How AI triage cut ticket resolution time 40% at a 200-seat healthcare org
Same team, same tools, 40% faster resolution — by changing what happens in the first 90 seconds of a ticket's life.
A 200-seat healthcare organization came to us with a familiar profile: a competent two-person internal IT team, a ticket queue that never emptied, and clinicians who had learned to work around problems rather than report them. Average resolution time was just over four hours. Six months later it was under 2.5 — a 40% reduction with the same team.
What changed
Nothing about the people or the ticketing tool. What changed was the first 90 seconds of every ticket. AiTOps now reads each incoming request, classifies it, attaches the user’s device state and recent changes, and either resolves it (password resets, MFA re-enrollment, printer mapping, mailbox quota) or routes it to the right person with the investigation already done.
The numbers
- 38% of tickets closed by automation with no human touch.
- Median first response dropped from 47 minutes to under 2.
- Escalations to the internal team arrived with device, user, and change context attached — engineers reported spending “almost no time asking what happened.”
- Recurring issues flagged by the AI were root-caused; three chronic ticket types disappeared entirely.
What the internal team did with the time
Finished the Intune migration that had been stalled for a year, completed the HIPAA control evidence that had been a quarterly scramble, and started an EHR optimization project the clinicians had been requesting. That’s the actual return on AI triage: not fewer people, but senior people doing senior work.
