A shared inbox is a useful place to try AI when staff spend time reading messages just to work out who should deal with them. Start by having it suggest a category and an owner. Keep the original message visible, and let a person check the suggestion before the workflow moves anything important.
That is a smaller, more testable job than asking AI to run customer service. The first question is whether it puts the right work in front of the right person without hiding requests.
Agree what the labels mean
Imagine a fictional maintenance business with one inbox for bookings, existing jobs and accounts. Staff agree four labels: new enquiry, existing job, accounts query and needs review.
Write a short definition and a few examples for each. A customer saying “I need to move next Tuesday's visit” concerns an existing job, even though the message also asks about booking. A message containing both a repair request and an invoice dispute needs a route that preserves both tasks.
Keep urgency separate from subject. A clear accounts query can still need prompt attention. State which phrases or known senders trigger an immediate alert under your ordinary operating procedure, whether or not the AI classification has finished.
Microsoft documents a category-classification action that returns proposed labels and a confidence score. That is one implementation option. A fluent explanation or high score is not evidence that your particular message has been routed correctly.
Connect a suggestion to a useful next step
A trial record should show the message reference, received time, proposed label, proposed team and a short passage supporting the choice. Don't make staff open three tools to find the email the model read.
Use a stable message reference so a repeated run updates the same suggestion rather than creating another task. The person accepting a suggestion should be able to change it and record a reason.
For the first trial, leave messages in the ordinary inbox. Add the suggestions to a separate review view instead of archiving or deleting the originals. Staff should be able to continue their normal work if the AI service is unavailable.
Avoid turning a proposed label into an immediate customer promise. “Existing job” does not authorise the system to change the appointment, and “accounts” does not establish that an invoice is correct.
Test the cases that create extra work
Use approved test material with unnecessary personal information removed. Include short messages, forwarded chains, attachments, several requests in one message and customers whose job reference is missing.
Check these outcomes separately:
- Did the correct team receive each request?
- Were urgent cases visible in time?
- Did staff have enough context to act?
- Did the workflow preserve multiple requests in one email?
- Did a repeat run create duplicate work?
- Could staff find every original message when the model failed?
Have someone label the expected result before inspecting the AI's suggestion. If staff disagree, settle the routing rule first. Otherwise you are measuring an unclear process as though it were a model problem.
Count corrections and missed work
Suppose, in this fictional trial, the model correctly routes 45 of 50 messages. “90% correct” sounds tidy, but the five mistakes might all involve urgent job changes. Record the type and consequence of each error, not just the average.
Measure how long staff spend reviewing suggestions compared with ordinary sorting. Include the work of finding overlooked messages and correcting the wrong owner. A category that needs constant correction may be better handled by a simple rule or left with a person.
Use the first-automation selection guide if your inbox process still has no clear owner or completion point. Clear routing usually matters more than a clever prompt.
Expand only after the queue works
Once suggested routing helps, you might allow a narrowly defined category to create an internal task automatically. Keep an exception route, an assigned owner and a way to pause that action.
Review unassigned and ageing messages alongside accepted suggestions. A successful model request does not prove that the task reached the team, and a task being created does not mean someone completed it.
The useful result is a dependable handoff. Whether staff call it AI triage or inbox automation matters much less than being able to see what arrived, who owns it and what still needs attention.