IT Leaders Explore New Avenues in AI and Advanced Tech use in Software Development

Hyderabad: IT leaders are explore new avenues in AI and advanced technology use in Software Development Life Cycle, according to a OutSystems and KPMG survey titled “AI in software development: Exploring opportunities and uncertainties,” launched on Wednesday.

The research surveyed 555 software executives around the world whose companies span IT consultancy services, manufacturing, banking, financial services and insurance among others. As many as 84 per cent of respondents reported that their organisations first began to incorporate AI technologies in their SDLCs between six months and five years ago, with the earliest adopters primarily being IT services companies. Across regions, EMEA and North America remain roughly on equal footing, while APAC is steadily catching up.

The findings show that testing, quality assurance, and security vulnerability detection are by far the most widely adopted use cases for AI in software development. Nonetheless, generative AI (GenAI) is set to transform the industry by significantly enhancing these processes and introducing unprecedented capabilities.

A total of 75 per cent of software executives have seen up to a 50 per cent reduction in development time by implementing AI and automation.

“AI is redefining the impossible,” said Paulo Rosado, CEO and founder at OutSystems. “I’m laser-focused on helping teams compress multi-year legacy modernisation projects into just a few months. The latest AI disruptions have brought us the potential to compress these development timelines into even shorter and faster projects. With AI, historically impossible transformation projects are not only possible but easier, cheaper, and faster to accomplish.” he added.

But confidence is also paired with risk awareness surrounding tech debt in the form of orphan code and hallucinations, a lack of context for an organisation’s specific coding needs, and scalability concerns. With strategy baked into AI in SDLC processes, about 56per cent of respondents said they experienced or expected to experience a higher quality of applications, with fewer bugs and improved performance.

The AI opportunity is undeniably huge, but its wider adoption in other areas of the SDLC beyond software testing and vulnerability detection still face some barriers. Chief among these are data privacy and security concerns (56 per cent of respondents) and regulatory and compliance challenges (42 per cent). Moreover, about 38per cent of executives cite difficulties integrating generative AI into existing workflows as the primary barrier to adoption.

“There’s a lot of speculation on what will change with the rise of GenAI,” said Michael Harper, Managing Director at KPMG US. “While there will be challenges, those with effective change management initiatives will reskill and upskill their workforces, leading to AI and jobs evolving in tandem,” he added.

The speed and sprawl of AI, namely GenAI, is paving the way for an increase in investments for nearly all respondents. But risks concerning the reliability of AI-generated code persist, though they can be mitigated with existing approaches, such as user acceptance testing, unit testing, and regression testing. “It’s up to the developer working with AI to guarantee the quality of the deliverables, but this becomes way more efficient with AI,” said Coutinho and added, “AI is, in fact, a great partner in creating tests in synthetic data.”

Other oft-cited concerns were the limited availability of skilled personnel and difficulties integrating GenAI into existing tech stacks and workflows. Fears of job losses are high as well, with 89 per cent of respondents claiming that certain roles will be eliminated by AI.