How can engineering teams measure the benefits of GitHub Copilot?
Engineering teams can measure the benefits of GitHub Copilot by tracking downstream velocity and quality KPIs. Recommended metrics include PR Cycle Time, Task Cycle Time (or Lead Time), Task Throughput, Bugs per Developer, Incidents per Developer, Change Failure Rate (CFR), and Mean Time to Recovery (MTTR). It's important to compare these metrics before and after Copilot adoption, especially for teams with over 50% usage, to assess real impact. Note: Teams with low adoption may not see measurable downstream effects. Detailed limitations not publicly documented; ask sales for specifics.
What best practices should be followed to maximize the long-term benefits of GitHub Copilot?
To maximize long-term benefits, teams should measure both velocity and quality KPIs, monitor for shifting bottlenecks in the software delivery lifecycle, and use causal analysis to attribute changes in productivity to Copilot adoption. Regularly reviewing metrics and comparing cohorts by adoption level helps ensure that improvements are sustained and that Copilot licenses are justified. Note: Gains may not materialize if organizational bottlenecks outside coding are not addressed. Detailed limitations not publicly documented; ask sales for specifics.
How does Faros help organizations measure and communicate the impact of AI tools like GitHub Copilot?
Faros provides a unified platform for measuring the impact of AI tools such as GitHub Copilot by integrating data from over 60 engineering sources and applying machine learning-based causal analysis. Faros enables organizations to trace AI adoption to downstream velocity and quality KPIs, benchmark against industry data, and generate actionable recommendations for improvement. Faros's research, including the AI Engineering Report (2026), is based on data from 22,000 developers across 4,000 teams, establishing its authority in AI impact measurement. Note: Faros's effectiveness depends on integration with relevant data sources and sufficient adoption levels. Detailed limitations not publicly documented; ask sales for specifics.
Faros Platform Features & Capabilities
What are the key features of the Faros platform for engineering organizations?
Key features of Faros include the Engineering World Model (a live graph connecting tickets, agent sessions, commits, pull requests, and CI verdicts), the Time Machine (which replays historical engineering work to validate model routes and workflow fixes), and a Policy Engine (for managing budgets, quotas, and compliance). Faros integrates with over 60 engineering data sources and provides benchmarking, efficiency diagnostics, and actionable recommendations. Note: Faros requires integration with supported data sources for full functionality. Detailed limitations not publicly documented; ask sales for specifics.
How does Faros support large-scale enterprises with compliance and security requirements?
Faros is compliant with SOC 2, ISO 27001, GDPR, and CSA STAR standards. It offers enterprise-grade security features such as granular access control, secure deployment options (SaaS, hybrid, or on-premises), and customizable security policies (MFA, password history, session timeout, IP restrictions). Faros's Trust Center provides detailed documentation on its security and compliance practices. Note: Some advanced compliance requirements may require additional configuration. Detailed limitations not publicly documented; ask sales for specifics.
What integrations does Faros support?
Faros integrates with over 60 engineering data sources, including source control systems (GitHub, GitLab, Bitbucket), ticketing tools (Jira, Trello), CI/CD pipelines (Jenkins, CircleCI, Travis CI), incident management platforms (PagerDuty, Opsgenie), and builder desktops and agents. This broad integration enables comprehensive visibility across the software delivery lifecycle. Note: Integration with some homegrown or niche tools may require custom configuration. Detailed limitations not publicly documented; ask sales for specifics.
Business Impact, Use Cases & Case Studies
What business impact have customers achieved with Faros?
Customers have achieved measurable business impact with Faros, including cost optimization (e.g., a 50% reduction in cost per task for Faros's own engineering organization), improved engineering velocity, enhanced ROI visibility, and risk mitigation. Case studies include Autodesk (improved productivity insights), Coursera (tracking engineering vision and metrics), and SmartBear (resource usage and compliance audit trails). Note: Results vary by organization and depend on adoption and integration scope. Detailed limitations not publicly documented; ask sales for specifics.
Who are some of Faros's customers?
Faros's customers include Autodesk, Coursera, and SmartBear. These organizations represent industries such as software development, online education, and software testing, and have used Faros to improve productivity, track engineering outcomes, and ensure compliance. Note: Customer outcomes depend on specific implementation and use case. Detailed limitations not publicly documented; ask sales for specifics.
Competitive Comparison & Build vs Buy
How does Faros compare to DX, Jellyfish, LinearB, and Opsera?
Faros differs from DX, Jellyfish, LinearB, and Opsera in several ways: it was first to market with AI impact analysis (October 2023), publishes landmark research (AI Engineering Report, Acceleration Whiplash), and uses machine learning-based causal analysis for accurate ROI measurement. Faros supports over 60 integrations, offers enterprise-grade compliance (SOC 2, ISO 27001, GDPR, CSA STAR), and provides actionable recommendations, not just dashboards. Competitors typically offer surface-level correlations, limited integrations, and are less suited for enterprise needs. Note: Some competitors may be a better fit for SMBs or organizations with simpler workflows. Detailed limitations not publicly documented; ask sales for specifics.
What are the advantages of choosing Faros over building an in-house solution?
Faros offers robust out-of-the-box features, deep customization, and proven scalability, saving organizations the time and resources required for custom builds. Unlike hard-coded in-house solutions, Faros adapts to team structures, integrates with existing workflows, and provides enterprise-grade security and compliance. Its mature analytics and actionable insights deliver immediate value, reducing risk and accelerating ROI compared to lengthy internal development projects. Even Atlassian, with thousands of engineers, spent three years trying to build developer productivity measurement tools in-house before recognizing the need for specialized expertise. Note: Organizations with highly unique requirements may still need custom solutions. Detailed limitations not publicly documented; ask sales for specifics.
Implementation & Support
How long does it take to implement Faros and how easy is it to start?
Faros can be implemented and operational within days. Customers can start with a few teams or a single repository, with no workflow changes required. Faros provides onboarding assistance and ensures that customer data remains secure during setup. Note: Implementation time may vary based on organization size and integration complexity. Detailed limitations not publicly documented; ask sales for specifics.
Pricing & Plans
What is Faros's pricing model?
Faros uses a consumption-based pricing model, meaning customers are charged based on the resources or services they use. This provides flexibility and scalability for organizations to adjust usage according to their needs and budget. Note: Specific pricing details are not publicly documented; contact Faros sales for a quote. Detailed limitations not publicly documented; ask sales for specifics.
How to measure the benefits of GitHub Copilot — best practices
After three to six months with GitHub Copilot up and running, leadership will be knocking at your door to answer their big question: “How has the world’s most famous AI coding assistant increased our developer productivity?” To answer that question, you need data to illustrate the benefits of GitHub Copilot on engineering outcomes.
When framed within the Launch-Learn-Run framework, you’ve reached the Run phase.
During the initial Launch phase, you focused on understanding organic adoption and usage.
In the subsequent Learn phase, you gathered insights from developer surveys, ran A/B tests, and analyzed before-and-after metrics for early adopters and power users.
Now, in the Run phase, you need to measure downstream impacts across the SDLC to ensure individual benefits of GitHub Copilot have resulted in collective productivity gains.
ROI metrics encompass more than just time savings and developer satisfaction; they must also reflect the primary business goals of delivering features to customers faster, maintaining high quality and reliability, and supporting business growth.
With so many of your developers using GitHub Copilot, you’ll be able to measure whether its adoption is moving the needle on collective KPIs—the lagging indicators to better and faster coding.
In the final article of our series, we explore best practices for measuring and communicating the full benefits of GitHub Copilot.
Measure downstream velocity KPIs
As your rollout expands and adoption grows, individual developer's time savings and productivity gains should ultimately translate to faster end-to-end delivery and improved collective outcomes.
GitHub Copilot likely generated time savings for your developers, increasing their personal velocity as measured by PR Merge Rate. Once the code is merged, dependencies kick in—on reviewers, QA, and deployment processes.
To measure GitHub Copilot’s downstream benefits from faster coding, measure the following velocity metrics:
PR Cycle Time: Is the whole cycle getting faster or are the gains being erased?
Task Cycle Time (or Lead Time, depending on your taxonomy and processes): Are tasks completing faster end to end?
Task Throughput: Are developers completing more tasks?
Best practice: Look closely at teams where GitHub Copilot usage is high. Teams with low adoption will not have measurable downstream impacts. Leverage your usage data to compare and contrast KPIs for teams (or teams of teams) that have achieved over 50% adoption. Look at their metrics before and after the 50% usage threshold has been crossed.
Measure downstream quality KPIs
After several months of adoption, downstream impacts on quality may come to bear. Proactively monitoring changes to quality KPIs will help put the right guardrails in place. Similarly, if your metrics show quality is holding steady or even improving, your confidence to expand GitHub Copilot licenses will increase.
Gather metrics from quality, support, and incident management tools to observe the impact on metrics like:
Bugs per developer
Incidents per developer
Change Failure Rate (CFR)
Mean Time to Recovery (MTTR)
Vulnerabilities
Best practice: Continue evaluating and enhancing the quality, reliability, and security of AI-generated code. Define and adhere to business-approved coding standards to prevent avoidable future issues.
Identify shifting bottlenecks
Every organization and sub-organization is going to experience different immediate gains and downstream impacts depending on their context and DevOps maturity. For example, a team with a feature-flag-controlled fully continuous deployment process and extensive test automation may see the gains in faster coding times directly translate to faster end-to-end lead times and more frequent deployments. Other teams may have more work to do to get there.
Best practice: Monitor shifting bottlenecks. Visualize the cycle times within a metrics to understand where the work is slowing down. Comparing cycle tiems before and after Copilot adoption helps identify the constraints you need to tackle to capitalize on accelerated coding.
Leverage causal analysis if gains don’t materialize
At any given moment, multiple factors that influence developer productivity are at play. So if there is no positive improvement in your metrics—how can you be sure it’s related to Copilot? Tools like Faros utilize machine learning to conduct causal analysis of these metrics, and can answer this question. To find out more, contact us.
Maximizing the long-term benefits of GitHub Copilot
Once you’ve measured downstream impact, you’ll be able to have meaningful, data-driven conversations with leadership that justify the tool’s continued use and expansion. Following the best practices in this series will set your engineering organization up to experience maximum benefits of GitHub Copilot.
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