AI Opportunity Roadmap case study.
Three people 5 hours.
One engineer 20 minutes.
A UK service firm ran component reviews twice a week. Every shipped item needed sign-off from a developer, test analyst, and business analyst — five hours of skilled time, every cycle. An AI Opportunity Roadmap engagement reshaped the loop: one engineer working with AI agents, twenty minutes, same review depth, findings written straight to the backlog.
93%
Per item, against the original three-role loop.
£318.75
UK contractor mid-market day-rates, fully attributable.
£33,150
Contractor cost recovered at the client’s actual cadence.
The old loop had the right depth.
It didn’t have the right cost.
The team had to check every item shipped or revised for accessibility, structural quality, performance, and reusability. Then turn each finding into a properly-formed user story, groomed onto the development backlog. That review took three roles.
Developer
3hrs
Inspecting what shipped, the code behind it, and whether the foundations were sound.
Test Analyst
1hr
Validating that it worked for everyone, including edge cases.
Business Analyst
1hr
Writing up findings and shaping them into a piece of work the team could pick up.
5 hrs · £343.75
Three calendars to align. Days of lag between “we found something” and “the team can start fixing it.” The depth of the review wasn’t the problem. The cost of getting from review to action was.
One engineer.
One session.
Same review depth.
I designed it as a five-stage agent-driven workflow that one engineer can run end to end. It reproduces the quality of the three-role review and feeds every finding straight onto the backlog.
Scope
Item type, URL or supplied code, technical context, intended use.
SCOPE_DEFINITION_01Collect
Rendered output, source code, screenshots, behaviour notes.
EVIDENCE_GATHER_02Check
Accessibility, structural quality, performance, reusability.
REVIEW_AGENT_03Score
Severity, impact, evidence IDs, recommended fix per finding.
SCORING_RUBRIC_04Output
Decision snapshot, findings, roadmap, evidence appendix.
REPORT_OUTPUT_05A second AI step turns each recommendation into a properly-formed user story. That replaces the business analyst’s write-up entirely. The backlog is ready in the same session as the review.
Total Time per Item 20 minutes
What stayed the same.
Review depth.
I designed the new workflow to reproduce all four review domains the original process covered. Every claim ties back to captured evidence.
Accessibility
That the component works for everyone, including keyboard and screen-reader users.
Structural Quality
That the foundations are right and the component is machine-readable.
Performance
That it loads fast, scales to mobile, and degrades gracefully on slow networks.
Reusability
That it fits the wider system without duplicating work other teams have already done.
Every severity, impact, and recommended fix is tied to an evidence ID: selector, count, snippet, screenshot, or test result. The defensibility of the findings sits in that evidence trail. The defensibility of the time saved sits in the comparison below.
The bottleneck was never the review itself. The skilled people involved did careful, thorough work in the time they had. The cost was in the coordination — three calendars to align, the write-up handed off, the story groomed in another session. Once the workflow ran end to end in one tool, those steps collapsed.
What actually changed.
Each review returns
most of a working afternoon.
Each item: 4h 40m and £318.75 back, every time the workflow runs.
4h 40m
Recovered skilled-engineer time per review, back into delivery work.
£318.75
Difference between old three-role cost and new single-engineer cost.
93%
Identical reduction figure across time and cost, by construction.
| Measure | Old loop | New loop | Saving | Reduction |
|---|---|---|---|---|
| Time | 5 hours | 20 minutes | 4 hrs 40 min | 93% |
| Cost | £343.75 | £25.00 | £318.75 | 93% |
The per-item saving scales linearly.
The cadence sets the size of the prize.
Annual Savings — by Review Cadence
At two reviews per week the saving compounds to 486 engineer-hours and £33,150 of contractor cost recovered per year. Lower cadences scale linearly. The actual figure for any firm is whatever the real review cadence is, multiplied by £318.75 per review.
Method & measurement.
Showing the working.
Day-rates are conservative UK contractor mid-market figures. They can be stress-tested with internal cost bases without changing the 93% reduction.
| Role | Day-rate | Hourly rate | Hours per item | Cost per item |
|---|---|---|---|---|
| Old loop — three roles | ||||
| Developer | £600 | £75.00 | 3.0 | £225.00 |
| Test analyst | £450 | £56.25 | 1.0 | £56.25 |
| Business analyst | £500 | £62.50 | 1.0 | £62.50 |
| Old loop total | — | — | 5.0 hrs | £343.75 |
| New loop — single engineer | ||||
| Engineer | £600 | £75.00 | 0.33 | £25.00 |
| New loop total | — | — | 0.33 hrs | £25.00 |
Old cost · vs · new cost
= £318.75 saved per item
93%
Savings scale linearly with review cadence. Substitute the actual cadence to refine.
Internal fully-loaded costs typically lower the hourly figure by 30–40% — but don’t change the 93% reduction.
Defensibility of findings is preserved by the evidence-ID trail. Defensibility of time saved is the before-and-after comparison.
Single-digit pence per review at current model pricing. Excluded from the headline; including them moves the saving by under 0.1%.
Where this pattern works.
In any firm.
Most service firms I’ve worked with have at least one process where multi-role coordination is doing the job a structured AI workflow could do in a fraction of the time. I’ve seen it as a code review, a content audit, a compliance pass, a quality check that involves the same three people every time. The component-review shape is one variant.
A recurring review cycle
Anything checked and signed off on a regular cadence.
Multiple skilled people per pass
Cost compounds with calendars.
A handoff lag between finding and action
Review, write-up, grooming, ready for work.
An evidence trail that depends on who’s running it
Defensibility varies with the reviewer.
486 engineer hours and £33,150 of contractor cost back every year, with findings ready for action in the same session as the review.
Want to find this kind of saving
in your operations?
The AI Opportunity Roadmap is how I do this kind of work for any firm — a 3–4 week engagement built on the Sprint Method. I map where AI should already be doing the work, cost every opportunity in recovered hours and recovered cash, and give you a sequenced 90-day plan for the order to do them in.