Engineering Leaders, measure the impact of GitHub Copilot
Understand the impact on developer productivity and satisfaction. Leverage holistic ROI dashboards to communicate the value, monitor the impact, and optimize your rollout.
Some of our customers include
Analytics that go far beyond the basics available from the GitHub Copilot API.
Github Copilot API
Faros AI
Developer metrics
Last activity date per developer
Adoption metrics per developer and team (DAU, WAU, MAU)
Copilot usage data
Last 28 days only
Full data history
Impact analysis
GitHub data only
Team and Power User filters
A/B Testing and Before/After analysis
Out-of-the-box dashboards for tracking adoption, impact, risk and value
Out-of-the-box developer surveys
Personalized AI/ML insights about adoption, usage and impact
Comprehensive visibility into the impact of GitHub Copilot.
Engineering leaders are deploying GitHub Copilot under the watchful eyes of executives who anticipate significant productivity gains. But how do you measure and communicate the impact?
Faros AI illustrates the impact and ROI of AI coding assistants. Track adoption and usage. Measure the benefits. Monitor the risks. Optimize the rollout.
The right implementation can unlock 40% higher ROI
AI pair programming tools are driving up licensing costs, with new tools being announced daily. Improve your return on investment from GitHub Copilot with a full measurement framework that guides you from pilot to rollout to optimization.
Vendor Comparison
WHICH IS THE BEST AI CODING ASSISTANT?
Identify the most effective tool, best-suited to your code and favored by your developers.
A/B Testing
IS THE TOOL WORTH IT?
Observe the impact of AI augmentation on different cohorts and profiles.
Before and After
WHAT’S CHANGED?
Measure the time savings. Calculate the economic benefit. Spot shifting bottlenecks.
While these tools have the potential to increase productivity, having a way to evaluate their impact scientifically will help build the business case for the investment.
Shai Peretz
SVP Engineering, Riskified
Track adoption and usage over time
Optimize your rollout for higher GitHub Copilot impact and ROI.
- Measure daily, weekly, and monthly adoption.
- Track acceptance rates and lines of code generated, by language and editor.
- Compare cohorts using different types of licenses (Business vs. Enterprise).
- See which activities are augmented most: coding, testing, debugging, documenting, etc.
- Identify unused licenses and understand why.
Measure downstream impacts across the entire SDLC
Separate hype from reality and set the right expectations for your org.
- Identify new bottlenecks as adoption increases (e.g., in code review, deployment, QA).
- Maintain visibility into quality, reliability, and tech debt.
- Identify potential security and compliance issues in AI-augmented code.
- Present cost/benefit metrics to executive leadership.
Understand time savings and developer satisfaction
See how accelerated coding is changing dev patterns.
- Measure cumulative time savings and understand where they’re being reinvested (velocity, quality, tech debt, upskilling, etc.).
- Capture developer sentiment with out-of-the-box surveys.
- Understand which teams and roles are adopting and benefiting most.
- Improve training and mentoring to optimize your rollout.
