UMM AI 
Writing a helpful reply and being authorised to send it are two different things. Before you automate a customer conversation, decide which decisions the system is allowed to make.
Separate the routine from the consequential
A reply about opening hours is different from a reply about a disputed payment. One may rely on stable, approved information. The other may need account context, judgement and permission to act.
Map the types of enquiries your team receives. For each, consider what could happen if the answer is wrong, incomplete or sent to the wrong person.
Give people a clear place in the process
A useful starting point is to let AI prepare a draft while a person reviews it. That review should check the facts, tone, recipient and any commitment being made.
If some replies can be sent automatically, define the conditions and the route to a person. An unanswered question should not become an invented answer.
Test before widening the scope
Use representative enquiries, including unclear wording and unusual situations. Review the information the system uses and whether it knows when to stop.
Watch the work after launch: corrections, escalations, complaints and the effort needed to review drafts. Faster responses are useful only when the whole experience improves.
FAQ
Frequently asked questions.
Can we start with AI drafting only?
Yes. Start with reviewed drafts, then assess quality, exceptions and customer feedback before allowing automatic replies.
Does every reply need a person?
Set review rules by consequence and uncertainty; routine answers may be suitable when sources and permissions are clear.
What should we measure beyond response speed?
Track corrections, escalations, complaints and review effort to understand whether automation improves the customer’s experience overall.
