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Software Strategy

Measuring Whether AI Tooling Improved Delivery

Adoption is not impact. Which delivery measures move if the tooling is working, and why lines of code is the worst possible metric.

Updated 2 min readBy SpiderHunts Technologies

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Quick answer — TL;DR

Measure lead time from start to production, change failure rate and how long review takes. If those have not moved, the tooling changed how work feels rather than how fast it reaches users.

The short answer

The question is whether work reaches production faster without more of it breaking. Those two together are the test, and they are measurable from data you already have in version control and your issue tracker.

Volume measures answer a different question and reward the wrong behaviour.

What to measure

MeasureWhat it tells you
Lead time, start to productionWhether work actually flows faster
Review turnaroundWhether the constraint moved to review
Change failure rateWhether speed cost quality
Time to restore after a failureWhether problems are found quickly
Rework rateWhether first attempts are landing

The second row is the one that catches the common outcome. Faster writing and slower review can leave lead time unchanged while everyone feels busier.

What not to measure

  • Lines of code, which rewards verbosity
  • Commits, which rewards splitting work
  • Suggestions accepted, which measures the tool not the outcome
  • Licences purchased, which measures spending
  • Self-reported time saved, which is unreliable in both directions

The third is the one vendors report and it tells you about adoption rather than about delivery.

Establish a baseline first

  1. Pull lead time and failure rate for the period before adoption.
  2. Note anything else that changed at the same time, such as team size.
  3. Give it long enough that a learning period does not dominate.
  4. Compare like work, since a quarter of migration work is not a quarter of feature work.
  5. Accept that attribution is imperfect and say so.

Without step one, any later number is uninterpretable. Teams routinely adopt tooling and then discover they cannot say whether it helped.

Ask the team as well

Quantitative measures miss things that matter: whether the unpleasant work got easier, whether people spend less time stuck, whether onboarding is quicker.

Those are legitimate benefits even when lead time is unchanged. They are also worth naming honestly as what you got, rather than claiming a throughput improvement the data does not show.

FAQ

Frequently asked questions

The questions readers ask us after this guide.

Still have a question?

Ask us directly — a senior engineer will get back to you.

Ask about your project

How long before we can judge?

Long enough to get past the learning period and to gather enough completed work to compare, which for most teams is months rather than weeks.

Is lines of code ever useful?

As a measure of productivity, no. It rewards exactly the wrong behaviour.

What if lead time has not improved?

Look at where the time goes. Usually the constraint moved to review or to decisions, which is useful to know and fixable.

Should we survey the team?

Yes, alongside the data. Some real benefits do not show up in delivery metrics, and it is better to name them honestly.

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