An illustrative example built from representative data — not a real company or customer result. It shows the four sections of the report and, more importantly, the interpretation that comes with each.
Example internal IT team
220 employees
Team size
4-person IT team
Monthly demand
~560 tickets / month
Executive summary
Ticket volume has risen 45% over twelve months while the team has stayed at four people. First response is fast and mostly inside SLA, but resolution is slow and SLA breaches have climbed from 11% to 18%. The root cause isn't effort — it's a small number of high-volume request types being handled manually, poor categorisation hiding the real demand, and a backlog concentrated in three queues. None of it needs a new platform. The 90-day plan below targets the demand and the routing first, where the return is fastest.
Where your time is going
IllustrativeMonthly ticket volume
Interpretation
Demand rose 45% year-on-year with no added headcount, and the growth isn't evenly spread: access and password requests drove two-thirds of the increase. That's not a "work harder" problem — it's a demand-shape problem, and demand-shape problems are fixable without hiring.
What's breaking
Illustrative18%
of tickets breached their SLA last quarter, up from 11%
22%
of resolved tickets were reopened at least once
71%
of tickets older than 14 days sit in just 3 categories
31%
of P2 incidents were touched by 3+ teams before resolution
Behind those headline numbers are the findings that only appear when you join data together — each one ending in hours or money, not just an observation:
“Password & MFA reset” was logged 312 times last quarter — your highest driver — with no knowledge article covering it. At a median 9 minutes each, that’s ~47 agent-hours a quarter, and no route to self-service.
“Access requests” take a median 4.2 days to close, but only 3 hours of that is active work — the rest is spent awaiting the requester with no automated chase. A reminder would recover ~60 hours of elapsed time a month.
Why it's happening
IllustrativeSection 3 works through all seven dimensions. Here's one, in the score → findings → cost format the whole section uses.
Findings
What it's costing you
Reporting built on this data understates your real top demand drivers, so staffing and automation decisions are made against the wrong picture. The 6 mis-logged request types alone account for roughly 93 analyst-hours a month of manual handling — around half an FTE.
Your 90-day plan
IllustrativeHigh impact · lower effort
High impact · higher effort
Medium impact
Days 1–30
Stabilise
Days 31–60
Reduce demand
Days 61–90
Automate and measure
The codes against each action (F1, F7, …) trace every recommendation back to a numbered finding earlier in the report. If an action has no finding behind it, it doesn't make the plan.
Illustrative example only. Figures are representative of typical 50–500-employee service desk operations, not a real customer. Your report contains your own numbers.
Your report is built from your own service desk data — trends, root causes, and a 90-day plan you own. £995, delivered inside a week.