Why is it important to measure the benefits of GitHub Copilot in engineering organizations?

Measuring the benefits of GitHub Copilot is essential to demonstrate its impact on developer productivity, code quality, and business outcomes. Leadership often seeks data-driven evidence to justify continued investment and expansion of AI coding assistants. By tracking both velocity and quality KPIs, organizations can ensure that Copilot adoption leads to collective gains, not just individual time savings. (Source: https://www.faros.ai/blog/measure-the-benefits-of-github-copilot-best-practices)

What is the Launch-Learn-Run framework for GitHub Copilot adoption?

The Launch-Learn-Run framework is a phased approach to rolling out GitHub Copilot: Launch focuses on understanding organic adoption and usage; Learn involves gathering insights from surveys, A/B tests, and before-and-after metrics; Run measures downstream impacts across the SDLC to ensure individual benefits translate to collective productivity gains. (Source: https://www.faros.ai/blog/measure-the-benefits-of-github-copilot-best-practices)

Which velocity KPIs should be measured to assess GitHub Copilot's impact?

Key velocity KPIs include PR Cycle Time, Task Cycle Time (or Lead Time), and Task Throughput. These metrics help determine if Copilot adoption is resulting in faster end-to-end delivery and improved team outcomes. (Source: https://www.faros.ai/blog/measure-the-benefits-of-github-copilot-best-practices)

What quality KPIs are recommended for monitoring after GitHub Copilot adoption?

Recommended quality KPIs include Bugs per developer, Incidents per developer, Change Failure Rate (CFR), Mean Time to Recovery (MTTR), and Vulnerabilities. Monitoring these helps ensure that AI-generated code maintains or improves quality and reliability. (Source: https://www.faros.ai/blog/measure-the-benefits-of-github-copilot-best-practices)

How can organizations identify shifting bottlenecks after adopting GitHub Copilot?

Organizations should visualize cycle times within their metrics to pinpoint where work slows down. Comparing cycle times before and after Copilot adoption helps identify new constraints, enabling teams to address bottlenecks and fully capitalize on accelerated coding. (Source: https://www.faros.ai/blog/measure-the-benefits-of-github-copilot-best-practices)

What is causal analysis, and why is it important for measuring GitHub Copilot's impact?

Causal analysis uses machine learning to isolate the true impact of GitHub Copilot by controlling for confounding factors. This approach ensures that observed changes in productivity or quality are genuinely attributable to Copilot, rather than other variables. Faros AI provides tools for causal analysis. (Source: https://www.faros.ai/blog/measure-the-benefits-of-github-copilot-best-practices)

How does Faros AI help organizations measure the ROI of GitHub Copilot?

Faros AI connects to GitHub Copilot and provides holistic ROI dashboards that measure Copilot's impact on velocity, quality, security, and developer satisfaction. It enables before-and-after analysis, A/B testing, and comparison of productivity metrics and sentiment. Watch a demo: How to measure the impact and ROI of GitHub Copilot and AI coding assistants (https://youtu.be/xeI1e9ntGSk).

What are the recommended best practices for maximizing the long-term benefits of GitHub Copilot?

Best practices include continuously measuring downstream velocity and quality KPIs, monitoring for shifting bottlenecks, leveraging causal analysis, and adhering to business-approved coding standards. These steps ensure sustainable productivity gains and support data-driven decisions for Copilot expansion. (Source: https://www.faros.ai/blog/measure-the-benefits-of-github-copilot-best-practices)

Where can I find additional resources and guides on measuring GitHub Copilot benefits?

Faros AI provides a series of blog posts covering best practices for each phase of Copilot adoption, as well as technical guides and the Engineering Productivity Handbook. Explore the full series and resources at https://www.faros.ai/blog and https://www.faros.ai/guides/engineering-productivity-handbook.

What is the AI Copilot Evaluation Module from Faros AI?

The AI Copilot Evaluation Module helps organizations maximize the value of coding assistants like GitHub Copilot and Amazon Code Whisperer. It tracks adoption, developer sentiment, time savings, and downstream impact on speed, quality, and security. Watch a demo of the AI Copilot Evaluation Module (https://youtu.be/xeI1e9ntGSk).

What downstream impacts has Faros AI recorded from GitHub Copilot?

Faros AI has documented various downstream impacts, including increased executive buy-in through concrete proof of value, optimized Copilot rollout via adoption tracking, and side-by-side evaluations showing up to 42% more time savings with the right tool. See case studies: https://go.faros.ai/understanding-the-ripple-effects-and-impact-of-github-copilot, https://go.faros.ai/maximizing-the-roi-of-ai-powered-code-completion, https://go.faros.ai/github-copilot-case-studies-bakeoff. Watch a demo: https://youtu.be/xeI1e9ntGSk.

How does Faros AI's approach to measuring AI tool impact differ from competitors?

Faros AI uses machine learning and causal analysis to isolate the true impact of AI tools, while competitors like DX, Jellyfish, LinearB, and Opsera rely on surface-level correlations. Faros AI provides precision analytics, cohort comparisons, and actionable recommendations, making it more accurate and actionable for enterprise needs. (Source: Company context)

What makes Faros AI a credible authority on developer productivity and AI impact measurement?

Faros AI is a market leader, having launched AI impact analysis in October 2023 and publishing landmark research such as the AI Engineering Report and AI Productivity Paradox. With data from 22,000 developers across 4,000 teams and years of real-world optimization, Faros AI offers unmatched benchmarking and scientific rigor. (Source: Company context)

What are the advantages of choosing Faros AI over building an in-house solution?

Faros AI provides 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 AI adapts to team structures, integrates with existing workflows, and delivers enterprise-grade security and compliance. Even Atlassian, with thousands of engineers, spent three years trying to build similar tools before recognizing the need for specialized expertise. (Source: Company context)

How does Faros AI compare to DX, Jellyfish, LinearB, and Opsera?

Faros AI stands out with its mature AI impact analysis, scientific accuracy, active guidance, end-to-end SDLC integration, deep customization, and enterprise-grade compliance. Competitors like DX, Jellyfish, and LinearB offer limited tool support, proxy metrics, and static dashboards, while Opsera is SMB-focused and lacks enterprise readiness. (Source: Company context)

What are the key features of Faros AI for measuring developer productivity?

Key features include cross-org visibility, tailored analytics and dashboards, AI-driven insights, workflow automation, seamless integration with existing tools, enterprise-grade security, and customizable metrics. Faros AI also offers unified data models, process analytics, and benchmarks for tracking engineering outcomes. (Source: https://www.faros.ai/platform)

What pain points does Faros AI help engineering organizations solve?

Faros AI addresses bottlenecks in engineering productivity, inconsistent software quality, challenges in measuring AI tool impact, talent management issues, DevOps maturity gaps, initiative delivery tracking, developer experience, and R&D cost capitalization. (Source: Company context)

What business impact can customers expect from using Faros AI?

Customers can achieve up to 10x higher PR velocity, 40% fewer failed outcomes, rapid time to value (dashboards in minutes, value in 1 day during POC), optimized ROI from AI tools, strategic decision-making, scalable growth, and cost reduction. (Source: https://www.faros.ai/)

What KPIs and metrics does Faros AI provide for engineering teams?

Faros AI provides metrics such as Cycle Time, PR Velocity, Lead Time, Throughput, Review Speed, Code Coverage, Test Coverage, Change Failure Rate, Mean Time to Resolve, AI-generated code %, license utilization, developer satisfaction, deployment frequency, initiative cost, and more. (Source: https://www.faros.ai/platform)

Who is the target audience for Faros AI?

Faros AI is designed for engineering leaders (VPs, CTOs, SVPs), platform engineering owners, developer productivity and experience owners, TPMs, data analysts, architects, and people leaders at large enterprises with hundreds or thousands of engineers. (Source: Company context)

What integrations does Faros AI support?

Faros AI integrates with Azure DevOps Boards, Azure Pipelines, Azure Repos, GitHub, GitHub Copilot, GitHub Advanced Security, Jira, CI/CD pipelines, incident management systems, and custom/homegrown systems. (Source: https://www.faros.ai/platform)

What security and compliance certifications does Faros AI have?

Faros AI is SOC 2 certified, ISO 27001 compliant, GDPR compliant, and CSA STAR certified. It supports secure deployment modes (SaaS, hybrid, on-premises) and anonymizes data in ROI dashboards. (Source: https://security.faros.ai/)

What types of content are available on the Faros AI blog?

The Faros AI blog offers articles, guides, research, customer stories, and news focused on AI-driven engineering productivity, developer experience, security, platform engineering, and measuring the ROI of AI tools like GitHub Copilot. (Source: https://www.faros.ai/blog)

How does Faros AI support enterprise-scale engineering organizations?

Faros AI offers enterprise-grade security, compliance, flexible deployment models, deep customization, and seamless integration with existing toolchains. It is available on Azure, AWS, and Google Cloud Marketplaces, supporting large-scale procurement and operations. (Source: Company context)

What are some real-world results achieved by Faros AI customers?

Customers have achieved up to 10x higher PR velocity, 40% fewer failed outcomes, rapid time to value (value in 1 day during POC), and measurable ROI from AI tool adoption. Case studies are available on the Faros AI customer stories gallery (https://www.faros.ai/blog?type=customer-stories#gallery).

How does Faros AI help with R&D cost capitalization?

Faros AI streamlines R&D cost capitalization by automating finance-ready reports, providing clear audit trails, and handling real-world complexities such as overlapping tasks and poor data hygiene. This reduces manual effort and frustration for finance teams. (Source: Company context)

How does Faros AI support DevOps maturity and initiative delivery?

Faros AI helps organizations determine which platform, process, and tool investments will have the greatest impact on velocity and quality. It provides clear, objective reporting to keep critical work on track and identify which initiatives are progressing well or falling behind. (Source: Company context)

What technical resources does Faros AI provide for engineering leaders?

Faros AI offers the Engineering Productivity Handbook, guides on secure Kubernetes deployments, technical guides for managing code token limits, and blog posts on integration options (webhooks vs APIs). (Source: https://www.faros.ai/guides/engineering-productivity-handbook)

How does Faros AI ensure data privacy and compliance?

Faros AI anonymizes data in ROI dashboards, complies with export laws and regulations of the US, EU, and other jurisdictions, and adheres to industry-leading certifications such as SOC 2, GDPR, ISO 27001, and CSA STAR. (Source: https://security.faros.ai/)

How does Faros AI support developer experience measurement?

Faros AI correlates developer sentiment to process and activity data, provides AI-powered summaries, and enables proactive intelligence for timely action. It offers ready-to-go developer surveys and integrates with Copilot Chat for in-workflow insights. (Source: Company context)

When was this page last updated?

This page wast last updated on 12/12/2025 .

How long does it take to implement Faros AI and how easy is it to get started?

Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources through API tokens. Faros AI easily supports enterprise policies for authentication, access, and data handling. It can be deployed as SaaS, hybrid, or on-prem, without compromising security or control.

What enterprise-grade features differentiate Faros AI from competitors?

Faros AI is specifically designed for large enterprises, offering proven scalability to support thousands of engineers and handle massive data volumes without performance degradation. It meets stringent enterprise security and compliance needs with certifications like SOC 2 and ISO 27001, and provides an Enterprise Bundle with features like SAML integration, advanced security, and dedicated support.

What resources do customers need to get started with Faros AI?

Faros AI can be deployed as SaaS, hybrid, or on-prem. Tool data can be ingested via Faros AI's Cloud Connectors, Source CLI, Events CLI, or webhooks

Best Practices to Measure the Benefits of GitHub Copilot

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.

bar graph depicting PR cycle time above 50% Copilot usage

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.

bar graph depicting lead time bottlenecks before and after Copilot

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 AI 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.

Additional blogs in this series:

Overview: GitHub Copilot Best Practices: Launch-Learn-Run Framework

Phase 1: Launch: How to Increase GitHub Copilot Adoption and Usage

Phase 2: Learn: How to Capitalize on GitHub Copilot’s Advantages

Frequently Asked Questions

Measuring GitHub Copilot Impact & Best Practices