AI is speeding up coding, but what about everything else across the software development lifecycle? According to new research from Atlassian, developers still report mixed gains and growing frustration.
As part of its State of Developer Experience Report, 87% of respondents told Atlassian that AI helps them write code faster, but nearly three quarters still face the same – or even greater friction – across the development lifecycle as before.
Indeed, traditional organizational and workflow bottlenecks are still slowing down processes, and that's led to a reduction in time savings that can be attributed to AI.
Last year's version of the poll found that 68% of software developers saved at least ten hours every week thanks to AI. By this year, that had slipped to 25%.
Beyond that, half of developers polled said they spend at least four hours a week managing output from AI, with two thirds adding that they have to spend time fixing errors in work.
Atlassian's study is by no means the first to report mixed gains from AI in software development. Research from Harness, for example, showed that while the technology is speeding up coding, eight-in-ten developers now spend more time reviewing code and completing other "invisible work" tasks.
The AI coding conundrum
So what’s slowing down software development? The survey suggests half of developers are spending too much time finding information and attending meetings, with a further 45% pinning the blame on context switching.
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That tracks with separate research from Workday, which found one-in-four UK workers spend seven or more hours a week copying information between apps including AI tools.
"AI can help developers move quicker, but faster code is not the same as better software delivered sooner," said Andrew Boyagi, Customer CTO at Atlassian. "If teams still struggle to find information, clarify requirements and move work through the delivery process, speeding up coding alone won’t translate into business outcomes."
Atlassian said that AI has potential to address some of these challenges – such as by streamlining knowledge discovery and automate handoffs – but most uses of AI across the software development lifecycle remains focused on code generation rather than workflow optimization.
"Making one task faster doesn’t improve the whole system," said Boyagi.
Notably, the survey found that developers feel pressure to use AI, with four-in-ten using the technology more often than is strictly necessary because they feel it's expected of them.
That's perhaps no surprise given six-in-ten report that their employer sees AI usage as a measure of productivity – and that just 17% of devs and 23% of engineering leads say their own organization has a "clearly communicated and implemented AI strategy".
"Leaders need to be clear about the problems they want AI to solve and measure the outcomes for the whole team, not just how often the tools are used," added Boyagi.
When AI does manage to speed up software development successfully, it exacerbates another existing problem: burnout.
This has become a recurring issue for development teams since the advent of generative AI. As ITPro reported in March this year, research from Harness found that the increased velocity created by the technology is creating huge pressure.
Developers now face more after-hours work as a result of botched code and remediation tasks. The majority of those polled by Harness (96%) now report working evenings or weekend multiple times a month due to release-related work.