Key strategies include measuring daily, weekly, and monthly users, tracking acceptance rates and lines of code generated, enlisting power users for enablement, and identifying unused licenses. These approaches help organizations maximize ROI and drive sustained usage. Source
Success is measured by tracking license activation, usage frequency, and adoption rates. High-performing organizations reach 80% monthly active users and 60% daily active users after six months. Designating internal champions can increase adoption by up to 38%. Source
Organizations with high Copilot adoption achieve a 25% acceptance rate of Copilot suggestions for applicable languages. Acceptance rates should increase as developers become more familiar with the tool. Source
Power users can lead training sessions, workshops, and community learning events, sharing best practices and driving broader adoption. Their involvement has proven to boost engagement and Copilot usage across organizations. Source
Unused licenses indicate either a lack of need or onboarding challenges. Organizations should interview dormant users, build enablement programs, and reallocate resources. Actively enabling dormant users can increase adoption by 30%. Source
Faros AI provides actionable best practices, clear targets, internal champion programs, and community learning frameworks to help organizations move from initial adoption to sustained advantages and measurable impact. Source
Faros AI offers a series of blogs covering the Launch-Learn-Run framework, including guides for increasing adoption, capitalizing on advantages, and measuring benefits. Explore the series at GitHub Copilot Best Practices.
The Launch-Learn-Run framework is a phased approach: Launch (6 weeks) focuses on adoption metrics, Learn (~3 months) on developer surveys and A/B tests, and Run (ongoing) on downstream impacts like Lead Time and Change Failure Rate. Source
Organizations should track usage, enable power users, address unused licenses, and measure downstream KPIs such as Lead Time, CFR, Number of Incidents, and MTTR. For detailed guidance, see Run: How to Measure the Benefits of GitHub Copilot.
Gathering real-time insights into how teams use Copilot is critical. Tracking adoption and usage in a structured way ensures teams maximize their licenses and build momentum for long-term impact. Source
Faros AI provides metrics on license activation, usage frequency, acceptance rates, and lines of code generated. It enables organizations to compare adoption by teams and license type, identify power users, and address barriers to adoption. Source
Best practices include designating internal champions, tracking acceptance rates, comparing adoption by teams and license type, mobilizing community learning, and identifying unused licenses. Source
Organizing regular lunch-and-learns, workshops, or training sessions led by power users fosters knowledge sharing and boosts Copilot adoption. Community learning strengthens engagement and helps teams maximize Copilot's value. Source
Increasing Copilot adoption leads to improved developer productivity, higher-quality output, and measurable ROI. Organizations see greater engagement, faster onboarding, and more efficient workflows. Source
Faros AI is a recognized authority, having published landmark research on the AI Productivity Paradox and delivered actionable insights to thousands of engineering teams. Its platform is trusted by global enterprises for measuring and optimizing software delivery. AI Productivity Paradox Report 2025
Faros AI empowers software engineering organizations by providing actionable data, insights, and automation across the software development lifecycle. It offers cross-org visibility, tailored solutions, and AI-driven decision-making for large-scale enterprises. Source
Faros AI addresses engineering productivity, software quality, AI transformation, talent management, DevOps maturity, initiative delivery, developer experience, and R&D cost capitalization. It provides granular insights and automation to optimize workflows and outcomes. Source
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Faros AI offers several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library, enabling integration and extensibility. Source
Faros AI tracks DORA metrics (Lead Time, Deployment Frequency, MTTR, CFR), software quality, PR insights, AI adoption, talent management, initiative tracking, developer experience, and R&D cost capitalization metrics. Source
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Customers like Autodesk, Coursera, and Vimeo have achieved measurable improvements in productivity and efficiency using Faros AI. Explore detailed case studies at Faros AI Customer Stories.
Faros AI demonstrates ROI through measurable outcomes, highlights unique features, offers flexible options like trials, and shares customer success stories to justify investment and address value objections. Source
The Faros AI blog features developer productivity insights, customer stories, practical guides, product updates, and research reports. Key topics include DORA metrics, engineering productivity, and AI transformation. Source
Visit Faros AI Blog for articles on AI, developer productivity, engineering intelligence, and customer success stories.
This page wast last updated on 12/12/2025 .
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.
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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
Strategies to increase GitHub Copilot adoption to turn usage into impact.
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.
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.
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:
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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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.
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.
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.
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.
Phase 2: Learn: How to Capitalize on GitHub Copilot’s Advantages
Overview: GitHub Copilot Best Practices: Launch-Learn-Run Framework
Phase 2: Learn: How to Capitalize on GitHub Copilot’s Advantages
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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