Generative AI Boosts Work Productivity by 70%

According to a recent survey report, generative AI has improved the productivity of software engineers by 70%. Developers have witnessed the reduction in task completion time for existing code updates, especially impactful in leveraging  existing codebase functions and reducing development cycle time

The study, conducted by New York-based management consulting firm Zinnov and data engineering and lifecycle company Ness Digital Engineering is based on based on data collected using Ness's Matrix platform from over 100 software engineers, across various use cases and development settings, offering an analysis of their practical experiences in live engineering scenarios. This was over 14 sprints where the team evaluated sprints before and after generative AI deployment. 

“We identified multiple gains from this. We saw productivity enhancements in line with the complexity of tasks and based on the experience of the engineer. The generative AI integration into engineering workflow tasks has led to a significant 38% decrease in the time required to complete tasks, with a notable 48% reduction in task completion time observed among senior engineers,” Sidhant Rastogi, managing partner of Zinnov, told AIM, emphasising that as the engineers get more conversant with generative AI, efficiency will increase further. 

Even though generative has shown promising results in reducing time and bossing production, concerns arise about the potential negative impact on junior developers' learning opportunities by limiting exposure to these tasks. 

“Generative AI accelerates coding updates, allowing engineers to focus on business context and logic. This shift elevates the workload's nature and empowers senior engineers to guide junior developers, expediting learning and encouraging innovative contributions to projects,” commented Rastogi.

Key Findings

Besides improving productivity, the report shows that senior engineers experienced a 48% reduction in task completion time, enabling better planning and support for junior engineers. 

Generative AI contributed to approximately a 10% reduction in high code complexity tasks, helping engineers in navigating complex coding scenarios efficiently. Additionally, there was a 70% improvement in engagement due to simplified tasks and a more collaborative work environment, leading to a positive professional experience.

“Generative AI significantly impacts routine sustenance tasks in software development, such as legacy code maintenance and updates. Automated tasks, like generating test cases from existing code, ensure continuous alignment with evolving software architecture, reducing regression bugs and ensuring quality assurance. It also facilitates global collaboration, breaks down knowledge barriers, and creates a cohesive product development environment, enhancing decision-making and employee engagement,” Vikas Basra, global head, IE practice, Ness Digital Engineering, told AIM about the report.