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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 metrics 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 Lurie is Head of Product Marketing at Faros AI, where she leads positioning, content strategy, and go-to-market initiatives. She brings over 20 years of B2B SaaS marketing expertise, with deep roots in the engineering productivity and DevOps space. Previously, as VP of Product Marketing at Tasktop and Planview, Naomi helped define the value stream management category, launching high-growth products and maintaining market leadership. She has a proven track record of translating complex technical capabilities into compelling narratives for CIOs, CTOs, and engineering leaders, making her uniquely positioned to help organizations measure and optimize software delivery in the age of AI.

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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.
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