I built this at my day job to drive adoption of an enterprise AI tool. The 20.5% time saving isn't a benchmark or a vendor claim — it came from UAT testing I ran. The tool lets account teams run the business case themselves.
This is day-job work, not a hypothetical. I was responsible for demonstrating the commercial value of an enterprise AI tool to drive adoption across large account teams.
Before anyone takes that conversation seriously, you need a number they trust. We ran a UAT programme, we measured the time savings, and we got 20.5%. I turned that into a calculator so any account team could run the business case themselves — no analyst needed.
We ran a structured UAT programme across a sample of users. They timed themselves on their standard tasks before and after using the tool. Average time saving: 20.5%.
That number didn't come from a whitepaper or a vendor study. It came from our own data, measured in our own environment. That's the only kind of number that holds up in a business case conversation — and it's the number baked into the model below.
Adjust the inputs for your organisation. The 20.5% time saving rate is fixed — that's the UAT result.
Time saving rate: 20.5% — average from structured UAT testing. Fixed.
Hours saved per employee/year: hours/week × 0.205 × 52 weeks.
Annual cost saving: hours saved × (annual salary ÷ 2,080 working hours) × number of users.
Working week: 40 hours assumed for hourly rate calculation.
Based on UAT data from a single programme. Actual time savings will vary by role and task type. Cost savings are indicative and based on loaded salary. Does not include tool licensing or implementation costs. All figures in GBP.
Whether you're from GitLab or just curious — I'm easy to reach.