From Commits to Outcomes: A Healthier Way to Talk About Engineering Performance

Written by: Monserrat Raya 

The Temptation of Simple Numbers

nAt some point, almost every engineering leader hears the same question. nn“How do you measure performance?” nnThe moment is usually loaded. Year-end reviews are approaching. Promotions need justification. Leadership above wants clarity. Ideally, something simple. Something defensible. nnThe easiest answer arrives quickly. nnCommits. Tickets closed. Velocity. Story points delivered. Hours logged. nnEveryone in the room knows these numbers are incomplete. Most people also know they are flawed. Still, they feel safe. They are visible. They fit neatly into spreadsheets. They create the impression of objectivity. nnAnd under pressure, impression often wins over accuracy. nnWhat starts as a convenience slowly hardens into a framework. Engineers begin to feel reduced to counters. Leaders find themselves defending metrics they do not fully believe in. Performance conversations shift from curiosity to self-protection. nnThis is not because leaders are careless. It is because measuring performance is genuinely hard, and simplicity is tempting when stakes are high. nnThe problem is not that activity metrics exist. The problem is when they become the conversation, instead of a small input into it.

Why Activity Metrics Feel Safe (But Aren’t)

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Activity metrics persist for a reason. They offer relief in uncomfortable moments.

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The Appeal of Activity Metrics

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They feel safe because they are:

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• Visible. Everyone can see commits, tickets, and throughput.

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• Comparable. Numbers line up nicely across teams and individuals.

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• Low-friction. They reduce the need for nuanced judgment.

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• Defensible upward. Leaders can point to charts instead of narratives.

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In organizations under pressure to “simplify” performance measurement, these traits are attractive. They create the sense that performance is being managed, not debated.

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The Hidden Cost

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The downside is subtle but significant.

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Activity metrics measure motion, not contribution.

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They tell you something happened, not whether it mattered. They capture effort, not impact. Over time, they reward visibility over value and busyness over effectiveness.

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This is not a new insight. Even Harvard Business Review has repeatedly warned that performance metrics, when misapplied, distort behavior rather than clarify it, especially in knowledge work where output quality varies widely. When leaders rely too heavily on activity metrics, they gain short-term clarity and long-term confusion. The numbers go up, but understanding goes down.

The Behaviors These Metrics Actually Create

nMetrics do more than measure performance. They shape it. Once activity metrics become meaningful for evaluation, engineers adapt. Not maliciously. Rationally. nn

What Optimizing for Activity Looks Like

nnOver time, teams begin to exhibit familiar patterns: n

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• More commits, smaller commits, noisier repositories

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• Work sliced unnaturally thin to increase visible throughput

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• Preference for tasks that show progress quickly

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• Reluctance to take on deep, ambiguous, or preventative work

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nRefactoring, mentoring, documentation, and incident prevention suffer first. These activities are critical to long-term outcomes, but they rarely show up cleanly in dashboards. nnEngineers notice. Quietly. nnThey learn which work is valued and which work is invisible. The system teaches them what “good performance” looks like, regardless of what leaders say out loud. nnThis is where trust begins to erode. When engineers feel evaluated on metrics that misrepresent their contribution, performance conversations become defensive. Leaders lose credibility, not because they lack intent, but because the measurement system feels disconnected from reality. nnMetrics do not just observe behavior. They incentivize it.

What “Outcomes” Actually Mean in Engineering

nAt this point, many leaders nod and say, “We should focus on outcomes instead.” nThat phrase sounds right, but it often remains vague. nOutcomes are not abstract aspirations. They are concrete, observable effects over time. nn

Outcomes, Grounded in Reality

nnIn engineering, outcomes often show up as: n

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• Improved reliability, fewer incidents, faster recovery when things break

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• Predictable delivery, with fewer last-minute surprises

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• Systems that are easier to change six months later, not harder

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• Teams that unblock others, not just ship their own backlog

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• Reduced cognitive load, making good decisions easier under pressure

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nNone of these map cleanly to a single number. That is precisely the point. nnOutcomes require interpretation. They demand context. They force leaders to engage with the work, not just the artifacts of it. nnThis does not make performance measurement weaker. It makes it more honest. nn

Using Metrics as Inputs, Not Verdicts

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This is the heart of healthier performance conversations.
Metrics are not the enemy. Treating them as verdicts is.

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Where Metrics Actually Help

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Used well, metrics act as signals. They prompt questions rather than answer them.

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A drop in commits might indicate:

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• Work moved into deeper problem-solving

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• Increased review or mentoring responsibility

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• Hidden bottlenecks or external dependencies

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A spike in throughput might signal:

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• Healthy momentum

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• Superficial work being prioritized

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• Short-term optimization at long-term cost

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Strong leaders do not outsource judgment to dashboards. They use data to guide inquiry, not to end discussion.

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This approach aligns with how Scio frames trust and collaboration in distributed environments. In Building Trust Across Screens: Human Capital Insights from Nearshore Software Culture, performance is treated as something understood through patterns and relationships, not isolated metrics.
Removing judgment from performance reviews does not make them fairer. It makes them emptier.

Where Activity Metrics Fall Short (and What Outcomes Reveal)

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Activity vs Outcome Signals in Practice

This table is not an argument to discard metrics. It is a reminder that activity and outcomes answer different questions. Confusing them leads to confident conclusions built on partial truth.

How Experienced Leaders Run Performance Conversations

nLeaders who have run reviews for years tend to converge on similar practices, not because they follow a framework, but because experience teaches them what breaks.n

What Changes with Experience

nSeasoned engineering leaders tend to:n

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• Look at patterns over time, not snapshots

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• Ask “what changed?” instead of “how much did you produce?”

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• Consider constraints and trade-offs, not just results

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• Value work that prevented problems, even when nothing “happened”

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nThese conversations take longer. They require trust. They cannot be fully automated.nThey also produce better outcomes.nEngineers leave these discussions feeling seen, even when feedback is hard. Leaders leave with a clearer understanding of impact, not just activity.nnThis perspective often emerges after leaders see how much performance is shaped by communication quality, not just individual output. In How I Learned the Importance of Communication and Collaboration in Software Projects, Scio explores how delivery outcomes improve when expectations, feedback, and ownership are clearly shared across teams. That same clarity is what makes performance conversations more accurate and less adversarial.

Why This Matters More Than Fairness n

nnMost debates about performance metrics eventually land on fairness. nnFairness matters. But it is not the highest stake. nn

The Real Cost of Shallow Measurement

nnWhen performance systems feel disconnected from reality: n

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• Trust erodes quietly

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• Engineers disengage without drama

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• High performers stop investing emotionally

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• The best people leave without making noise

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nThis is not a tooling problem. It is a leadership problem. nHealthy measurement systems are retention systems. They signal what the organization values, even more than compensation does. nScio partners with engineering leaders who care about outcomes over optics. By embedding high-performing nearshore teams that integrate into existing ownership models and decision-making processes, Scio helps leaders focus on real impact instead of superficial productivity signals. nnThis is not about control. It is about clarity.

Measure to Learn, Not to Control

nnThe goal of performance measurement is not to rank engineers. nIt is to understand impact. Activity is easy to count. Outcomes require judgment. Judgment requires leadership. nWhen organizations choose outcomes-first thinking, performance conversations become less defensive and more constructive. Alignment improves. Trust deepens. Teams optimize for results that matter, not numbers that impress. nnMeasuring well takes more effort. It also builds stronger teams.