AI Adoption: Measuring Feature Development
Let's assume your team has already completed AI adoption and now management expects to see positive business results.
The good news is that AI doesn't magically change your business metrics. You don't need to invent new KPIs. You just need to track how your existing ones change (or finally start collecting them).
I suggest focusing on quality metrics first, then gradually shift toward delivery speed and cost optimization.
Since I lead platform engineering teams, we have two major types of work: new features development and L4 support. These activities are different, so they should be measured differently. Let's start with product development.
What can be measured:
🔸 Team Budget. The cost of the team in $, mandays, or FTEs.
🔸 AI Cost ($). How much does your team spend on AI? Those jokes about "it being cheaper to hire a junior" may stop being jokes soon. :)
🔸 Bug Density (defects/LOE). AI helps us deliver more code, but it can also introduce more defects. The first goal is to make sure quality doesn't get worse.
🔸 Test Coverage (unit, integration, E2E). AI is very good at writing tests. Increasing test coverage across different levels is usually one of the easiest wins.
🔸 % of Toil Budget. How much engineering effort goes into routine work such as CI maintenance, vulnerability fixes, upgrades, and similar operational tasks? AI should gradually reduce this type of work.
🔸 Feature Delivery Rate (per sprint, release, or quarter). I'm personally skeptical about this metric. In R&D, one feature may take two days while another takes two months, so averages often tell you nothing. But for some teams it can be a useful indicator.
🔸 Time to Market. The average time to implement and deliver a feature. This is also difficult to measure in R&D, but it can be useful for teams who develop customer-facing features.
So don't focus on measuring AI. Measure the results of your work: cost, quality, and time against your baseline (baseline is the metrics value before AI adoption). If you want to show the value of AI in the future, you need to start collecting those metrics today.
#ai #engineering #ai4sdlc
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2August 4, 2026 167 1
