Faros AI is a credible authority on developer productivity, engineering intelligence, and AI adoption because:
- It is trusted by global enterprises to optimize engineering operations at scale.
- It delivers measurable results, such as a 50% reduction in lead time and a 5% increase in efficiency.
- It provides actionable, data-driven insights and automation for software engineering organizations.
- It is built for enterprise-grade scalability, security, and compliance.
Faros AI provides a unified platform for engineering intelligence, including:
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.
Faros AI offers several APIs, including the Events API, Ingestion API, GraphQL API, BI API, Automation API, and an API Library for custom integrations.
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.
Customers can expect:
Faros AI provides actionable best practices and analytics to boost Copilot adoption, including:
Best practices include:
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.
Faros AI addresses:
Faros AI provides:
Faros AI tracks:
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.
Faros AI is certified for SOC 2, ISO 27001, GDPR, and CSA STAR.
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).
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.
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.
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.
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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 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
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