The arrival of generative AI has raised expectations for improving software development efficiency. Engineering organizations using AI coding assistants are seeing efficiency gains of about 10% to 15% on average. However, many companies fail to monetize these gains because they are unable to reposition the saved time and resources to productive uses.
Organizations taking a comprehensive approach can see efficiency gains of 30% or more. The extra gains result from going beyond generative AI code generation, using generative AI for other tasks, and taking a more comprehensive approach to improving efficiency, including determining the right baselines and metrics. Developers spend about half their time writing and testing code.
Although they report a 30% improvement from generative AI for these activities, this represents a net efficiency improvement of 15% across developers’ total time. A more comprehensive approach to efficiency includes not only generative AI-assisted code generation and testing but also a thorough look at three dimensions: focusing on the right work, ensuring speedy and high-quality execution, and optimizing resourcing costs.
generative AI’s impact on coding efficiency
The fastest way to improve efficiency is to refocus efforts on work that creates the most value. This includes aligning investments with strategy, weighing expense-to-revenue ratio across products, balancing resource allocation, and linking product strategy to developer priorities. Executing rapidly with high quality involves deploying the full potential of generative AI, continuous integration and delivery, and maintaining a modern architecture.
Two software development organizations might operate at similar speeds and quality but show different cost profiles depending on their model and talent structure. Factors include geographical footprint, outsourcing levels, ratio of senior to junior engineers, and roles of various team members. Many companies struggle to understand their baseline efficiency and measure the improvements from initiatives like generative AI.
About two-thirds of leaders surveyed are not satisfied with their ability to measure these impacts effectively. In conclusion, while generative AI offers significant efficiency gains, realizing its full potential requires a comprehensive approach, from strategic alignment and prioritization to maintaining modern architectures and optimizing resource costs. For most organizations, achieving these gains will demand a careful reconsideration of their current practices and a commitment to continuous improvement.
