasing criteria from generation speed toward workflow coverage and knowledge accumulation. If a system can continuously deliver vetted topic-development methods, service records, and handoff information, institutions may reduce training costs and the disruption caused by employee turnover.
But we do not believe that “uploading chat logs” can produce a digital sales champion. High-performing employees possess field judgment, communication nuance, and relationship trust that are difficult to structure; AI can preserve only the parts that can be organized and reviewed. Medical aesthetics materials also involve sensitive personal information, medical advice, and institutional liability. Permission isolation, authorized use, and human confirmation will determine whether this approach can operate sustainably over the long term.
Another risk is that workflows become固化 by models. If an institution writes scripts that work in the short term directly into its knowledge base without specifying their conditions of use and review responsibilities, the system may scale outdated experience. “The more it is used, the better it understands the business” therefore still depends on continuously maintaining materials, rules, and boundaries of accountability.
Impact on Regular People
For enterprise IT: When enterprises purchase AI, they will pay closer attention to whether it can integrate with knowledge bases, permission systems, and job workflows—not just whether it looks good in a demo. Data governance and human review will also become prerequisites for deployment.
For individual careers: Content organization, handoff documentation, and preparation may increasingly be delegated to AI, while credential review, critical communications, and accountability judgments remain human responsibilities. How effectively experience can be accumulated will increasingly shape an individual’s replaceability within a team.
For the consumer market: Consumers may receive more consistent consultation preparation and service handoffs, but personalized communication may also increasingly derive from historical data analysis. How institutions explain the scope of authorization and protect sensitive information warrants continued attention.