Ticket triage with AI: a worked example

This is an illustration, not a client story. It follows one ticket through a support or IT desk to show what an AI agent can do at each step, and where your people stay in control.

The situation

A team shares one inbox. Tickets arrive unsorted, urgent ones look like routine ones, and the same answers are typed again and again. Knowledge sits in old tickets and documents, and in people's memory.

What the agent does with one ticket

The ticket reads: 'I can't log in after resetting my password. I have a client call in an hour.' Here is what happens.

1. 1It reads the ticket and works out the type, an access problem, and the urgency, high, because the person has a deadline.
2. 2It routes the ticket to the right queue, using the categories and rules your team set.
3. 3It looks through your knowledge base and past resolved tickets for the same problem, and finds the relevant article and two similar cases.
4. 4It drafts a reply based on those, and lists the sources it used, so the person reviewing can check them.
5. 5If the same problem is already open on another ticket, it links the two so one fix can close both.

Where the person steps in

• A member of your team reads the draft, edits it if needed and approves it before it is sent.
• Sensitive categories, such as security incidents or complaints, can skip the draft and go straight to a person.
• Anything the agent cannot find support for is passed on with a note, not guessed at.

What you would need to provide

• A sample of recent tickets, with personal details removed if you prefer.
• Your knowledge base and the categories you use.
• Your escalation rules, and who approves replies.

How you would know it works

Agree the measures beforehand: the time to first response, how often a ticket lands in the right queue first time, checked on a sample, and how often reviewers have to change the draft. Measure before and after, then decide together how much the agent should do on its own.