IT service desk AI effectiveness & readiness assessment
A hands-on assessment of whether your service desk's data and processes can support AI, and what gaps to fill if not. If you've already deployed AI, it tells you whether it's underperforming, and why.
The same issue logged five different ways means tickets don't route right the first time. This is the costliest pattern in service desk data: roughly 1 in 8 tickets get reassigned 2+ times, burning 8.5 extra hours and tanking satisfaction.
13% of tickets drive 80% of lost productivity. If you can't see which 13%, you're fighting the wrong fires.
Teams with structured escalation resolve on first contact 27 points higher than teams without.
If a ticket only makes sense to whoever wrote it, no one else can work it — and AI definitely can't learn from it.
The root cause behind the two problems above. Missing fields, thin notes: AI has nothing to learn "resolved" from.
Enough consistent signal for automation to help, instead of scaling the existing chaos.
Across the six areas above, so you know exactly where you stand.
If you haven't adopted AI yet, this is your starting order. If you already have, this is why it isn't paying off.
Is it underperforming because the tool is bad, or because the data feeding it is bad? If you haven't turned AI on yet, this tells you what to fix first.
A call to align on how the assessment works and what's expected from both sides.
Your ticket data is analyzed against the six areas above, with follow-up if anything needs clarifying.
A scorecard and a ranked list of what to fix, reviewed together on a call.
Three weeks, start to finish.