# Experience with GitHub Copilot for Developer Productivity at Zoominfo

arXiv: https://arxiv.org/abs/2501.13282 · HTML: https://mlsanity.com/paper/2501.13282/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jan 2025.

## Built on by (most cited first, 8 of 8)

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- [The Fools are Certain; the Wise are Doubtful: Exploring LLM Confidence in Code Completion](https://mlsanity.com/paper/2508.16131/) (2025)
- [How AI Coding Agents Communicate: A Study of Pull Request Description Characteristics and Human Review Responses](https://mlsanity.com/paper/2602.17084/) (2026)
- [Pre-Filtering Code Suggestions using Developer Behavioral Telemetry to Optimize LLM-Assisted Programming](https://mlsanity.com/paper/2511.18849/) (2025)
- [The Role of GitHub Copilot on Software Development: A Perspective on Productivity, Security, Best Practices and Future Directions](https://mlsanity.com/paper/2502.13199/) (2025)
- [Semantic Source Code Segmentation using Small and Large Language Models](https://mlsanity.com/paper/2507.08992/) (2025)
- [Echo: Learning from Experience Data via User-Driven Refinement](https://mlsanity.com/paper/2605.21984/) (2026)

## Abstract (arXiv)

This paper presents a comprehensive evaluation of GitHub Copilot's deployment and impact on developer productivity at Zoominfo, a leading Go-To-Market (GTM) Intelligence Platform. We describe our systematic four-phase approach to evaluating and deploying GitHub Copilot across our engineering organization, involving over 400 developers. Our analysis combines both quantitative metrics, focusing on acceptance rates of suggestions given by GitHub Copilot and qualitative feedback given by developers through developer satisfaction surveys. The results show an average acceptance rate of 33% for suggestions and 20% for lines of code, with high developer satisfaction scores of 72%. We also discuss language-specific performance variations, limitations, and lessons learned from this medium-scale enterprise deployment. Our findings contribute to the growing body of knowledge about AI-assisted software development in enterprise settings.