Organizations across the globe are taking notice – over 50,000 businesses (including roughly one-third of Fortune 500 companies) have already integrated Copilot into their development workflows. With such widespread adoption and enthusiasm (in a recent survey, 55% of developers said they prefer using Copilot), the big question for any software-driven organization is: what is the return on investment (ROI) of GitHub Copilot, and how can we measure it?
We’ll also discuss how to measure these effects within your own teams, ensuring that any gains from AI assistance are quantified and understood by stakeholders – from software developers and team leads to HR managers and executives.
AI coding assistants have quickly gone from novelty to mainstream. Tools like GitHub Copilot, Microsoft’s Copilot suite, and others are being embedded in IDEs to help developers write code faster and with less friction.
Why focus on ROI? For engineering executives and business leaders, any new tool or technology must justify itself in terms of the tangible value it delivers.
ROI for Copilot can be viewed in multiple dimensions:
HR managers and people analytics professionals are interested in how Copilot might reduce burnout or upskill employees.
One of the most direct ways Copilot delivers value is by helping developers complete tasks faster. Both self-reported surveys and controlled experiments show significant productivity gains. In GitHub’s large-scale survey of over 2,000 developers, 88% of respondents felt more productive when using Copilot
Summary of a GitHub experiment: Developers with Copilot completed a coding task 55% faster (1h11m vs 2h41m on average) and with a higher completion rate (78% vs 70%). This concrete lab result illustrates Copilot’s potential to save developers substantial time on routine programming tasks.
Speed isn’t the only factor in developer productivity. Good ROI means achieving efficiency without degrading quality (in fact, ideally improving it). A comprehensive analysis of Copilot must, therefore, look at code quality and reliability metrics. Here, the data is encouraging: Copilot’s assistance appears to maintain or even boost certain quality indicators in software delivery.
One measurable metric is the pull request merge rate – the percentage of PRs that pass code review and get merged into the codebase.
At Accenture, teams saw a 15% increase in PR merge rate after adopting GitHub Copilot. In simple terms, not only were developers submitting more code changes, but a higher fraction of those changes were accepted and merged, suggesting the code met the team’s quality standards more often.
Beyond the hard metrics of code and time, there’s a human dimension to Copilot’s ROI that organizations should measure: developer satisfaction and well-being. Writing software is a creative, mentally intensive activity, and anything that makes developers happier and less frustrated can have indirect but substantial returns (through higher engagement, lower turnover, and a culture of innovation). Interestingly, one of the most pronounced effects of GitHub Copilot observed in studies is the boost to developer morale and flow.
The ROI implications of improved developer satisfaction are significant yet sometimes overlooked. Happy developers tend to be more productive, more cooperative, and more creative. Research has shown that satisfied developers perform better and produce higher-quality work.
To truly understand Copilot’s impact, organizations should approach it as a scientific experiment or a continuous improvement project. Here are some best practices for conducting a metrics-driven evaluation of Copilot in your context:
Implementing the above approach can be challenging without the right tooling. This is where people analytics and engineering intelligence platforms come into play. One such solution is Worklytics, which offers a way to measure and maximize the impact of AI tools like GitHub Copilot across your organization.
Visibility into AI Usage: Many companies invest in AI assistants but lack visibility into where and how these tools are being used. Worklytics addresses this by connecting to data from your developers’ toolchain – for example, integrating with GitHub (including Copilot’s usage metrics), project trackers, CI/CD pipelines, and even communication tools. It allows you to track AI adoption and usage by team, tool, and role.
In practice, this means you can see which teams have embraced Copilot (and to what extent, such as suggestions accepted per developer), and which teams or departments are lagging in usage. This level of detail is invaluable: if one product line is slow to adopt Copilot, you can intervene with targeted training or explore if there are obstacles preventing usage.
Ensuring ROI Through Enablement: Metrics are not just for observation but for action. Worklytics’ insights can pinpoint opportunities to boost adoption and proficiency with Copilot.
For instance, if the data shows that some teams rarely use the tool, management can investigate why – perhaps those teams were unaware of certain features or had a poor initial experience.
Essentially, you turn metrics into a feedback loop for continuous improvement: measure usage → identify gaps or successes → intervene → measure again to see if ROI improved. Worklytics is built to facilitate this loop, providing timely data to drive behavior change and maximize impact.
The age of AI-assisted development is here, and GitHub Copilot is leading the charge in reshaping how software is written. For organizations, the key to benefiting from this technology is not blind adoption but measured adoption.
Leverage metrics and analytics to illuminate the results. A metrics-driven analysis will validate the wins, uncover areas for growth, and ensure that your investment in AI tools pays back dividends in productivity, quality, and developer happiness.
