Automated ticketing: how AI Workers cut IT workloads and speed up SLA resolution

Look at a month of tickets in any IT helpdesk and the pattern is the same. A small number are genuinely hard. The rest are variations on a handful of requests: access, accounts, resets, licences, and the questions people ask because they cannot find the answer themselves.

Where the time goes

• Triage: reading the ticket, working out what it actually asks for, and finding who should own it.
• Context: looking up the requester, their role, their manager, and what they already have access to.
• Policy: checking whether the request is allowed and who has to approve it.
• Action: the actual change, which is often the fastest step.
• Closure: updating the ticket, notifying the requester, and recording what was done.

The action is minutes. Everything around it is hours, spread across days of waiting. That waiting is what breaks service levels.

What an AI Worker takes on

Kepler handles the surrounding steps automatically. It enriches every ticket with the requester's details and entitlements the moment it arrives, so an engineer who does pick it up starts with context rather than searching for it. For the repetitive categories, it runs the whole thing: policy check, approval request if needed, the change, and the closure note.

What that does to the numbers

Resolution time for automated categories drops from days to minutes, because there is no queue. Engineers' time shifts toward the tickets that need judgement. And the cost per ticket falls, because the expensive part, human attention, is spent only where it changes the outcome.

Start with one ticket type

The fastest path is not to automate everything. Pick the single most frequent request, prove it end to end with approvals in place, and expand from there. It is exactly how we run a Kepler pilot, and it is why the first results show inside the first month.