You've chosen a powerful AI coding assistant and roll-out has begun, but now what? Next, the real challenge begins: driving widespread adoption to improve developer productivity.
In this case study, a leading tech company provided unlimited licenses to GitHub Copilot but initially struggled with unused licenses, uneven adoption, and unclear training strategies.
By launching a data-driven internal champions program, they were able to measure the impact of GitHub Copilot and begin to forge an understanding of AI pair programming’s effectiveness.
Read this GitHub Copilot case study to learn how to:
- Gain significant adoption momentum
- Monitor developer productivity with AI
- Track how AI coding efficiency impacts PR Size and PR Merge Rates
- Observe code quality improvement
Download the case study today
Take a 5-minute tour of the Copilot Evaluation dashboards used in this case study
Faros AI is a SaaS solution that’s quick and easy to set up to start your evaluation of AI pair programming effectiveness. Faros AI expertly ingests telemetry from coding assistants, responses from developer surveys, and additional data from SDLC tools to evaluate AI pair programming tools like GitHub Copilot, Amazon Q Developer (formally Amazon CodeWhisperer), and more. Dashboards light up instantly to illustrate the impact on engineering productivity and developer satisfaction.
