Category
Quality & measurement in the AI era
Quality used to mean "no bugs." It can't anymore.
When AI multiplies the amount of code a team produces, the old definition of quality, code that passed its tests and its review, can no longer be verified by people reading and testing it. DORA's 2024 research found that AI adoption, while it helps individual productivity, is associated with lower software delivery stability. Quality now has to be built in at the prompt, at the review of the whole output, and in layers of testing that used to be too expensive to run.
The metric you use to judge people is the reason your data lies
When a metric is used to evaluate people or teams, they start protecting the number instead of the reality it was meant to describe. In AI-assisted teams this shows up as healthy-looking burndown charts built on commitments sized for pre-AI capacity. The fix is to measure the system rather than the people, and to use metrics to ask questions instead of to hand out grades.