When AI Metrics Go Sideways: The Hidden Gap Between Productivity and Impact 🎯
At the recent Engineering Leadership Conference (ELC), I hosted a roundtable about “Defining Outcome-Oriented Metrics for AI Features.”
Now, the intent was clear: to explore how we measure the real-world impact of AI-powered features: those predictive, adaptive capabilities inside products. Think of Gmail’s Smart Compose or Spotify’s recommendation engine. Features where success means measurable improvements in user experience, not just clever code.
But like all good roundtables, this one took on a life of its own. Within minutes, the discussion naturally and passionately pivoted toward something else entirely: how to measure productivity and impact from AI-assisted development using tools such as GitHub Copilot, Cursor, and Bedrock.
And honestly? That pivot itself was the story.
A Collective Reality Check
The shift revealed an industry-wide tension that almost every leader in the room felt: the enormous, often unspoken pressure to prove that AI is improving engineering productivity.
One engineering director admitted, half-jokingly, “Our velocity jumped 10x in Jellyfish since rolling out Copilot, until we looked closer and realized the code quality hadn’t budged.”
Another added, “I’m being asked if AI has made my team ten times faster, but what does that even mean?”
You could feel the shared exasperation. Everyone’s trying to measure progress, but often with the wrong ruler.
We’re still using activity metrics such as lines of code, commits, and test coverage to justify strategic outcomes like innovation, time to market, and user value. It’s like judging marathon performance by how many steps your smartwatch counted.
The Productivity Mirage
Teams everywhere are deploying AI tools with the hope, or sometimes the expectation, of exponential productivity gains.
But AI productivity isn’t linear or universal. Junior engineers often see huge gains, while senior engineers slow down, spending more time validating AI output, rewriting suggestions, or debugging unpredictable code.
One participant put it bluntly:
“AI doesn’t always save time, it just shifts where you spend it.”
And that’s the crux. Measuring AI success solely through speed or commit counts misses the bigger picture.
Instead of asking, “How much code did AI help us write?” we should ask, “How much better did it make what we deliver?”
Reframing the Question: From Output to Outcome
As the group unpacked this tension, a common theme emerged: measurement should start from the top.
If your goal is to improve QA velocity, measure defect leakage reduction, not test case count.
If AI agents are speeding up incident resolution, tie it to revenue protection or uptime improvements.
This isn’t about measuring the existence of AI activity, it’s about measuring the difference it makes.
The best insights came when leaders shared how they built translation layers between engineering metrics and business value. For example, connecting mean time to resolution (MTTR) to uptime, and uptime to customer churn or retention.
That’s the connective tissue most organizations still lack.
The Before and After Principle
A recurring takeaway was simple: you can’t measure impact without a baseline.
One engineering manager described how her team captured pre-AI sprint velocity and bug rates as a baseline before rolling out AI tools. Only with that context could they assess whether improvements were real or just perceived.
In contrast, another leader confessed their dashboards looked impressive but lacked any pre-AI data, making it impossible to tell if productivity actually improved.
Baseline data is the unsung hero of outcome measurement. It’s the only way to distinguish hype from progress.
AI as a Force Multiplier, Not a Magic Wand
Another important nuance emerged: AI’s job isn’t to replace talent, it’s to amplify it.
One leader described AI as a force multiplier that helps teams close resource gaps or automate repetitive work such as documentation, backlog triage, or QA summaries.
But a healthy reminder echoed around the table: if the validation cost of AI output outweighs the time saved, it’s not multiplication, it’s division.
Shared Ownership of AI Success
Perhaps the most mature takeaway from the discussion was that AI success cannot sit solely with engineering.
It requires a shared framework across Product, Ops, and Finance so that AI outcomes align with business metrics like churn, onboarding time, or customer satisfaction.
If one team measures commits and another measures revenue, no amount of AI will bridge that disconnect.
As someone summarized it perfectly:
“The goal isn’t to prove that AI works, it’s to prove that it matters.”
The Real Lesson Behind the Shift
In hindsight, that early topic detour was actually a mirror for where most tech organizations stand today.
We’re still learning to connect the dots between AI tools that change how we work and AI features that change what we deliver. The fact that the group gravitated toward the first proves how pressing that challenge is.
And it wasn’t a misunderstanding, it was a reflection of where the real organizational pain lives right now.
Closing Thoughts
AI has already transformed our workflows. What it hasn’t yet transformed is how we measure success.
Until we stop chasing vanity metrics and start defining value in business terms, we’ll keep confusing movement with momentum.
So maybe this roundtable didn’t go off-topic after all. Maybe it went exactly where it needed to. 💡
Because the ultimate metric of progress isn’t how many AI tools we use, it’s how intelligently we measure what they change.
Let’s keep this conversation going. If there’s one thing this roundtable proved, it’s that we’re all navigating this terrain together, trying to separate AI reality from AI myth.
P.S. If you’re interested in diving deeper into outcome-oriented metrics for the AI features you’re building, not just using, I’d love to continue that discussion. Maybe that’s the next roundtable! 😊
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✅ Originally discussed at ELC 2025. Adapted reflections from the roundtable “Defining Outcome-Oriented Metrics for AI Features.”
