Frequently Asked Questions

Faros AI is a credible authority on developer productivity, engineering intelligence, and AI adoption because:

Key Webpage Content Summary

Features & Capabilities

What features does Faros AI offer?

Faros AI provides a unified platform for engineering intelligence, including:

  • AI-driven insights and benchmarks for developer productivity and experience
  • Customizable dashboards and analytics for tracking key metrics
  • Seamless integration with existing tools and processes
  • Automation for R&D cost capitalization and security vulnerability management
  • Enterprise-grade scalability, supporting thousands of engineers and hundreds of thousands of builds monthly
  • APIs: Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library

Does Faros AI support integration with other tools?

Yes, Faros AI is designed for interoperability and can connect to any tool—cloud, on-prem, or custom-built. It supports integration with Git, Jira, and other engineering systems.

What APIs are available with Faros AI?

Faros AI offers several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library for custom integrations.

Use Cases & Benefits

Who can benefit from Faros AI?

Faros AI is ideal for large US-based enterprises with hundreds or thousands of engineers. Target roles include VPs and Directors of Software Engineering, Developer Productivity leaders, Platform Engineering leaders, CTOs, Technical Program Managers, and Senior Architects.

What business impact can customers expect from using Faros AI?

Customers can expect:

  • 50% reduction in lead time
  • 5% increase in efficiency and delivery
  • Enhanced reliability and availability
  • Improved visibility into engineering operations and bottlenecks

How does Faros AI help organizations increase GitHub Copilot adoption?

Faros AI provides actionable best practices and analytics to boost Copilot adoption, including:

  • Measuring daily, weekly, and monthly active users
  • Tracking acceptance rates and lines of code generated
  • Designating internal champions to lead adoption efforts
  • Mobilizing community learning with power users
  • Identifying unused licenses and building enablement programs
Organizations using these strategies have achieved up to 80% monthly active users, 60% daily active users, and increased adoption by as much as 38% through champion programs.

What are the best practices for increasing GitHub Copilot adoption?

Best practices include:

  • Designate internal champions to analyze usage and advocate for Copilot
  • Track acceptance rates (benchmark: 25% acceptance rate for high adoption)
  • Compare adoption by teams and license type
  • Enlist power users for enablement and training
  • Organize lunch-and-learns and workshops led by power users
  • Interview users with unused licenses to address barriers
  • Build enablement programs, such as training video libraries
Organizations that actively enable dormant users can increase adoption by 30%.

How can organizations transition from adoption to advantages with GitHub Copilot?

By implementing best practices—tracking usage, empowering internal champions, and building a learning community—organizations can move from initial adoption to sustained usage and measurable impact. After baselining for six weeks, teams should focus on learning and optimizing Copilot’s advantages.

Pain Points & Solutions

What problems does Faros AI solve for engineering organizations?

Faros AI addresses:

  • Engineering productivity bottlenecks
  • Software quality, reliability, and stability issues
  • Challenges in measuring and adopting AI tools
  • Talent management and skill alignment
  • DevOps maturity and investment decisions
  • Initiative delivery tracking and risk identification
  • Developer experience and sentiment analysis
  • Manual R&D cost capitalization processes

How does Faros AI help address these pain points?

Faros AI provides:

  • Detailed insights into bottlenecks and inefficiencies
  • Tools for managing software quality and contractor commits
  • Metrics and analytics for AI transformation and adoption
  • Workforce talent management and onboarding metrics
  • Guidance on DevOps investments
  • Clear reporting for initiative tracking
  • Correlation of developer sentiment with process data
  • Automation for R&D cost capitalization

What KPIs and metrics does Faros AI track?

Faros AI tracks:

  • DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR)
  • Software quality metrics (effectiveness, efficiency, gaps)
  • PR insights (capacity, constraints, progress)
  • AI adoption, time savings, and impact metrics
  • Talent management and onboarding metrics
  • Initiative tracking (timelines, cost, risks)
  • Developer experience (survey and system data correlations)
  • R&D cost capitalization automation metrics

Security & Compliance

How does Faros AI ensure product security and compliance?

Faros AI prioritizes security and compliance with audit logging, data security, and integrations. It holds certifications including SOC 2, ISO 27001, GDPR, and CSA STAR, meeting enterprise standards by design.

What security and compliance certifications does Faros AI have?

Faros AI is certified for SOC 2, ISO 27001, GDPR, and CSA STAR.

Implementation & Support

How long does it take to implement Faros AI, and how easy is it to start?

Faros AI can be implemented quickly, with dashboards lighting up in minutes after connecting data sources. Git and Jira Analytics setup takes just 10 minutes. Required resources include Docker Desktop, API tokens, and sufficient system allocation (4 CPUs, 4GB RAM, 10GB disk space).

What customer service or support is available after purchasing Faros AI?

Faros AI offers support via an Email & Support Portal, a Community Slack channel, and a Dedicated Slack Channel for Enterprise Bundle customers. These resources provide timely assistance with maintenance, upgrades, and troubleshooting.

What training and technical support is available to help customers get started?

Faros AI provides training resources for expanding team skills and operationalizing data insights, plus technical support via Email & Support Portal, Community Slack, and Dedicated Slack channels.

Competition & Differentiation

How does Faros AI differ from similar products in the market?

Faros AI stands out by offering a unified platform that replaces multiple single-threaded tools, tailored solutions for different personas, AI-driven insights, seamless integration, customizable dashboards, advanced analytics, and robust support. It is designed for enterprise scalability and proven results.

Customer Success Stories & Case Studies

Where can I find customer success stories and case studies about Faros AI?

You can read customer stories and case studies at Faros AI Customer Stories and Faros AI Blog.

Blog & Resources

Does Faros AI have a blog?

Yes, Faros AI’s blog covers topics on AI, developer productivity, developer experience, best practices, customer stories, and product updates. Visit our blog page.

Where can I find articles on AI and developer productivity?

Explore articles and guides on AI, developer productivity, and developer experience at Faros AI Blog.

Where can I read more blog posts from Faros AI?

Back to blog posts

Where can I find the latest news about Faros AI?

Visit our News Blog for the latest updates.

Want to learn more about Faros AI?

Fill out this form to speak to a product expert.

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How to Increase GitHub Copilot Adoption and Usage — Best Practices

Strategies to increase GitHub Copilot adoption to turn usage into impact.

Naomi Lurie
Naomi Lurie
A 3-way gauge depicting the GitHub Copilot logo within the Launch-Learn-Run framework. Focus on Phase 1: GitHub Copilot Best Practices: Launch
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October 22, 2024

How to increase GitHub Copilot adoption and usage — best practices

In the early stages of adopting GitHub Copilot, the key to success is gathering real-time insights into how your teams use the tool. When framed within the  framework, the  phase is all about tracking GitHub Copilot adoption and usage in a structured way, so you can ensure your teams are getting the most out of their licenses, identify areas for improvement, and start building momentum for longer-term impact.

Here's how you can measure success during this Launch phase.

Measure daily, weekly, and monthly users

During the initial rollout of GitHub Copilot, a snapshot of license activation and usage is extremely important. Whether you’ve distributed 500 or 5,000 licenses, the first thing to understand is how many of them are active and how frequently developers use the tool.

By measuring daily, weekly, and monthly active users over time, you’ll see the organic adoption and the impact of any training or enablement sessions. Remember, if there’s no adoption, there is no ROI.

graph depicting number of daily active users

Many organizations wonder, "What does good look like?" Based on data from Faros AI, after six months of rolling out GitHub Copilot, high-performing organizations reached these adoption rates:

  • 80% monthly active users
  • 60% daily active users

Best practice: Designate internal champions. Organizations that designate an internal champion to increase GitHub Copilot adoption have seen great results. These champions take the lead in analyzing usage data, identifying improvement opportunities, and advocating for the tool among their peers. Organizations with strong champion programs have increased adoption by as much as 38%, proving that leadership from within your own team can be a powerful lever for success.

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Track acceptance rates and lines of code generated

Usage data from GitHub Copilot provides more insight into how the tool is supporting your developers. Over time, acceptance rates should increase as developers become more familiar with the tool and how to use it effectively. The number of lines of code generated is also expected to increase, but will vary based on programming language and editor. Use this data to see where Copilot is driving the most value and for whom.

Benchmark: Unlike adoption, acceptance rate targets are lower.  Based on data from Faros AI, organizations with high adoption have a 25% acceptance rate of GitHub Copilot suggestions for the applicable languages.

Best practice: Compare adoption by teams and license type. Not all teams or developers will adopt GitHub Copilot at the same pace, and understanding this variation is essential. Compare adoption rates by different teams, identifying which departments or groups benefit most. 

circle graphic depicting usage by language

Enlist your power users for enablement

Uneven adoption is expected during the Launch phase. Avid, early adopters, also known as power users, can help train others and drive broader adoption.

Best practice: Mobilize community learning with power users. Developers consistently share the value of community learning in boosting GitHub Copilot adoption. Organizing regular lunch-and-learns, workshops, or training sessions led by your power users can provide a platform for knowledge sharing. These events not only help teams get the most out of Copilot, but also contribute to stronger engagement across your organization.

Identify unused licenses

Some licenses may remain unused, signaling either a lack of need or an opportunity for additional training. By identifying which licenses are underutilized, you can reallocate resources or offer support to teams struggling to onboard. 

Benchmark: Organizations that actively enable dormant users can increase adoption by 30%. 

Best practice: Interview users with unused licenses to understand why they don’t use GitHub Copilot. Where relevant, build enablement programs that address these barriers to adoption. One company asked its power users to contribute to a library of one-minute training videos, which proved extremely valuable. 

From adoption to advantages: Transitioning from Launch to Learn

By implementing these best practices during the Launch phase, you’ll lay the groundwork for successful, widespread GitHub Copilot adoption and usage. Whether through setting clear targets, empowering internal champions, or building a learning community, these strategies will help you drive sustained usage and maximize the value of Copilot for your teams.

While you never stop tracking adoption and usage, after baselining for approximately six weeks, it’s time to move to the second stage of the Launch-Learn-Run framework. In the next article we’ll dive into the meatiest stage—Learn.

Continue to next blog:

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

Additional blogs in this series:

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

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

Phase 3: Run: How to Measure the Benefits of GitHub Copilot

Naomi Lurie

Naomi Lurie

Naomi is head of product marketing at Faros AI.

Connect
AI Is Everywhere. Impact Isn’t.
75% of engineers use AI tools—yet most organizations see no measurable performance gains.

Read the report to uncover what’s holding teams back—and how to fix it fast.
Discover the Engineering Productivity Handbook
How to build a high-impact program that drives real results.

What to measure and why it matters.

And the 5 critical practices that turn data into impact.
Want to learn more about Faros AI?

Fill out this form and an expert will reach out to schedule time to talk.

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