Picture this: you’re staring at a dashboard full of charts. Velocity is up, tickets are closing fast, lines of code are piling up. Everything looks great. But the product still isn’t moving the needle for the business, customers are frustrated, and your team looks burned out.

‍

That’s the trap. Traditional engineering metrics like velocity, lines of code, or tickets closed measure activity but they rarely capture impact. You can ship more code, faster, and still be building the wrong thing.

‍

The best engineering leaders know this. They’re moving past vanity metrics and focusing on what really matters: outcomes for the business and health for the team. Metrics, when used well, become conversation starters — not performance hammers.

‍

This post explores that shift, drawing on insights from engineering leaders who’ve redefined how they measure success in the AI era.

‍

Why Old Metrics Fail

‍

The Velocity/LoC Fallacy

‍

Velocity and lines of code are tempting. They give you neat, trackable numbers. But let’s be honest: they don’t actually tell you if your team is winning.

‍

Pedro Tabio, Co-Founder & CTO at Vibrant, put it bluntly: these metrics are often “meaningless in isolation.” They can be gamed, they ignore the complexity of work, and they fail to measure the real value delivered to users.

‍

The Downstream Effects

‍

Over-reliance on output metrics can backfire:

‍

• Declining quality: Shipping faster often means cutting corners.
• Burnout: Teams push harder to hit arbitrary velocity goals.
• Stalled innovation: Focusing on numbers discourages experimentation and creativity.

‍

Instead of motivating teams, these metrics often drive them toward short-term wins and long-term pain.

‍

The Shift to Outcomes and Team Health

‍

The New Standard

‍

Outcome-driven metrics ask the real question: did we create value for the user or the business?

‍

Ryan Cooley, Director of Engineering at ProcessMaker, captures it well: “Outcomes outweigh individual performance metrics.” His philosophy? Leaders should measure the impact of the work, not just the activity behind it. That means tracking user adoption, engagement, conversion rates, or fewer customer complaints.

‍

The Gut Check

‍

Julian Ramirez, Senior Engineering Manager at Dropbox, takes it one step further. For him, metrics aren’t verdicts, they’re a gut check. His approach: use metrics to spark conversations, uncover blind spots, and figure out where to dig deeper.

‍

Metrics should provide context, not control. They tell you where to look, not what to think.

‍

Actionable Metrics for Leaders

‍

If you want to move beyond vanity metrics, here are outcome- and health-based measures that actually matter:

‍

Metrics for Outcomes

‍

• Time to Value: How long does it take for a new feature to deliver measurable impact?
• User Engagement: Are customers actually using what you built?
• Operational Health: How many production incidents are you seeing? Is reliability improving?

‍

Metrics for Team Health

‍

• Cycle Time: How long does it take for code to get from commit to production? This reflects process health more than velocity ever will.
• Team Satisfaction: Anonymous surveys or pulse checks reveal morale, burnout risk, and engagement.

‍

A New Way to Lead

‍

The shift is clear: great engineering leadership isn’t about counting tickets or tracking lines of code. It’s about aligning metrics with outcomes and team health.

‍

Metrics aren’t weapons. They’re tools. Used the right way, they don’t police your team; they help you understand it. Leaders who embrace this shift build teams that are not only more effective, but also more motivated and impactful.

‍

That’s how you get beyond velocity and into real results.

‍

Want to dig deeper intro engineering metrics in the AI era? Check out our webinar, "Engineering Team Metrics That Actually Matter in 2025" on this very topic as well as the blog post recap here.

‍