Quick answer
An AI chatbot should solve a defined customer problem, such as answering routine service questions or finding information in a product catalogue. Start with actual enquiries and identify which can be answered from approved material. Keep a path to a person when the answer is uncertain or the request needs judgement.
Practical scope
Decide what documents the assistant may use, who updates them and whether it may access customer-specific records. If it collects personal information, define consent, retention and staff access. Test it with real questions, ambiguous wording and cases where the correct answer is “I don't know.”
Measure useful outcomes: resolved questions, handovers, incorrect answers and time saved. Ask developers to explain ongoing model, hosting and monitoring costs. A pilot with a narrow knowledge base is often the clearest way to learn whether the tool helps customers.
What to define before requesting proposals
- Specific customer problem and permitted knowledge sources
- Questions that require human judgement or escalation
- Personal data, permissions and retention
- Accuracy tests, monitoring, cost and success measures
Questions to ask service providers
- How does the chatbot cite or identify its source?
- What happens when information is missing or uncertain?
- Who reviews incorrect answers and updates knowledge?
How to assess the answers
Begin with a narrow, approved knowledge base and real test questions. Measure correct resolution, handover and harmful errors instead of message volume alone.
Related planning guides
- Connecting Your Website, CRM and ERP: An Integration Brief
- How to Compare Website and Software Development Quotes
Next step: Put these decisions into one project brief, then ask providers to respond to the same scope.