AI coding success brings new challenges

Some 84% of developers now use or plan to use AI tools, according to a recent survey of more than 49,000 developers. This widespread adoption of AI coding tools has led to a significant shift in the way software is developed, with 93% of developers using AI to aid in their work.

Despite the high adoption rate, productivity gains have plateaued at about 10%, according to a 2026 study from developer intelligence platform DX Research. This has left some CIOs wondering if the investment in AI coding tools has been worth it.

Kai Chuang, CIO of Circles, has seen this shift firsthand, as his developers’ work has shifted from hands-on coding toward design and systems architecture. Developers at the workplace hospitality services provider spend less time on literal programming, and more time specifying what to build and testing whether it works.

This change has been rapid, with no top-down mandate, Chuang said. Once developers began trusting the output, the changeover to nearly full AI code generation happened rapidly on its own.

A new division of labor is already the norm at UiPath, an enterprise automation software company, where well over the majority of production-deployed code is authored by coding agents already, said chief technology and product officer Raghu Malpani.

Developers are transforming from code writers to reviewers and system designers, defining intent, validating outputs, and shipping more code, faster. When coding is no longer the slow step, the bottleneck moves upstream to design, which AI is reshaping as well.

This puts new demands on business analysts and product managers to have concepts shovel-ready, Circles’ Chuang said. Using AI to explore use cases and mock up interfaces before involving developers lets them deliver a much better, more refined design.

If productivity looks flat, CIOs should first assess whether they’re measuring the wrong things. Consider what Cornerstone Research, an economic and financial consulting firm, found in its own data.

Related: Security DVR Cameras Explained

Across more than a million billable time records, the answer so far is essentially no change, said chief technology and innovation officer Phil Leslie. However, this conclusion, while accurate, is also misleading.

AI use has not measurably reduced the analyst share of hours, Leslie said. But what it has done is shift the mix: analysts report less time on coding and debugging, and more on interpretation, methodology and thinking. The job feels different, even though the hours have not moved.

Some organizations are reporting substantially larger productivity gains from AI-assisted coding. Even there, however, technology executives argue that productivity gains are not the most important change.

At Bank of America, for example, the AI-powered coding assistance used by more than 18,000 developers is generating efficiency gains. But raw speed isn’t the point, said Hari Gopalkrishnan, the bank’s chief technology and information officer.

The need for talented people who can solve complex problems, exercise judgment and build relationships will remain critical, Gopalkrishnan said. As AI-generated code proliferates, oversight shifts from the margins to the core of the job.

Erik Brown, a senior partner at management and technology consulting firm West Monroe, explained that the scarce skill is no longer writing code, but knowing what should be built, how it should be architected, whether it’s secure, and whether it actually advances the business outcome.

The companies that get this right will redesign the software development lifecycle around AI, he said.