Sample ReportIllustrative sample

What an IT Support Performance Audit looks like.

This is an illustrative example built from representative data — not a real company or a customer result. It shows the structure, depth and tone of the report you receive.

Example internal IT team

220 employees

Team size

4-person IT team

Monthly demand

~850 tickets / month

Executive summary

The service is responding quickly but resolving slowly.

Headline metrics look healthy — first response is fast and SLAs are largely met. Underneath, though, end-to-end resolution is slow, driven by a concentration of ageing tickets in a few categories, a high volume of repeat access requests handled manually, and reporting that measures acknowledgement rather than progress. The platform isn't the constraint here; the operating model is. The changes below are sequenced to reduce demand and shorten resolution without replacing any tooling.

Priority findings

01

Repeat access demand is consuming analyst capacity

Evidence

A high proportion of monthly demand relates to recurring software and access requests — the same handful of request types, over and over, each handled manually.

Recommendation

Move high-volume access requests into structured service catalogue workflows and automate appropriate approval and routing steps.

02

Backlog ageing is concentrated, not service-wide

Evidence

A small number of ticket categories account for a disproportionate share of tickets older than 14 days. The backlog isn't everywhere — it's parked in a few specific queues.

Recommendation

Create category-level ownership and a weekly ageing review focused on the highest-contributing queues.

03

First response metrics are masking resolution delay

Evidence

Tickets receive a fast initial acknowledgement, so first-response SLAs look healthy — but there are long gaps between meaningful analyst actions afterwards.

Recommendation

Add work-in-progress and ageing measures alongside first-response SLA reporting, so end-to-end delay becomes visible.

04

Manual ticket routing creates avoidable delay

Evidence

Predictable, high-volume demand is still routed by hand, adding a first-touch delay before work even reaches the right person.

Recommendation

Standardise categorisation and introduce routing rules for predictable high-volume demand.

Priority matrix

Where to spend effort first.

High impact · lower effort

  • Backlog ageing review
  • Routing rules

High impact · higher effort

  • Access request workflow redesign

Medium impact

  • Reporting redesign

90-day roadmap

A practical plan, sequenced.

Days 1–30

Stabilise

  • Introduce an ageing review
  • Establish category ownership
  • Implement basic routing quick wins
  • Update service reporting

Days 31–60

Reduce demand

  • Identify the top repeat requests
  • Redesign the highest-volume request workflows
  • Improve knowledge coverage

Days 61–90

Automate and measure

  • Implement automation candidates
  • Introduce improvement metrics
  • Establish a monthly service improvement review

This is what you'd get for your own service.

Your report is built from your service's real evidence — ranked findings, the numbers behind them, and a 90-day plan you own. £995, delivered within 7 days of receiving the agreed audit evidence.