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Generative AI implementation for engineering workloads has resulted in a noteworthy 38% reduction in task completion time, with a 48% improvement among Senior Engineers. According to the first-of-its-kind study jointly done by Zinnov and Ness Digital Engineering, Generative AI implementation can increase the potential for globalizing a higher number of products, as well as transforming entire organizational structures. The study is aimed at empowering CXOs of large enterprises, global software engineering capability centers, and software product companies to transform engineering productivity.
This unique study was conducted across a cohort of more than 100 engineers working in live engineering environments. The diversity of experience levels and projects spanning myriad business scenarios, new product development, proof of concept projects, and existing product enhancements spotlights a wide array of software engineering environments. The impact of Generative AI on task completion time was highest for projects that involved repeatable sustenance activities, including existing code updates up to 70%. Engineers witnessed maximum impact when it came to utilizing existing functions within the codebase, which led to a reduction in the development cycle time. AI tools also provided insights on optimizing code structures and efficiencies, thus enhancing the overall code quality and performance.
Generative AI implementation enhanced senior engineers’ efficiency, which in turn increased their commit rate compared to junior engineers. This can primarily be attributed to senior engineers’ ability to provide better prompts aligned with project context, understand and review code suggestions provided by Generative AI tools, and break down complex problems into simpler tasks. This can also lead to the optimization of workflow efficiencies for senior engineers, which will challenge the traditional cost paradigm of the organization. The study highlights the transformative ability of Generative AI adoption across organization types, across verticals.
Generative AI will eventually reduce the number of junior-level engineers across organizations, as it takes on simplistic coding tasks. In turn, organizational structures will see a marked shift towards a leaner shape, with a smaller base. In fact, Generative AI implementation will necessitate reshaping of team skill dynamics, with more emphasis on overseeing, interpreting output, and optimizing performance. Workforce reskilling will become a strategic imperative since organizations will shift from focusing entirely on technology expertise to emphasizing on domain expertise. The senior engineers’ enhanced efficiency will result in more cost-effective production of story points1, thus resulting in decreased cost/story point, even with top-heavy talent structures.
Though Generative AI tools have proven effective in addressing a range of code complexities, engineers do acknowledge the indispensable role that humans play, specifically in complex coding scenarios. In instances that demand deep creativity and problem-solving capabilities, Generative AI may struggle if the available data is insufficient or fails to represent real-world complexities adequately. The challenge also intensifies when interpreting Generative AI suggestions in complex code scenarios where outputs may be intricate and difficult to comprehend, thereby affecting the practical usability of the AI system.
A key finding of the study is that 70% of the engineers experience improved engagement through Generative AI adoption, fostering collaborative team dynamics and collective problem-solving, thus reducing attrition. The reasons behind this improved engagement include the reduced need for engineers to perform repetitive coding tasks manually, minimizing mental efforts, real-time learning assistance with the use of Generative AI tools, and the assistance of Generative AI tools in testing. This has a direct positive impact on global engineering teams to collaborate better and create a cohesive, efficient, and globally integrated product development environment. It would also help engineering leaders evaluate the different ways that globalization of product development can effectively leverage Generative AI. One way to evangelize this across organizations is through dedicated AI Centers of Excellence, which can play a pivotal role in advising, guiding, and overseeing AI projects within the organization. In fact, an AI COE can serve as the vital bridge that connects executive decision-making with the pragmatic implementation of AI initiatives.
Generative AI has become an inseparable part of our daily lives today. Contrary to perceptions that Generative AI will negatively affect the workplace, we have seen that by including Generative AI tools as a part of a comprehensive framework, it can help in boosting employee productivity – specifically in software engineering. Many organizations are yet to tap into the full potential of Generative AI – a targeted approach with the right checks and balances – from identifying the right use cases for Generative AI implementation to assessing the technology’s impact on product development processes and defining the roadmap for leveraging Generative AI to enhance globalization. This will enable enterprises to accelerate innovation, optimize costs, and enable global expansion seamlessly.
The findings from this study not only provide valuable quantitative insights around the impact on productivity but also shed light on the human facets of technology adoption.
1. Story points are units of measurement used to determine how much effort is required to complete a product backlog item or any other piece of work. The team assigns story points based on the work’s complexity, amount, and uncertainty.
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