# Measuring the Impact of AI Coding Assistants

### 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.

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#### **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.
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#### **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.
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#### **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.
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<p align="center"><a href="https://oobeya.io/schedule-a-demo" class="button primary">Explore AI Impact Dashboard ></a></p>

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### 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.**

<figure><img src="/files/jLOM46iV3YrFcfzoo2Cj" alt=""><figcaption><p>Copilot Adoption Over Time</p></figcaption></figure>

{% hint style="info" %}
**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.
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### **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.

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### **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**.

<figure><img src="/files/aHoW3tAo7gXhO5klP83H" alt=""><figcaption><p>Copilot Engagement &#x26; Acceptance Trends</p></figcaption></figure>

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### **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.

<figure><img src="/files/fpz8bz3d6QbFzIK7u4W8" alt=""><figcaption><p>Copilot Usage by Language(Top 5) &#x26; Editor</p></figcaption></figure>

{% hint style="info" %}
**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.
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### **5. Feature Usage Insights**

Identify the most impactful AI features:

* In-IDE code completions
* Chat-based interactions (GitHub.com, IDE chat)
* Pull request integrations

> *Example:* IDE code completions dominate usage compared to chat and PR suggestions.

<figure><img src="/files/mIxerrDj7JbVc4rVib5L" alt=""><figcaption><p>Copilot Feature Usage Over Time</p></figcaption></figure>

{% hint style="info" %}
**Copilot Feature Usage Over Time:** Shows usage trends of various Copilot features (IDE Code Completions, Chat, PR) across time periods.
{% endhint %}

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### 6. **Team & User-Level Metrics**

Pinpoint your most engaged teams and identify where training is needed:

* Most active team
* Most efficient usage team
* Users with no activity in the last 7 or 30 days

<figure><img src="/files/EsWJQggPtlUNAKLraCqK" alt=""><figcaption><p><strong>Team Usage Metrics</strong></p></figcaption></figure>

{% hint style="info" %}
**Team Usage Metrics:** A per-team breakdown of AI assistant usage, including activation, engagement, and suggestion effectiveness.
{% endhint %}

<figure><img src="/files/1FZT4rUP5SRFdtaaXRHv" alt=""><figcaption><p>Copilot Seats, User-Level Metrics - Users with no activity in the last 7 or 30 days</p></figcaption></figure>

***

<p align="center"><a href="https://oobeya.io/schedule-a-demo" class="button primary">Explore AI Impact Dashboard ></a></p>


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