GitHub Copilot: Real metrics to measure its impact on the business

The adoption of GitHub Copilot is advancing rapidly across many organizations, but measuring its true impact requires more than checking a few dashboards. It demands a deeper perspective, one that simultaneously integrates business, quality, and collaboration metrics.

The temptation to rely on simple indicators, such as the number of commits or pull request frequency, often leads to misinterpretations. Initially, because “frequency” does not always mean “efficiency.” Likewise, “more activity” is not necessarily synonymous with “greater adoption.”

In this article, we will explore the available options to understand whether Copilot impacts acceleration or quality, and we will offer concrete recommendations for moving forward when the numbers aren’t aligned.

Process and business metrics: Is Copilot accelerating the workflow?

One of the first signals to analyze is whether development cycle times improve. Metrics such as Lead Time and Cycle Time help understand how quickly requirements are taken, developed, and finally deployed to production. When these metrics consistently decrease, it can be inferred that Copilot is helping accelerate delivery.

However, doing things faster does not necessarily mean doing them better. To avoid biased interpretations, it is essential to complement these indicators with others that measure the impact on process quality. This is where time to market comes into play, showing whether value is being delivered faster. But that value must be maintained or improved, not degraded along the way. This is why quality-focused metrics should be analyzed in parallel.

Quality indicators: The balance between “faster” and “better”

To validate whether the speed generated by Copilot is accompanied by quality, it’s useful to observe metrics such as the number and types of automated tests, quality gates levels, and the evolution of security controls.

Quality gates are particularly relevant: they show how high the bar is for allowing code to reach production. If their level improves consistently without slowing down delivery, we can be confident that Copilot is adding real value.

Service availability metrics also help evaluate the quality of software in production. Reduced downtime, fewer outages, and faster detection and resolution times are clear signs that teams are developing more robustly.

A concrete example: in one client facing severe code quality issues and a high volume of end-user complaints, adopting the Copilot framework reduced monthly tickets by 90%. In this case, the success metric was not commits or speed, but the drastic reduction in user-perceived failures.

Impact on collaboration and the full development lifecycle

Copilot affects individual productivity, that’s true, but also cross-functional collaboration. To assess its impact in this area, it’s useful to analyze touchpoints between teams: how tests are generated, when security controls are executed, how deployments or rollbacks are performed, and how long it takes to restore a service after an incident.

These touchpoints are key hinges in the development cycle and help determine whether Copilot contributes to improving integration between roles and teams. When collaboration flows better, deliveries tend to be more consistent and predictable.

What to do if the metrics don’t align

Unlike other technology processes, there is no universal formula for correcting deviations in Copilot adoption. There is no magic checklist or guaranteed step-by-step plan. What does work is carrying out a deep retrospective exercise: reviewing what was done, what wasn’t, and listening to both leaders and more junior profiles.

In many cases, deviations are more related to insufficient training, poorly chosen metrics, or unrealistic expectations than to the tool itself.

AI doesn’t perform miracles: it is a powerful tool that requires time to integrate into the culture, products, and ways of working.

At Nubiral, we support this process by helping define clear metrics, detect blockers, and establish progressive adoption practices so that Copilot generates real impact.

Conclusions

Understanding whether GitHub Copilot generates real value in an organization requires looking beyond traditional metrics.

Its adoption is a process that demands learning, realistic expectations, and short, measurable goals. With a holistic view and well-guided continuous improvement, it can become a key enabler for scaling quality, productivity, and business value.

Would you like to evaluate Copilot adoption in your organization and understand where it is generating impact? We look forward to hearing from you: Schedule your meeting!

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