The rapid advancement of generative AI is transforming the software development landscape. A recent global survey and expert insights reveal how this technology is already impacting the software development lifecycle (SDLC) and what the future may hold. While the promises of generative AI, such as self-writing code and fully automated testing, are enticing, realizing these benefits will be a gradual process.
Carolina Dolan Chandler, chief digital officer at Globant, compares the current AI revolution to the early days of digital transformation, emphasizing that it will be a long-term process affecting every job role. The survey of over 300 business leaders found that while 94% are using generative AI in software development, only 12% say it has fundamentally changed their development process. However, 38% believe generative AI will substantially change the SDLC across most organizations within the next three years.
Generative AI is not limited to code generation. 82% of respondents use it in at least two phases of the SDLC, with common use cases including feature design and prototyping, requirement development, testing, and bug detection. Despite room for deeper integration, 46% say generative AI is meeting expectations, while 33% report it exceeds expectations.
Looking ahead, 49% of leaders believe advanced AI tools, such as assistants and agents, will lead to efficiency gains or cost savings, while 20% anticipate improved throughput or faster time to market. As the AI landscape evolves, Gartner analysts warn that over 80% of software engineers must acquire new skills to maintain their jobs.
Generative AI transforming software engineering
Philip Walsh, a senior principal analyst at Gartner, stresses that while AI will transform the role of software engineers, human expertise and creativity will remain essential. Gartner outlines three stages of AI’s impact on software development. In the short term, AI tools will enhance productivity within set boundaries.
In the mid-term, AI agents will automate tasks, with most code being AI-generated. In the long term, AI engineering will become more efficient, driving demand for skilled software engineers. The growing recognition of AI/ML engineering roles is evident, with 56% of software engineers surveyed by Gartner believing it to be the most in-demand role.
However, many lack the necessary skills to integrate applications with AI/ML. As AI-generated code gains traction, the effectiveness of these tools remains debated. Studies have shown mixed results, with some indicating a decline in code quality and no significant productivity gains.
To stay relevant in this evolving landscape, software engineers must continuously upskill and adapt to meet industry demands. Organizations must also invest in AI developer platforms and upskill their data engineering and platform engineering teams to drive continuous integration and development for AI artifacts.
