AI coding tools tend to arrive gradually and then all at once. One engineer starts using an agent for a feature, another uses it to build an internal tool, and soon the team is producing more software without having made an explicit decision to do so. The first instinct is often to focus on which AI tools people are using. The more useful question is what happens to everything they create.
Start by looking at the path from working code to running software. How many decisions have to be made before something can reach production? Who creates the environment, connects the database, configures networking, handles credentials and decides how the application will recover? If those steps depend on a small number of people, faster coding will eventually produce a longer queue for them.
The answer is not to automate every existing step individually. Decide instead which decisions genuinely need to remain decisions. Security boundaries and organizational policy probably do. Choosing how to configure the same infrastructure for the hundredth application probably does not. A good path to production removes repeated choices while preserving the ones that matter.
The goal is simple: more software should not require proportionally more operational work.
If your team is adopting AI to increase what developers can create, look beyond the coding tool. The real measure of productivity is how easily that work becomes software other people can depend on.