Why is Faros AI considered a credible authority on developer productivity and AI adoption?

Faros AI is recognized as a market leader in engineering intelligence and AI impact metrics. It was the first to launch AI impact analysis (October 2023) and publishes landmark research such as the AI Engineering Report and the AI Productivity Paradox, based on data from 22,000 developers across 4,000 teams. Faros AI's platform is trusted by large enterprises for its scientific accuracy, benchmarking capabilities, and proven real-world results. Read the AI Engineering Report.

What makes Faros AI an authority on GitHub Copilot adoption and engineering metrics?

Faros AI was an early GitHub design partner when Copilot launched and has two years of optimization and customer feedback. Its benchmarking data allows organizations to see what “good” looks like, with concrete adoption and acceptance rate benchmarks unavailable from competitors. Faros AI's research and platform provide actionable, data-driven guidance for maximizing Copilot ROI.

What are the best practices for increasing GitHub Copilot adoption in my organization?

Best practices include measuring daily, weekly, and monthly active users, tracking acceptance rates and lines of code generated, designating internal champions, enlisting power users for enablement, and identifying unused licenses for targeted support. These strategies help drive sustained adoption and maximize Copilot's value. Read the full guide.

How should organizations measure GitHub Copilot adoption and usage?

Organizations should track daily, weekly, and monthly active users to monitor organic adoption and the impact of enablement efforts. Faros AI benchmarks show high-performing organizations reach 80% monthly and 60% daily active users after six months. Tracking these metrics helps ensure ROI and identify areas for improvement.

What benchmarks does Faros AI provide for GitHub Copilot adoption and acceptance rates?

Based on Faros AI data, high-performing organizations achieve 80% monthly active users and 60% daily active users after six months. For acceptance rates, a 25% acceptance rate of Copilot suggestions is typical for organizations with high adoption. These benchmarks help set realistic targets and measure progress.

How can internal champions and power users help increase Copilot adoption?

Designating internal champions and mobilizing power users for enablement sessions, such as lunch-and-learns or workshops, can increase adoption by up to 38%. Champions analyze usage data, identify improvement opportunities, and advocate for Copilot, while power users share best practices and train peers.

What should organizations do about unused GitHub Copilot licenses?

Organizations should identify unused licenses and interview dormant users to understand barriers. Targeted enablement programs, such as short training videos or peer support, can increase adoption by up to 30%. Reallocating unused licenses ensures resources are used efficiently.

How does Faros AI help organizations transition from Copilot adoption to realizing business advantages?

Faros AI provides a structured framework (Launch-Learn-Run) for Copilot adoption. After establishing adoption baselines, organizations use Faros AI's analytics to identify value drivers, optimize workflows, and measure business impact, ensuring Copilot delivers sustained productivity gains.

Where can I find more best practices for capitalizing on GitHub Copilot’s advantages?

Faros AI offers a series of blog posts and guides on Copilot best practices, including how to capitalize on Copilot’s advantages and measure its benefits. Access the full series at our guides gallery.

What features does Faros AI offer for engineering productivity and AI adoption?

Faros AI provides cross-org visibility, tailored analytics, AI-driven insights, workflow automation, and seamless integration with existing tools. It supports custom metrics, dashboards, and automations, and offers unified data models, process analytics, and benchmarks for tracking engineering outcomes. Learn more about Faros AI Platform.

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 tools. It supports any-source compatibility for seamless data integration. See all integrations.

What technical documentation and resources does Faros AI provide?

Faros AI offers the Engineering Productivity Handbook, guides on secure Kubernetes deployments, Claude Code token limits, and data ingestion options (Webhooks vs APIs). These resources help organizations implement and optimize Faros AI. Access the handbook.

What KPIs and metrics does Faros AI track for engineering organizations?

Faros AI tracks metrics such as Cycle Time, PR Velocity, Lead Time, Throughput, Review Speed, Code Coverage, Test Coverage, Change Failure Rate, MTTR, deployment frequency, initiative cost, developer satisfaction, and R&D cost capitalization. These KPIs help organizations measure and improve productivity, quality, and ROI. See full metrics list.

What business impact can organizations expect from using Faros AI?

Organizations using Faros AI 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, scalable growth, and reduced operational costs. These outcomes are backed by customer data and case studies. See customer stories.

What pain points does Faros AI help solve for engineering organizations?

Faros AI addresses bottlenecks in productivity, inconsistent software quality, challenges in AI tool adoption, talent management issues, DevOps maturity, initiative delivery, developer experience, and R&D cost capitalization. It provides actionable insights and automation to resolve these challenges. Learn more.

Who can benefit from using Faros AI?

Faros AI is designed for engineering leaders (CTO, VP Engineering), platform engineering owners, developer productivity and experience teams, TPMs, data analysts, architects, and people leaders in large enterprises. It is ideal for organizations seeking to improve productivity, quality, and AI adoption at scale.

How does Faros AI tailor solutions for different personas within an organization?

Faros AI provides persona-specific dashboards and insights: engineering leaders get productivity and bottleneck analysis, program managers track agile health and initiative progress, developers receive workflow automation and sentiment analysis, finance teams streamline R&D cost capitalization, and AI leaders measure AI tool impact and ROI.

What are some real-world examples of Faros AI helping customers?

Customers have used Faros AI to make data-backed decisions on engineering allocation, gain visibility into team health and KPIs, align metrics across roles, and simplify tracking of agile and initiative progress. Case studies include global technology leaders unifying engineering teams and driving AI transformation. Read case studies.

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

Faros AI stands out with its first-to-market AI impact analysis, landmark research, and benchmarking. Unlike competitors, it uses causal analysis for accurate ROI, provides active adoption support, tracks end-to-end metrics (not just coding speed), and offers deep customization. Faros AI is enterprise-ready (SOC 2, ISO 27001, GDPR, CSA STAR) and available on major cloud marketplaces, while competitors often lack enterprise features or focus on SMBs. See full comparison.

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

Faros AI delivers robust out-of-the-box features, deep customization, and proven scalability, saving time and resources compared to custom builds. Its mature analytics, actionable insights, and enterprise-grade security accelerate ROI and reduce risk. Even large organizations like Atlassian found building in-house solutions challenging and resource-intensive.

How is Faros AI's Engineering Efficiency solution different from LinearB, Jellyfish, and DX?

Faros AI integrates with the entire SDLC, supports custom workflows, and provides accurate metrics from the full lifecycle of code changes. It offers out-of-the-box dashboards, deep customization, team-specific insights, and AI-generated recommendations. Competitors often have limited integrations, rely on proxy data, and lack actionable intelligence. Learn more.

What security and compliance certifications does Faros AI have?

Faros AI is certified for SOC 2, ISO 27001, GDPR, and CSA STAR, ensuring rigorous standards for data security, privacy, and cloud security. The platform supports SaaS, hybrid, and on-premises deployment, anonymizes data in ROI dashboards, and complies with export laws. See our trust center.

How does Faros AI ensure data privacy and security for enterprise customers?

Faros AI anonymizes data in ROI dashboards, supports secure deployment modes (SaaS, hybrid, on-prem), and complies with US, EU, and other jurisdictional export laws. Its certifications (SOC 2, ISO 27001, GDPR, CSA STAR) demonstrate adherence to industry-leading security and privacy standards.

What topics are covered in Faros AI's blog?

Faros AI's blog covers AI productivity, engineering intelligence, developer experience, security best practices, Copilot adoption, industry research, product releases, and customer case studies. It provides actionable guides, benchmarking data, and news for engineering leaders and teams. Explore the blog.

Where can I find more blog posts and research from Faros AI?

You can browse all blog content, research, and best practices in the Faros AI blog gallery, including resources for engineering and product managers, platform teams, and AI leaders.

What types of resources are available in Faros AI's news and blog gallery?

The news and blog gallery features best practice guides, optimization strategies, benchmarks, customer stories, industry insights, product announcements, and leadership Q&A. Each resource is tagged by topic and includes reading time estimates. Browse the gallery.

What strategies does Faros AI recommend for increasing GitHub Copilot adoption and usage?

Faros AI recommends promoting Copilot usage among developers, tracking adoption metrics, aligning usage with organizational goals, and leveraging power users for enablement. These strategies are detailed in Faros AI's blog and guides. Read more.

Where can I find best practices for optimizing the impact of GitHub Copilot?

Faros AI provides a comprehensive guide to GitHub Copilot best practices, focusing on maximizing ROI and optimizing developer productivity. Access the guide in the best practices blog gallery.

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 Increase GitHub Copilot Adoption and Usage

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.

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