Measuring the Impact of AI Coding Assistants

Track adoption, usage patterns, and efficiency gains of AI-powered coding assistants like GitHub Copilot.

Maximize the value of your AI investments

AI Coding Assistant tools like GitHub Copilot promise productivity gains, but are they truly delivering for your teams? Oobeya’s AI Coding Assistant Impact module turns that question into measurable answers. Track adoption, engagement, and acceptance trends across your organization so you can maximize the value of your AI investments.

Encourage adoption where it's low

Identify teams or languages with minimal AI assistant usage and apply targeted onboarding or awareness initiatives to boost adoption rates.

Provide targeted enablement for teams struggling with usage

Use detailed engagement and acceptance data to pinpoint skill gaps and deliver focused training, ensuring teams use AI assistants effectively.

Measure the ROI of your AI assistant investment

Link adoption and engagement metrics to measurable productivity gains, proving the business value of your AI assistant deployment.

Explore AI Impact Dashboard >

1. Why Measure AI Coding Assistant Impact?

AI assistants are rapidly entering the developer workflow — but adoption doesn’t always equal impact. Without visibility, you can’t answer key questions:

Are developers actively using AI assistants?
How often do they accept suggestions?
Which teams benefit most?
Are we seeing a measurable productivity gain?

Oobeya gives you a clear, data-backed answer to all of these questions in one place.

Copilot Adoption Over Time: Tracks the number of active and engaged users compared to the total licensed users over time. This chart helps you measure adoption rate and license utilization across teams.

2. Track Adoption & Engagement

Understand how widely AI assistants are being used in your organization.

Active Users: Number of users who have Copilot installed and interacted with it.
Engaged Users: Number of users who accepted at least one Copilot suggestion.
Adoption Rate: Ratio of engaged users to active users: Engaged Users / Active Users

Example: In the last 90 days, adoption reached 73.33% with a +560% increase in active users.

3. Measure Usage Quality, Not Just Quantity

Go beyond “who uses it” — see “how well it’s used.” Track:

Total Suggestions vs. Accepted Suggestions
Acceptance Rate by Suggestions
Acceptance Rate by Lines of Code

Example: 74.3K suggestions → 22.1K accepted → 29.79% acceptance rate.

4. Analyze by Language & IDE

Find where AI assistants deliver the most value:

Which programming languages have the highest acceptance rates?
Which IDEs see the most usage?
Are there teams or languages where adoption is low?

Example: C# leads in usage with 51.9% acceptance for suggestions.

Copilot Usage by Language(Top 5) & Editor: Visualizes Copilot usage by the top 5 most active programming languages and development editors. This widget helps identify where Copilot is most frequently triggered.