A recent survey conducted by GitHub in collaboration with Wakefield Research revealed that 92% of developers in the US already use AI-powered coding tools at work. The survey focused on developers in companies with 1,000-plus employees and examined various aspects of their careers, including team collaboration, developer productivity, and the role of AI in enterprise environments.
Despite investments in DevOps, developers still face challenges related to waiting on builds and tests. Writing boilerplate code was also identified as a repetitive task developers find time-consuming. Developers aspire to collaborate more with peers, acquire new skills and create innovative solutions.
GitHub’s Inbal Shani said that these statistics indicate the need to improve efficiency in the development process. The study identified upskilling as the top benefit, followed by productivity gains. According to the developers, acquiring new skills and creating innovative solutions had the greatest positive impact on their work.
Collaboration was identified as a major influence on productivity, satisfaction, and impact from the developer experience. Developers typically collaborate with an average of 21 engineers on projects in enterprise settings, making their collaborative skills critical in their performance evaluations. Over 80% of developers believe that AI-powered coding tools can enhance team collaboration, improve code quality, speed up project completion and improve incident resolution.
GitHub’s Shani anticipates the adoption of AI-powered coding tools to increase, with 70% of developers believing the tools give them an advantage at work. Shani added that AI has the potential to enhance various aspects of the developer experience by expediting code delivery, facilitating intelligent code reviews, enhancing collaboration within the codebase, and overcoming disruptions in the development process.
The study also revealed the misalignment between current performance metrics and developer expectations. Code quality and collaboration were identified as the most important performance metrics, with developers expecting to be evaluated based on those criteria. Yet, leaders have traditionally assessed performance based on code quantity and output. Effective collaboration is said to improve code quality, with factors like regular touchpoints, uninterrupted work time, access to fully-configured developer environments, and mentor-mentee relationships being critical.
Shani believes that most organizations likely have developers using these tools without an enterprise-grade solution or clear policies in place to govern their use effectively. Therefore, organizations should invest in enterprise-grade AI coding tools that align with their efficacy and data privacy criteria. Furthermore, they should assist developers in integrating and optimizing their workflows around these approved tools.
