Most engineering teams have now tried tools like Cursor, Claude, Copilot, or similar. Getting people to use them is usually not the hard part. The harder question is whether they are actually improving how the team works.
In many organizations, usage goes up quickly. But the impact is uneven. Some developers become much faster, while others barely change how they work. Some teams ship more code, but review load increases. Some metrics look better, but the bottlenecks simply move somewhere else.
That is the part leaders need to understand.
- Where is AI creating real capacity?
- Where is it just creating more code to review?
- Where are a few strong users pulling the average up?
- And where are teams getting faster versus simply adding more pressure to the system?
At Deventura, we help engineering leaders answer these questions using Git data. We show how AI tools are affecting contribution patterns, review load, code complexity, and team capacity. The point is not to only measure AI usage, but to understand whether AI is actually having an impact on output.
The teams that get the most value from AI will not be the ones with the highest adoption numbers. They will be the ones that can see what is working, what is not, and where to improve next.
