Choose a focused use case with a useful outcome
Knowledge search, reply drafting, document-field extraction and enquiry classification need different quality criteria. Start with your examples and define a correct outcome. Sometimes conventional search or a rule is simpler and cheaper. When AI is useful, design it as part of the workflow: who sees the result, what they check and what happens next. The aim is useful assistance, not a separate chat window added merely to advertise artificial intelligence.
Test with your information and difficult examples
A model can sound confident and still be wrong. Connecting a knowledge base helps retrieve context but does not guarantee factual answers. Build a test set covering routine requests, ambiguity, missing information and attempts to change instructions through incoming text. Decide when sources are needed, when a draft is acceptable and when to hand over to a person. Repeat checks after model or content changes instead of treating quality as permanently settled at launch.
Plan permissions, actions and costs explicitly
An assistant does not automatically need every document or permission to send messages for the business. Agree on sources, user access, sensitive-data handling and actions needing confirmation. External model services bring their own terms and usage costs. Define a fallback when the model is unavailable or fails. Deliver a manageable capability with a process for knowledge updates, monitoring and evaluation, rather than a promise that AI will somehow learn everything on its own.
Two useful questions
Can AI start with drafting only?
Yes. Drafting is useful when accuracy and operator control matter. Additional permissions can be considered after evaluating quality in real work.
Does the system train itself on every message?
No. Conversation history, knowledge-base updates and model training are different processes. Each must be designed and agreed separately rather than assumed to happen automatically.