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If you're still measuring engineering success with the same metrics you were using five years ago, you might be tracking the wrong things. Velocity, DORA stats, and PR count have long been the gold standard for tracking performance, but with AI transforming engineering workflows, those metrics might not be telling the full story anymore.
That’s exactly what we tackled in our recent webinar, “Engineering Team Metrics That Actually Matter in 2025.” I was joined by:
- Julian Ramirez, Senior Engineering Manager at Dropbox
- Pedro Tabio, Co-Founder & CTO at Vibrant
- Ryan Cooley, Director of Engineering at ProcessMaker
Together, we explored which KPIs actually help engineering teams succeed today, how AI is reshaping productivity tracking, and the balance between speed, quality, and developer happiness. Here are the biggest takeaways.
1. DORA & Velocity Still Matter—But Only If You Use Them Right
Despite ongoing debates, DORA metrics and velocity haven’t become obsolete—but they need to be used in the right context. For high-performing teams, raw speed doesn’t equal success unless it’s tied to delivering customer value.
One major shift is measuring time to value instead of just deployment frequency. Engineering teams are realizing that shipping fast isn’t enough—it’s about ensuring new features drive impact quickly. A team can hit all of its deployment goals but still fall short if customers struggle to adopt what’s being shipped.
For mobile teams, the traditional DORA model often needs adjustments due to app store review cycles and fixed release cadences. Instead of forcing rigid frameworks, successful teams customize their KPIs to reflect the realities of their tech stack and release process.
💡 Key Takeaway: Traditional engineering metrics can still be useful, but they need context and alignment with actual business impact.
2. AI is Forcing a Rethink of Engineering Productivity
One of the biggest shifts happening right now is the rise of AI-assisted development—and the question of how to track productivity when AI is doing more of the coding.
Instead of measuring individual developer output, leading teams are focusing on collaboration and problem-solving. If AI can generate large portions of code, then the best engineers aren’t the ones writing the most lines of code—they’re the ones asking the right questions and structuring solutions effectively.
This raises an interesting question: Should we start tracking DORA for AI agents the same way we track human engineers? If AI coding assistants are increasingly integrated into workflows, shouldn’t they be evaluated on cycle times, failure rates, and velocity? It’s a conversation that’s just beginning, but one that engineering leaders need to prepare for.
💡 Key Takeaway: AI is changing how teams measure productivity, shifting the focus from individual coding speed to team collaboration, problem-solving, and orchestration of AI-driven development.
3. The Trade-Off: Speed vs. Quality vs. Developer Happiness
Every engineering team faces the challenge of balancing speed, quality, and developer happiness—and the best teams know that optimizing for one at the expense of the others is a losing game.
Teams that over-index on speed risk accumulating tech debt and frustrating engineers who have to clean up the mess later. On the flip side, teams that are too focused on perfection often move too slowly, missing market opportunities and delaying value delivery.
One of the most overlooked investments? Developer experience. A well-equipped, small engineering team with great tooling and efficient workflows will always outperform a larger, slower-moving team bogged down by inefficient processes.
💡 Key Takeaway: The best engineering teams invest in developer experience as a way to increase both speed and quality—without burning out their engineers.
4. What Engineering Metrics Need to End in 2025?
Some metrics simply don’t make sense anymore—especially in an AI-augmented world. Lines of code are at the top of that list. With AI able to generate thousands of lines in seconds, this metric has become completely meaningless.
Another outdated approach? Tracking velocity as an individual performance metric. Engineering is a team sport, and rewarding developers based on personal PR count or story points encourages gaming the system rather than driving real impact.
💡 Key Takeaway: The best metrics aren’t about activity—they’re about outcomes. If a metric isn’t helping your team make better decisions, it’s time to drop it.
Final Thoughts: What Should You Measure in 2025?
The best engineering leaders track impact, not just activity. That means:
✅ Measuring time to value, not just deployment speed
✅ Tracking collaboration and problem-solving, not just code output
✅ Using AI-assisted productivity metrics, but not relying on them blindly
Engineering success is evolving, and so should the way we measure it. If you’re still using the same metrics you were five years ago, it’s time to rethink what actually matters.
Missed the webinar? Watch the full replay here.
What engineering team metrics are you tracking in 2025?
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