Effective AI-assisted development starts with the workflow around the model: the context it receives, the tools it can call, the checks that run automatically, and the points where humans review the result.
Inside large organizations, that workflow has to account for governance, cost, security, and team adoption. It should compress routine implementation work and make the review surface easier to inspect and measure.
The practical test is whether the workflow helps teams ship reliable software faster. Useful measures include cycle time, review burden, defect rate, and how often the automation makes a human decision easier.