People use Copilot. Delivery has not improved.
You bought the licences and people use them, but cycle time, release frequency or quality has not moved as you expected. Adoption tells you that the tool is being used. It does not establish whether the tool improved the work or where any benefit was offset.
Compare the possible explanations
Coding may have become faster while work still waits for review, decisions or release. Extra output may have increased rework. The tool may also be poorly suited to the tasks, lack useful context or need better support for the people using it.
Flat delivery measures do not establish which explanation is right. AI can magnify an existing constraint, and it can introduce new work.
Follow a piece of work through the system
Compare similar work before and after adoption. Separate time spent coding from time waiting, reviewing and correcting. Check the quality of the result as well as how quickly it was produced.
Use that evidence to choose a focused change: improve tool use, remove a queue, address rework or test another explanation. Our opportunity discovery starts with one problem and leaves you with the findings.
For the broader argument, read Are We Still Pretending Coding Was the Bottleneck?. Compare its argument with the evidence from your own work.