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Introduction
Artificial intelligence is no longer judged simply by how many developers use it. Organizations now want concrete evidence that AI is improving software development, reducing bottlenecks, and accelerating delivery without sacrificing code quality. Measuring AI adoption has evolved beyond counting users or generated code. The real question has become: Does AI actually help engineering teams ship better software faster?
To answer that question, GitHub has expanded the Copilot Usage Metrics API with two new performance indicators focused on the code review process. These additions allow enterprise leaders and engineering managers to examine how AI adoption influences review speed and collaboration throughout the software development lifecycle. Rather than relying on assumptions, organizations can now compare review efficiency across different AI adoption phases and gain a much clearer understanding of Copilot’s real-world impact.
GitHub Expands Copilot Usage Metrics API
GitHub has introduced two new code review velocity metrics to the Copilot Usage Metrics API, extending the existing AI adoption phase reporting available for enterprise and organization administrators.
Previously, reports focused primarily on pull request merge counts and merge times. While those metrics provided useful insights into deployment efficiency, they offered only part of the picture. Software development depends heavily on peer reviews, collaboration, and iterative improvements before code reaches production.
With this latest enhancement, organizations can now analyze how quickly pull requests receive their first review and how many review rounds they typically undergo before being merged. These measurements provide a deeper understanding of engineering productivity and software delivery performance.
New Metric One: Average Minutes Until First Review
Measuring Initial Review Speed
The first newly introduced metric is avg_pull_requests_minutes_to_review.
This value represents the median number of minutes between the creation of a pull request and its first code review.
Instead of simply tracking how long it takes to merge code, this metric focuses on one of the earliest and most important stages of software collaboration. Fast initial reviews help developers receive feedback quickly, reduce context switching, and maintain development momentum.
Engineering managers can now determine whether teams with stronger GitHub Copilot adoption receive reviews faster than teams still in earlier stages of AI integration.
New Metric Two: Review Cycles Before Merge
Understanding Development Efficiency
GitHub has also added avg_pull_requests_review_cycles.
This metric calculates the median number of review submissions that a pull request receives before it is successfully merged into the main codebase.
A lower number of review cycles may indicate that developers are submitting higher-quality code from the beginning, potentially assisted by GitHub Copilot’s suggestions, inline explanations, and code generation capabilities.
Conversely, a higher review-cycle count can highlight opportunities for additional developer training, better coding practices, or improved AI utilization.
Why These Metrics Matter
Looking Beyond Code Generation
Many organizations measure AI success by counting generated lines of code or tracking adoption percentages. While these numbers demonstrate usage, they rarely reveal business value.
The newly introduced review metrics focus on operational outcomes instead of raw activity.
Engineering teams spend significant time waiting for reviews, responding to reviewer comments, updating pull requests, and coordinating releases. These stages often become hidden productivity bottlenecks.
By analyzing review latency alongside AI adoption phases, engineering leaders can begin answering questions such as:
Are experienced Copilot users producing cleaner code?
Do AI-assisted developers require fewer revisions?
Are review queues moving faster?
Which departments benefit most from AI adoption?
Where should additional Copilot training be prioritized?
Instead of relying on intuition, organizations gain measurable data that supports strategic engineering decisions.
Metrics Are Limited to Successfully Merged Pull Requests
Ensuring Accurate Productivity Measurements
GitHub designed these new measurements carefully to avoid misleading statistics.
Both review metrics only include pull requests that are ultimately merged.
Every merged pull request is counted exactly once and is attributed to the day it is merged.
This methodology avoids double-counting while ensuring that only completed engineering work contributes to the reported statistics.
Pull requests that receive reviews but are later abandoned or closed without merging are intentionally excluded from these calculations.
As a result, organizations obtain cleaner and more meaningful productivity insights.
Available Across Enterprise and Organization Reports
Consistent Reporting at Every Scale
The new review metrics are now available in both Enterprise and Organization Copilot Usage reports.
They appear in:
1-Day Reports
These reports provide short-term visibility into daily engineering activity and adoption trends.
28-Day Reports
Longer reporting periods help organizations identify sustained improvements, seasonal variations, and long-term productivity gains.
This dual reporting structure allows engineering leadership to monitor both immediate performance changes and broader organizational progress.
AI Adoption Phases Remain Unchanged
Maintaining Reporting Consistency
Although GitHub has expanded the available metrics, it has not modified the AI adoption phase definitions.
Organizations can continue using their existing adoption cohorts while benefiting from richer analytics.
This consistency allows companies to compare historical data without needing to rebuild dashboards or adjust previous reporting methodologies.
A Bigger Shift Toward Data-Driven Engineering
AI Success Must Be Measurable
As AI becomes deeply integrated into software development, executive leadership increasingly expects measurable returns on investment.
The latest Copilot API enhancement reflects a broader industry trend toward evidence-based engineering management.
Rather than asking whether developers like AI tools, organizations are beginning to ask whether AI reduces engineering costs, accelerates delivery timelines, improves collaboration, and increases software quality.
Review latency and review-cycle metrics provide a stronger foundation for answering those questions with actual operational data instead of subjective opinions.
What Undercode Say:
Deep Editorial Analysis
GitHub’s decision to introduce review-focused metrics represents a significant evolution in how AI productivity is evaluated.
For years, organizations have celebrated AI adoption using vanity metrics, including user counts, generated code, or feature usage. Those statistics rarely demonstrate measurable business value.
Engineering productivity depends on the entire development pipeline.
Writing code is only one stage.
Code reviews often consume more engineering hours than implementation itself.
Waiting for reviewer approval delays releases.
Multiple review rounds increase engineering costs.
Long review queues reduce developer focus.
Fast feedback improves software quality.
Reduced review cycles typically indicate stronger code submissions.
GitHub Copilot may indirectly improve developer confidence before pull requests are opened.
Cleaner code usually generates fewer reviewer comments.
Teams with mature AI adoption may naturally develop better engineering habits.
However, correlation should never automatically imply causation.
Organizations should compare repository complexity.
Team size matters.
Programming language differences influence review times.
Security-sensitive repositories naturally require longer reviews.
Compliance requirements may increase review cycles regardless of AI usage.
Review culture varies dramatically between companies.
Open source projects often experience different workflows than enterprise software.
Engineering managers should analyze multiple KPIs together.
Deployment frequency remains important.
Lead time still matters.
Change failure rate should not be ignored.
Developer satisfaction also contributes to long-term productivity.
The new metrics create opportunities for richer engineering dashboards.
Organizations can combine API results with CI/CD statistics.
Repository health metrics become more meaningful when correlated with review velocity.
AI adoption should remain a tool rather than the objective itself.
Successful organizations optimize workflows instead of maximizing AI usage percentages.
The strongest engineering teams will use these metrics to identify bottlenecks rather than reward individuals.
Transparency encourages continuous improvement.
Measurement drives accountability.
Context prevents misleading conclusions.
Ultimately, GitHub is moving the conversation away from “How much AI are we using?” toward “How much value is AI creating?”
That is a far more meaningful discussion for modern software engineering.
Deep Analysis
Using the API and Engineering Analysis Commands
Administrators and DevOps teams can integrate these metrics into broader engineering observability workflows.
curl -H "Authorization: Bearer <TOKEN>" https://api.github.com/ jq '.totals_by_ai_adoption_phase' grep "avg_pull_requests_minutes_to_review" report.json grep "avg_pull_requests_review_cycles" report.json cat report.json | jq diff previous-report.json latest-report.json watch -n 3600 ./collect_metrics.sh git log --stat gh api python analyze_copilot_metrics.py
These commands illustrate how engineering teams can retrieve, parse, compare, and automate Copilot usage reports while integrating them into DevOps dashboards and performance monitoring systems.
✅ GitHub has introduced two new Copilot Usage Metrics API fields that measure review latency and review cycles.
✅ The new metrics apply only to merged pull requests, ensuring each contribution is counted once after successful integration into the codebase.
✅ AI adoption phase definitions remain unchanged, meaning organizations can compare new review metrics with existing adoption cohorts without modifying historical reporting.
Prediction
(+1) Positive Outlook
Organizations will increasingly rely on review velocity metrics instead of simple AI adoption percentages when evaluating engineering performance.
Future GitHub analytics are likely to introduce even deeper insights, including code quality, defect reduction, reviewer workload, and AI-assisted development trends.
As engineering leaders gain access to richer operational data, AI investments will become easier to justify through measurable improvements in software delivery speed and collaboration.
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