Artificial intelligence can draft code, explain a repository, suggest tests, review changes and automate repetitive engineering steps. Its value depends on how well those outputs fit the actual codebase and move through the team's normal delivery process.
Useful adoption should be measured through reviewed and validated outcomes, not model activity alone. Tests, maintainability, customer impact and safe deployment determine whether generated work improves engineering rather than simply increasing output volume.
