Generative AI cuts software task time by up to 70 per cent, report finds

The adoption of Generative Artificial Intelligence (AI) tools in software development can significantly boost productivity and reduce task completion time, according to a report by Ness, a digital engineering services company, and Zinnov, a global management consulting firm.

The report highlighted that Generative AI tools such as Copilot and CodeWhisperer have the potential to transform software engineering productivity, particularly in routine development tasks.

It stated: “Generative AI (GAI) has a significant impact on repeatable sustenance activities and reducing knowledge barriers… 70% reduction in task completion time for existing code updates….. 48% reduction in task completion time for senior engineers.”

Ness and Zinnov conducted a detailed analysis of more than 100 software engineers across various use cases and development environments to assess the real-world impact of Generative AI in software development.

The study found that task completion time for existing code updates can be reduced by as much as 70 per cent when developers use Generative AI tools. This indicates that AI can be particularly effective in repetitive coding activities and maintenance work.

Generative AI tools also improve productivity among engineers with different levels of experience. Senior engineers experienced a 48 per cent reduction in task completion time when using these tools.

However, the impact of Generative AI varies depending on several factors, including the experience level of engineers, the complexity of the coding task, and the development environment.

In highly complex coding tasks, productivity gains from AI tools appear more limited. The study observed that high code complexity environments saw around a 10 per cent reduction in task completion time, suggesting that skilled engineers will continue to play a crucial role in complex software development.

The report further noted that the use of Generative AI can improve knowledge sharing and collaboration within development teams. Around 70 per cent of engineers reported improved engagement while working with these tools. Such tools can reduce knowledge barriers between teams and help developers work more effectively in distributed global teams.

Ness used its proprietary Matrix platform — a dynamic, data-driven engineering platform — to monitor key engineering performance indicators such as quality, productivity, responsiveness, and code quality during the study.

The report concluded that Generative AI has strong transformative potential in software engineering if used appropriately. Its overall impact will depend on factors such as engineer seniority, task type, and the complexity of the code involved.

Published on March 6, 2026