01 · Common Mistake
Many companies start with tools, training, or platforms too early
When companies begin AI projects, they often buy model accounts, arrange a generic AI training session, or ask IT to evaluate a private deployment. None of these actions are wrong, but without a business scenario they rarely become real productivity.
The problem is usually not a lack of AI tools. The problem is that work has not been broken down into a process AI can actually support: customer profile preparation, meeting note summarization, policy monitoring, equipment troubleshooting, proposal drafting, or internal knowledge Q&A.
02 · Right Starting Point
Start with one verifiable business scenario
A verifiable scenario is not a slogan. It has clear inputs, repeated steps, observable outputs, and someone who can judge whether the result is good enough.
For example, 'improve efficiency with AI' is too vague. 'Summarize 20 industry updates every morning, produce a research brief, and cite sources' can be implemented and tested.
- Choose one department before the whole company.
- Choose one frequent task before a universal assistant.
- Define the output before discussing intelligence.
- Assign an acceptance owner before building.
03 · Selection Criteria
Use five conditions to judge whether a use case should go first
Companies usually have many AI ideas at once: customer service, sales assistants, contract review, knowledge bases, reporting, analytics, and workflow automation. A shared screening framework prevents scattered experimentation.
A good first AI scenario should be frequent, painful, supported by accessible data, measurable by humans, and low enough risk that mistakes can be contained.
- Frequency: does it happen often enough to matter?
- Pain: does it involve repeated searching, organizing, summarizing, judging, or notifying?
- Data: can the system access the relevant documents, tables, records, or rules?
- Acceptance: can a business owner judge the output clearly?
- Risk: can errors be caught by review or limited pilots?
04 · 30-Day Path
Spend the first month shipping one MVP
For a first enterprise AI project, the safest rhythm is not a long platform roadmap. It is a 30-day MVP that real users can test in real work.
Week one is scenario diagnosis and workflow mapping. Week two is knowledge preparation and output design. Week three is a first Agent, knowledge base, or automation flow. Week four is limited pilot usage, logs, feedback, and a decision on whether to expand.
- Week 1: interview business owners and map the current workflow.
- Week 2: prepare knowledge sources, permissions, outputs, and acceptance criteria.
- Week 3: build a working MVP without excessive features.
- Week 4: test with real users and iterate from evidence.