AI Value Dashboard
Is AI actually creating value? Your latest ROI estimate, adoption, engineering delivery and developer experience results in one report, with a verdict and the gaps between what was assumed and what was measured.
How the measures connect
1
AI investment
API spend, licences, infrastructure, implementation and maintenance.
2
Adoption
Access, activation, active use, retention and workflow integration.
3
Workflow change
What work has actually changed, with a recorded baseline.
4
Productivity
Delivery, quality and developer experience measured together.
5
Business value
Capacity, cost reduction, net value, ROI and payback, clearly marked by confidence.
Every figure shows its formula, source, period and limitations. Read the methodology
Questions
How do I prove the value of AI to executives?
Show four things together: the business case (ROI and payback), adoption (how many people use AI weekly), a measured outcome (such as delivery speed in your repositories) and how the people using it rate it. Then show where the business case's assumptions differ from what you measured. A single ROI number without that evidence is rarely convincing.
What is the difference between estimated and measured AI value?
An ROI estimate rests on assumptions such as adoption and minutes saved per task. Measured value comes from data: usage exports, repository metrics and surveys. This dashboard labels each result as an estimate, observed or self-reported, and flags where the estimate is more optimistic than the evidence.
How do I share the report without uploading data?
The share link carries the report inside the link itself, in the part after the # sign, which browsers never send to a server. Anyone with the link sees the numbers, so share it like you would a document.
How often should I re-measure AI value?
Quarterly is a good rhythm, and before any funding or renewal decision. The dashboard flags results older than 90 days.