Is Your AI Coding Assistant Paying Off? How to Measure It

Two numbers show up in almost every vendor dashboard, and both are weak evidence.

Acceptance rate tells you how often developers press Tab. A developer can accept a suggestion and rewrite it a minute later, or accept boilerplate that would have taken five seconds to type. High acceptance may mean the tool is good at autocomplete, not that work finished sooner.

Lines of code is worse. More code is not a goal, and assistants make it cheap to produce more of it. A team that ships the same feature in twice the lines has a maintenance problem, not a productivity gain. Use both metrics only to see who is using the tool, never to judge whether it's working.

Choose a small set that spans speed, quality and experience:

• Pull request cycle time: from first commit or PR open to merge. Split it into coding time and review wait time, since assistants may speed up one and slow the other.
• Review load: comments per PR and reviewer hours. If PRs get larger and reviews get slower, the gain is partly moved, not saved.
• Rework and defects: reverts, follow-up fixes within a couple of weeks, and change failure rate.
• Lead time for a typical task type, such as a small bug fix or adding an endpoint.
• Developer survey: a short monthly question set on time saved, trust in suggestions and where the tool gets in the way.

The first four you likely already collect from your repository and deployment tooling. Avoid ranking individuals on any of them.

Follow these steps:

1. Capture two to three months of baseline data before rollout, so you know your normal variance.
2. Roll out to a group in stages, for example half the team first, and keep the rest as a comparison. Match groups by experience and the kind of work.
3. Hold for at least one full release cycle. First weeks are distorted by novelty and by learning to prompt.
4. Compare the metrics above, and read a sample of PRs from both groups, not just the numbers.
5. Repeat the survey to capture what the metrics miss.

If a controlled split isn't possible, a before-and-after comparison still works. Just note anything else that changed in the same period: new hires, a big refactor, a release freeze.

Do the arithmetic with your own numbers. Say a seat costs a few tens of dollars per developer per month and your fully loaded engineering cost is about $90 an hour. If the tool saves each developer even an hour a month of verified time, the seat pays for itself on paper. The harder question is whether that saved time is real once you count extra review, debugging of plausible-but-wrong code and time spent prompting.

Treat a survey answer such as "I save two hours a week" as a hypothesis and check it against cycle time. Where the two disagree, believe the delivery data and ask why. Confirm current pricing on the vendor's site, since plans change.

Watch for these:

• Cycle time drops but change failure rate or reverts rise. You're shipping faster and fixing more.
• Junior engineers show big gains while seniors slow down on review. That may be fine, but it should be a decision, not an accident.
• Usage concentrates in a few enthusiasts. Their numbers don't predict the team's.
• Gains vanish on complex work. Assistants tend to help most on well-understood, repetitive tasks.

Tie the findings back to your assistant policy and your review standards, and revisit the measurement each quarter as tools change.