JellyAI 免费 AI 诊断
返回前线笔记
AI DEPLOYMENT

企业 AI 试点为什么停在 Demo？

多数失败不是模型不够强，而是没有把真实流程、数据权限和验收指标一起设计进去。

AI 落地
AI DEPLOYMENT

Why Do Enterprise AI Pilots Stop at the Demo?

The issue is rarely model capability alone. Real workflows, data permissions, and acceptance metrics must be designed together.

AI Deployment

Demo 和生产之间差了什么

Demo 往往只验证“能不能回答”，生产环境要验证“能不能稳定完成一个业务动作”。这中间会遇到数据不完整、权限不清、流程责任人缺位、异常处理没有兜底等问题。

JellyAI 的处理方式

  • 先选择一个高频、重复、结果可量化的流程。
  • 把知识、规则、系统调用和人工审核节点画清楚。
  • 在上线前定义成功率、响应时间、节省人力和失败回滚方式。

试点的正确目标

第一个试点不应该追求覆盖所有部门，而是证明一条流程可以被 Agent 稳定接管、被团队看见、被指标评估，并能复制到下一条流程。

The Gap Between Demo and Production

A demo proves whether AI can answer. Production proves whether AI can reliably complete a business action with data, permissions, fallback, and ownership in place.

How JellyAI Approaches It

  • Pick one frequent, repetitive, measurable workflow.
  • Map knowledge, rules, system calls, and human review points.
  • Define success rate, response time, labor savings, and rollback before launch.

The Right Pilot Goal

The first pilot should prove that one workflow can be operated, observed, evaluated, and then reused as the basis for the next workflow.

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