Measuring AI in Engineering: What Leaders Need to Know About Productivity, Risk and ROI

AI is everywhere right now. Every week there’s a new coding assistant, a new tool that promises to make engineers “10x faster”. GitHub Copilot, Cursor, ChatGPT, it feels like they’re writing half the internet’s code already.

And look, some of this is amazing. I’ve used these tools myself. They save time on boring stuff. But here’s what I’ve noticed talking to engineering leaders over the past year: most of them can’t actually tell me if AI is making their teams more productive.

It’s all gut feeling. “Yeah, it feels like we’re shipping faster.” Feels. Not data.

Faster Code Does Not Mean Faster Delivery

Writing code quicker doesn’t mean you’re delivering value faster. If AI spits out 50 lines of code that need hours of cleanup and review, that’s not saving time. If deployments still get stuck because nobody’s reviewing PRs or automated tests are flaky, you haven’t solved anything, you’ve just made the to-do list longer.

We’ve seen this before. New tools come in, teams feel busy and excited, but nothing meaningful changes. AI is no different if you don’t measure it properly.

The Only Metrics That Matter

Forget giant dashboards. Forget vanity stats. There are just three things you should be looking at:

• Cycle time: How long does it take to get code from a developer’s machine into production? If AI’s helping, this number should drop.

• Deployment frequency: Are you shipping more often, with fewer delays?

• How your developers actually feel: Are these tools making their jobs easier, or are they grumbling about fixing AI-generated messes?

“If you can’t answer those questions with numbers and honest feedback, you’re guessing,” says Alex Circei, CEO of Waydev. “And guessing is not leadership.”

The Messy Part Nobody Talks About

Here’s what most blog posts skip over: adopting AI is messy.

Some developers love it. Others hate it. A few learn how to use it well, and suddenly they’re the only ones who understand half the new code. That’s how information gaps form.

AI code suggestions also aren’t perfect. Sometimes they’re brilliant. Sometimes they’re flat-out wrong. You end up spending as much time fixing as you save.

And yes, there’s tension. Some engineers wonder quietly if AI is here to replace them. Ignore that, and adoption stalls.

The only way through is to talk about it openly. Document AI-generated changes. Review them carefully. Set boundaries for what should and shouldn’t be automated. And keep your team involved in shaping how AI fits into their workflow.

Why We Built AI Adoption Metrics

After seeing this pattern too many times, we built AI Adoption metrics into Waydev. It’s simple: we look at the actual work happening in your repos and pipelines. Commits, pull requests, deployments: no guesswork.

We wanted leaders to finally answer questions like:

• Are cycle times shorter than before?
• Are you shipping updates more often without breaking things?
• Is code quality holding up while AI tools are in use?

“I kept seeing companies buy AI tools and celebrate adoption without proof.” Circei says. “With these metrics, you can finally see if AI is speeding you up or just adding noise.”

What Success Really Looks Like

The best results we’ve seen aren’t teams bragging about writing 10,000 lines of AI-generated code. They’re teams quietly shipping updates faster. Reviews don’t drag on. Releases don’t break production. Developers say they can focus on harder problems because the boring work is automated.

That’s real success. And it only shows up if you measure it.

The Bottom Line

AI can be a huge win for engineering teams, but it’s not magic. If you want it to work, you have to:

• Measure what’s actually changing.
• Face the messy parts of adoption.
• Support your developers as they adapt.

Do that, and you’ll see the payoff: faster releases, cleaner code, and happier engineers. Skip it, and you’ll just have another shiny tool nobody knows how to measure.