Where exactly are we on this transformative journey? How are enterprises navigating this latest terrain—and what’s still ahead? To analyze how generative AI is impacting the SDLC, MIT Technology Review Insights surveyed greater than 300 business leaders about how they’re using the technology of their software and product lifecycles.
The findings reveal that generative AI has wealthy potential to revolutionize software development, but that many enterprises are still within the early stages of realizing its full impact. While adoption is widespread and accelerating, there are significant untapped opportunities. This report explores the projected course of those advancements, in addition to how emerging innovations, including agentic AI, might bring about a few of the technology’s loftier guarantees.
Key findings include the next:
Substantial gains from generative AI within the SDLC still lie ahead. Only 12% of surveyed business leaders say that the technology has “fundamentally” modified how they develop software today. Future gains, nevertheless, are widely anticipated: Thirty-eight percent of respondents imagine generative AI will “substantially” change the SDLC across most organizations in a single to a few years, and one other 31% say this can occur in 4 to 10 years.
Use of generative AI within the SDLC is almost universal, but adoption will not be comprehensive. A full 94% of respondents say they’re using generative AI for software development in some capability. One-fifth (20%) describe generative AI as an “established, well-integrated part” of their SDLC, and one-third (33%) report it’s “widely used” in no less than a part of their SDLC. Nearly one-third (29%), nevertheless, are still “conducting small pilots” or adopting the technology on an individual-employee basis (somewhat than via a team-wide integration).
Generative AI will not be only for code generation. Writing software stands out as the most blatant use case, but most respondents (82%) report using generative AI in no less than two phases of the SDLC, and one-quarter (26%) say they’re using it across 4 or more. Essentially the most common additional use cases include designing and prototyping latest features, streamlining requirement development, fast-tracking testing, improving bug detection, and
boosting overall code quality.
Generative AI is already meeting or exceeding expectations within the SDLC. Even with this room to grow in how fully they integrate generative AI into their software development workflows, 46% of survey respondents say generative AI is already meeting expectations, and 33% say it “exceeds” or “greatly exceeds” expectations.
AI agents represent the subsequent frontier. Seeking to the longer term, almost half (49%) of leaders imagine advanced AI tools, similar to assistants and agents, will result in efficiency gains or cost savings. One other 20% imagine such tools will result in improved throughput or faster time to market.
