Redefining software engineering: The impact of generative AI

Software plays a vital role in modern business, whether it’s integrated into enterprise apps or products.

Despite its ubiquity, the ultimate challenge for software engineering organizations has always been to release quality software fast enough to keep up with constantly accelerating demand.

Over the years, productivity and quality standards have progressively enhanced through the adoption of new methods and technologies. Agile and continuous product-centric approaches, automation, open-source ecosystems, cloud native and composable software architectures, DevOps, and low code / no code development have all been introduced. And each has built on the advances of past iterations.

But demand for quicker time-to-value and better cost efficiency is only accelerating. This leaves software organizations struggling to deliver at the expected pace, with the expected quality, while controlling technical debt and costs. Industry focus has been intent on identifying the next significant stepchange.

In this paper, we explore generative AI’s potential across the software development life cycle, analyzing its influence on software development teams and delineating effective strategies for driving a transformative shift in software engineering.