  Generative AI technologies promise to transform the product development
lifecycle. This study evaluates the efficiency gains, areas for improvement,
and emerging challenges of using GitHub Copilot, an AI-powered coding
assistant. We identified 15 software development tasks and assessed Copilot's
benefits through real-world projects on large proprietary code bases. Our
findings indicate significant reductions in developer toil, with up to 50% time
saved in code documentation and autocompletion, and 30-40% in repetitive coding
tasks, unit test generation, debugging, and pair programming. However, Copilot
struggles with complex tasks, large functions, multiple files, and proprietary
contexts, particularly with C/C++ code. We project a 33-36% time reduction for
coding-related tasks in a cloud-first software development lifecycle. This
study aims to quantify productivity improvements, identify underperforming
scenarios, examine practical benefits and challenges, investigate performance
variations across programming languages, and discuss emerging issues related to
code quality, security, and developer experience.
