Start with the support job
A useful AI chatbot starts with a clear job. It should not try to answer everything. It should handle a defined set of questions, workflows, and handoffs better than the current process.
For customer support, that usually means answering repeated questions, helping users find policy or product information, collecting context before escalation, and routing complex issues to the right person.
Prepare the knowledge before the chatbot
The chatbot is only as strong as the knowledge it can access. Gather the help docs, product pages, internal notes, refund policies, onboarding guides, and support macros it should rely on.
Then clean them. Remove contradictions, rewrite vague answers, and add missing edge cases.
Design escalation from day one
The safest chatbot is one that knows when to stop. Define when it should ask for clarification, create a ticket, transfer to a person, or say it cannot answer.
Test with real conversations
Use real tickets, repeated questions, confusing phrasing, angry users, incomplete context, and policy edge cases. Track whether the answer came from approved knowledge and whether a user could act on it.
Launch with a review loop
After launch, review failed answers, escalations, misunderstood questions, and repeated gaps. The system should get better each week.