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Intuition to Evidence: Measuring AI's True Impact on Developer Productivity

Published: September 24, 2025 | arXiv ID: 2509.19708v1

By: Anand Kumar , Vishal Khare , Deepak Sharma and more

Potential Business Impact:

Helps programmers write code much faster.

Business Areas:
Machine Learning Artificial Intelligence, Data and Analytics, Software

We present a comprehensive real-world evaluation of AI-assisted software development tools deployed at enterprise scale. Over one year, 300 engineers across multiple teams integrated an in-house AI platform (DeputyDev) that combines code generation and automated review capabilities into their daily workflows. Through rigorous cohort analysis, our study demonstrates statistically significant productivity improvements, including an overall 31.8% reduction in PR review cycle time. Developer adoption was strong, with 85% satisfaction for code review features and 93% expressing a desire to continue using the platform. Adoption patterns showed systematic scaling from 4% engagement in month 1 to 83% peak usage by month 6, stabilizing at 60% active engagement. Top adopters achieved a 61% increase in code volume pushed to production, contributing to approximately 30 to 40% of code shipped to production through this tool, accounting for an overall 28% increase in code shipment volume. Unlike controlled benchmark evaluations, our longitudinal analysis provides empirical evidence from production environments, revealing both the transformative potential and practical deployment challenges of integrating AI into enterprise software development workflows.

Page Count
16 pages

Category
Computer Science:
Software Engineering