AI is breaking our engineering metrics faster than we can adapt — what we found auditing our own platform
Hey r/engineeringmanagers,
I'm in the Product Operations team at Odevo (European property-tech group). For the last six months we've been building a product and engineering metrics platform — DORA, Product Flow, Code Quality, the usual stack. After auditing what we actually have, the most striking finding wasn't the mess underneath (90+ Jira statuses, 15 issue types, inconsistent incident logging).
It was what AI is doing to the substrate. One team in our audit had clean inputs by every governance measure. Their cycle-time chart still looked like noise because the unit of work had collapsed to minutes — entire stories shipping in an afternoon.
Wrote about it on Medium: [Medium link] Curious whether you're seeing the same in your own teams — and how you're thinking about flow metrics in a portfolio where AI adoption is uneven.