Measuring AI's Real Impact on Your Engineering Team

Source: Dev.to

A few months back, the tech world got hit with a wave of panic‑inducing headlines. CEOs and tech leaders were on stages everywhere claiming that massive percentages of their code was now AI‑generated. If you weren’t on board, you were basically toast.

This kicked off what I can only describe as a spending frenzy. Companies started signing six‑ and seven‑figure contracts for AI coding tools, desperate not to fall behind. The question everyone was asking was simple: “How do we get our entire team using AI?”

Now? The conversation’s changing. The companies that jumped in early are starting to ask a much harder question: “Is this actually worth it?”

We’ve moved past the hype cycle into the messy reality of execution. Instead of “Are we using AI?” teams are asking “Are we using it well?” And here’s the problem: the metrics we’ve relied on for years to measure engineering productivity are completely inadequate for this new world.

Why Your Current Metrics Are Lying to You

Cycle Times That Mislead

Your cycle time might drop by 50 % and you’ll celebrate. But what’s really happening is that AI shortens the coding phase dramatically, while the code it generates is so convoluted that your review phase doubles in length. You haven’t actually gained anything—you’ve just moved the bottleneck elsewhere. Traditional tools can’t see this shift, so you end up celebrating a vanity metric while your team drowns in review friction.

DORA Metrics That Can’t Diagnose

Don’t get me wrong, DORA metrics are great for measuring overall pipeline health, but they’re too high‑level to tell you anything specific about AI’s impact. Your Change Failure Rate may go up, and DORA just shrugs. It can’t tell you whether that increase is due to poorly written AI prompts, bad code quality from your AI tool, or something completely unrelated to AI.

Lines of Code (Still Terrible)

Lines of code has always been a terrible metric, but AI makes it completely absurd. An AI tool can spit out thousands of lines in seconds. Measuring productivity this way is worse than useless because it actively rewards the wrong behavior.

A Better Framework for Measuring AI ROI

An engineering organization is fundamentally a system: you put things in (headcount, tools, cloud spend) and you get things out (a working product). A real framework for measuring AI ROI has to connect these inputs to outputs in a meaningful way.

Pillar 1: Measure Real Engineering Velocity

Start Simple – Track Basic Output

• Measure throughput: pull requests merged per week or issues resolved. This gives you a baseline of what your team is actually shipping.

Get More Sophisticated – Understand How Work Gets Done

• Track your AI Code Ratio, the percentage of merged code that came from AI.
• Analyze calendars to see if AI is actually freeing engineers for focused work or if they’re still spending all their time in meetings.

The Gold Standard – Segment by AI Usage

• Segment cycle‑time metrics by how much AI was used in each pull request. This lets you answer the most important question: “Are PRs with lots of AI‑generated code actually moving through our system faster than human‑written code?”

Pillar 2: Measure Quality and Maintainability

Start Simple – Track Your Change Failure Rate

• A lagging indicator that shows how many deployments are breaking things in production.

Get More Sophisticated – Look at Rework and Complexity

• Track your Rework Rate (percentage of code rewritten shortly after merge).
• Measure code complexity. Are AI‑heavy PRs more brittle than human‑written code?

The Gold Standard – Track Defects by AI Dosage

• Measure Defect Escape Rate for AI‑generated code versus human‑written code. You’ll usually need about 90 days post‑deployment to see meaningful patterns, but it gives a definitive answer on whether AI is improving or degrading customer experience.

Pillar 3: Measure Organizational Impact

Start Simple – Track Who’s Using the Tools

• Measure adoption: weekly active usage across the organization to see which teams are leaning in and which are holding back.

Get More Sophisticated – Measure Onboarding Speed

• Track “Time to 10th PR” for new engineers. Is AI actually helping them become productive members of the team sooner?

The Gold Standard – Assess the Talent Pipeline Risk

• AI automates many simple, repetitive tasks that used to be training ground for junior engineers. This creates a long‑term risk: are you eliminating the path from junior to senior engineer? Quantifying this is harder but critical for any serious ROI discussion.

Pillar 4: Measure Total Cost

Start Simple – Compare License Costs to Headcount

• Compare your annual spend on AI tools to what you’d pay for an additional engineer.

Get More Sophisticated – Track Token Usage

• Monitor token consumption. Which teams or engineers are power users? Where are you burning through credits the fastest?

The Gold Standard – Automate R&D Capitalization

• Use AI to automatically classify engineering work into categories like “New Feature,” “Maintenance,” or “Infrastructure.”
• This enables automated R&D cost capitalization reports for finance, turning engineering data into a strategic business asset.

Building the Right Culture Around Metrics

A framework doesn’t matter if your team doesn’t trust it. Engineering metrics can be incredibly valuable, but if you implement them poorly, you’ll just create fear and mistrust.

Be Transparent About What You’re Measuring

The “why” matters more than the “what.” Tell your team openly what you’re measuring and why. Frame it as a tool for finding and fixing systemic problems, not for micromanaging individuals.

Focus on Systems, Not People

Use metrics to understand the health of your development process, not to create a performance leaderboard. The question should always be about improving the system, not ranking engineers.