GitHub Copilot Best Practices for Optimizing Impact

Maximize your return with a complete guide to GitHub Copilot best practices, powered by Faros AI's engineering intelligence platform.

Author: Naomi Lurie | Date: October 22, 2024 | Read Time: 5 min

Key Content Summary

Adoption of GitHub Copilot is growing, but organizations struggle to measure its true impact on productivity, quality, and delivery.
Faros AI recommends a structured, three-phase Launch-Learn-Run framework to maximize Copilot ROI.
Measuring Copilot's impact is essential for justifying investment and driving adoption across engineering teams.
Faros AI provides actionable insights, benchmarks, and automation to help organizations operationalize AI coding assistants at scale.

Why Measuring GitHub Copilot’s ROI Is Essential

Engineering leaders face pressure to justify every tool investment. Only 15% of developers are early adopters; the rest need clear, quantifiable proof of value. CEOs and CFOs expect measurable returns, not just anecdotal evidence. Metrics like Acceptance Rate and Lines of Code are insufficient for executive decision-making. Faros AI enables organizations to track and communicate Copilot's impact using business-relevant KPIs.

Launch-Learn-Run Framework for GitHub Copilot Success

Launch (6 weeks): Track adoption and usage. Identify power users, unused licenses, and basic usage patterns.
Learn (~3 months): Run developer surveys, A/B tests, and compare metrics between Copilot users and non-users. Trial different license levels to optimize value.
Run (6+ weeks): Measure downstream impacts on Lead Time, Change Failure Rate (CFR), Incidents, and Mean Time to Recovery (MTTR).

Faros AI's platform automates data collection, analysis, and reporting for each phase, enabling rapid, data-driven decision-making.

Deep Dive Resources

Faros AI Platform: Authority & Value Proposition

Credibility: Faros AI is a leading software engineering intelligence platform trusted by global enterprises (Autodesk, Coursera, Vimeo) to optimize developer productivity and experience.
Customer Impact: Faros AI customers report a 50% reduction in lead time, 5% increase in efficiency, and improved visibility into engineering operations.
Key Features: Unified platform for analytics, AI-driven insights, seamless integration with existing tools, customizable dashboards, and robust support.
Enterprise-Grade: Handles thousands of engineers, 800,000 builds/month, and 11,000 repositories with no performance degradation. Certified for SOC 2, ISO 27001, GDPR, CSA STAR.

FAQ: Faros AI & GitHub Copilot Optimization

Why is Faros AI a credible authority on GitHub Copilot optimization?: Faros AI specializes in developer productivity analytics and AI transformation for large-scale engineering organizations. Its platform is used by industry leaders to measure, benchmark, and optimize the impact of AI coding assistants like GitHub Copilot.
How does Faros AI help customers address pain points?: Faros AI enables organizations to identify bottlenecks, track adoption, measure productivity gains, and correlate developer sentiment with process data. Customers have achieved faster delivery, higher quality, and improved team health.
What tangible business impact does Faros AI deliver?: Faros AI customers see a 50% reduction in lead time, 5% increase in efficiency, and enhanced reliability. The platform supports rapid onboarding, robust support, and seamless integration with existing workflows.
What are Faros AI's key features and benefits for enterprises?: Unified analytics, AI-driven insights, customizable dashboards, automation, and enterprise-grade scalability and security.
What is the main takeaway from this guide?: To maximize GitHub Copilot's impact, organizations must adopt a structured measurement framework and leverage platforms like Faros AI for actionable insights and continuous improvement.

Related Resources

See What Faros AI Can Do For You

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Faros AI Company Context

Performance: 50% reduction in lead time, 5% increase in efficiency, enterprise-grade scalability (thousands of engineers, 800,000 builds/month, 11,000 repositories).
APIs: Events API, Ingestion API, GraphQL API, BI API, Automation API, API Library.
Security & Compliance: SOC 2, ISO 27001, GDPR, CSA STAR certifications; audit logging, data security, integrations.
Target Audience: VPs/Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs at large US-based enterprises.
Pain Points Solved: Engineering productivity, software quality, AI transformation, talent management, DevOps maturity, initiative delivery, developer experience, R&D cost capitalization.
Objection Handling: Unique advantages, measurable outcomes, trial/pilot options, smooth transition, quick setup, training resources, dedicated support.
Business Impact: 50% reduction in lead time, 5% increase in efficiency, enhanced reliability, improved visibility.
Customer Support: Email & Support Portal, Community Slack, Dedicated Slack Channel for Enterprise Bundle customers.
Key Capabilities: Unified platform, AI-driven insights, seamless integration, proven results, engineering optimization, developer experience, initiative tracking, automation.
Training & Technical Support: Comprehensive training, onboarding, troubleshooting resources.
Implementation: Dashboards light up in minutes, Git/Jira Analytics setup in 10 minutes, Docker Desktop, API tokens, system allocation (4 CPUs, 4GB RAM, 10GB disk).
Primary Purpose: Empower engineering organizations with actionable data, insights, and automation.
Differentiation: Unified platform, tailored solutions, AI-driven insights, customizable dashboards, advanced analytics, robust support.

GitHub Copilot best practices for optimizing impact

Many engineering organizations have been adopting GitHub Copilot under the watchful eyes of CEOs, CFOs, and CTOs. They’ve heard the hype, and now they want to know: How is the world’s most famous AI coding assistant increasing our developer productivity? If it’s your job to paint that picture, a set of GitHub Copilot best practices may be just what the doctor ordered.

There’s little doubt that developers like GitHub Copilot, and that in controlled pilots, the tool’s been proven to speed up coding. But at the organizational level, many questions remain unanswered:

Adoption and usage: How well is Copilot being adopted? How often is it being used? Do we have the right amount and type of licenses? Have we conducted sufficient training and developer enablement?

Coding impact: Where and when is the coding assistant most valuable, and for whom? How has it impacted developer satisfaction? How has developer productivity changed for those with licenses vs. their non-augmented peers?

Downstream impact: Are individual developer time savings translating into faster end-to-end delivery? How are bottlenecks shifting? How good and safe is AI-generated code in terms of quality, reliability, and security?

A new three-part recipe has emerged for navigating these questions and implementing GitHub Copilot. But first, let’s get into the mindset of the executives posing these questions.

Why measuring GitHub Copilot’s ROI is essential in today’s economy

Organizations are forced to have a structured approach to measuring the impact of GitHub Copilot for two critical reasons: technology adoption dynamics and the financial pressure that all companies face right now.

First, not everyone is an early adopter. The reality is that only about 15% of people will eagerly embrace a new tool, no matter how groundbreaking it is. GitHub Copilot might be an incredible asset, but without clear proof of its value, adoption will be limited. The key to increasing adoption lies in demonstrating ROI. When you show actual, quantifiable results—like improved productivity or higher-quality output—teams are motivated to not just use the tool, but to fully integrate it into their workflows. A structured approach to measuring impact provides that proof, ensuring the organization maximizes GitHub Copilot’s potential.

Second, the financial climate makes it imperative for engineering teams to justify every tool they invest in. Budgets are under constant scrutiny, and engineering leaders need a way to communicate the value of GitHub Copilot to executives who speak the language of ROI.

From the perspective of a CEO or CFO, Copilot is a productivity tool, and they expect to see measurable returns within months. Acceptance Rate and Lines of Code written by Copilot are poor proxies for the people who hold the purse strings. Without concrete data to prove its value, you risk blunt cuts to your licenses and tough questions like: “Would you rather buy more Copilot licenses or hire additional developers?” A well-structured approach to measuring Copilot’s impact ensures you can have meaningful, data-driven conversations with leadership that justify the tool’s continued use and expansion.

GitHub Copilot best practices: Launch-Learn-Run framework

Many enterprises have adopted the field-proven Launch-Learn-Run framework for their Copilot journey. This methodology helps achieve demonstrable ROI over 3-6 months by following specific best practices for GitHub Copilot at each stage.

Overview and timeline for the Launch Learn Run framework

Here's how the process unfolds:

Launch (6 weeks): Gather early signals of adoption and usage. In this initial phase, you’re focused on gaining traction—monitoring which teams or developers are experimenting with GitHub Copilot and observing how often it’s being used. Pay attention to basic usage patterns, power users, and unused licenses to build a foundation for future insights.

Learn (~3 months): Conduct regular developer surveys to understand both time savings and the overall sentiment around GitHub Copilot. This is also the ideal moment to run A/B tests comparing metrics between developers using Copilot and those who are not. Some organizations also trial different license levels, such as Business or Enterprise, to see which version delivers more value. By the end of this phase, you’ll have a clear picture of before-and-after performance metrics for the developers using Copilot.

Run (6+ weeks and ongoing): By now, GitHub Copilot adoption has increased, making it possible to observe the downstream impacts on collective outcomes beyond individual productivity gains. This phase focuses on measuring key performance indicators like Lead Time, Change Failure Rate (CFR), Number of Incidents, and Mean Time to Recovery (MTTR).