The AI Copilot Impact report provides comprehensive insights into how your teams are adopting and utilizing GitHub Copilot, helping you understand the value and impact of AI-assisted development in your organization.

This report monitors Copilot adoption across your organization, tracking engagement levels, acceptance rates, and usage patterns by language and IDE to identify trends and opportunities for AI-assisted development.

First, the report displays Total Engaged Users over time, showing daily counts of developers actively using Copilot. This helps identify adoption trends and usage consistency across your organization.

Next, it breaks down Total Code Suggestions by language and over time. This visualization reveals which languages see the most Copilot assistance and can help identify where AI coding assistance provides the most value.

The report tracks Code Suggestions Accepted %, an important metric for understanding Copilot's effectiveness. This acceptance rate shows what percentage of Copilot's suggestions developers actually use, broken down by language. Higher acceptance rates indicate better alignment between Copilot's suggestions and your team's coding standards and the task at hand.

For additional insights, the report shows Lines Accepted % by language over time. This metric helps you understand not just whether suggestions are accepted, but how much code is being generated through AI assistance.

Total Lines Suggested provides context about the volume of AI assistance being offered, which when compared with lines accepted, gives you a view of Copilot's utility. The report also tracks Copilot IDE Chat usage, showing engagement with Copilot's conversational features. This includes both the number of engaged users and total chat interactions, helping you understand how developers are using Copilot for problem-solving beyond just code completion.

Finally, Engaged Users by IDE and Engaged Users by Language provide detailed breakdowns of where and how Copilot is being used, helping you identify patterns and opportunities for broader adoption.

Key Use Cases

• Adoption Tracking: Identify teams which might benefit from additional training or support
• Language Insights: Understand which languages benefit most from AI assistance
• Productivity Metrics: Correlate Copilot usage with other development metrics to assess productivity impact
• License Optimization: Ensure Copilot licenses are allocated to developers who actively use the tool

By monitoring these metrics over time, engineering leaders can make data-driven decisions about AI tool adoption, identify best practices for AI-assisted development, and ensure their teams are getting maximum value from GitHub Copilot.