Software developers now face a delicate balancing act with AI. While the technology is speeding up production and improving productivity, unintended by-products such as flawed code mean many end up with heavier workloads.
Harness’ 2026 State of DevOps Modernization report found that 45% of developers who ‘frequently’ use AI coding tools (multiple times a day) deploy code faster than moderate daily or weekly users.
The company noted that this highlights the growing benefits of AI coding tools for developers, with these solutions helping to speed up production and software delivery. In this regard, AI is living up to the hype.
There are notable drawbacks, however. More than two-thirds (69%) of frequent users admitted their teams experience deployment problems more often when AI-generated code is involved.
Across all respondents, including frequent, moderate, and minimal users of AI, 58% agreed they have serious concerns about the risks associated with AI-generated code.
Speaking to ITPro, Harness CTO Martin Reynolds said these pitfalls highlight the growing complexity of software development with AI in the equation.
The technology may simplify tasks such as coding, for example, but across the broader software development lifecycle (SDLC), cracks begin to appear due to the velocity of operations.
Crucial processes such as quality assurance (QA) and security testing are being stretched to breaking point. Simply put, AI has given the engine a boost, but the frame that holds everything together isn’t up to the task.
“When you’ve got developers working at ‘human speed’, shall we say, all those processes that were built to make sure that everything stayed up was at human speed, now we’re developing at ‘machine speed’ and those other things are catching up,” he explained.
“I think what ultimately happens is some of those edge cases and bugs or impacts don't necessarily get caught, because everything to the right isn't keeping up.”
The impact of this increased velocity in software development can hinder rather than help developers. Many now contend with heightened workloads despite AI being framed as a boon for teams.
Nearly half (47%) of frequent AI users reported that tasks such as QA, remediation, and validation have become more problematic.
Reynolds told ITPro this highlights the importance of traditional DevOps practices and the need for a considered approach to integration of these tools within daily operations.
AI isn’t a silver bullet, and as with any technology adoption process, the foundations need to be laid well in advance and be scalable. Downstream activities within the SDLC aren’t able to cope with increased volume at present.
“I will always say for any AI tool, it is still a tool, and you still have to learn your craft of how to use that tool,” he said. “You have to learn how to use it and get the best out of it.”
“But I also think that the downstream stuff they're saying they're spending their time on, it wasn't necessarily built to scale,” Reynolds added. “The foundations that you put in place that would allow it to scale haven't changed from before, like before AI to after.
“Having the good solid repeatable paths to production, scalable testing, having those good foundations, organizations that already have those are the ones that are generally scaling better, because they were built for scale.”
Telltale signs of strain
A clear sign that the volume of code production is having an adverse effect on development processes lies in remediation times, Reynolds told ITPro.
Harness’ report specifically highlighted longer recovery times as a key issue for developers that frequently use AI. These teams said it takes them an average of 7.6 hours to restore or resolve production incidents compared to just 6.3 hours for occasional or limited users.
Familiarity is a factor here, Reynolds noted. Teams are facing larger volumes of code and don’t have a clear understanding of what they’re dealing with, or even looking for.
“The mean time to recovery (MTTR) is taking longer, and it's taking longer because there's more code that they're not familiar with,” he said. “So even finding that problem is part of what's driving that.”
“You’ve got more going in,” Reynolds added. “Which leads to things just slipping through the gaps.”
The human strain of machine-speed development
This confluence of issues hampering development teams isn’t just hitting operational efficiency, as Harness found it has a human impact.
Developers now face more after-hours work as a direct consequence, with 96% of frequent AI users reporting having to work evenings or weekends multiple times each month due to release-related work.
Overwork, burnout, and “crunch culture” have been long-running issues in the profession, with a slew of studies in recent years highlighting the strain developers face.
Indeed, research from Harness in mid-2024 warned that burnout had reached “epidemic proportions” – and that was before the sustained influx of AI tools over the last two years.
Manual toil was highlighted as a key factor in this trend. Two years on, and with an array of AI-powered tools at their disposal, developers are still facing similar challenges and pressure
Reynolds repeated that this underlines the need for robust foundational preparation on the part of developer teams and enterprises at large to ensure smooth adoption of AI tools.
“I come back to that foundational thing. All those signs that you were seeing two, three years ago, I remember that because three years ago I still had engineering teams that worked for me, and [they] were having this problem,” he said.
“Part of it is if you didn’t solve the underlying things that were causing people to have to work late, whether that’s manual releases or manual release verification, or problem solving – if you’re not solving those fundamental things and those fundamental bottlenecks in the way you deliver your software, all that's happened is AI has just amplified them.”
Reynolds added that while AI is a valuable tool to reduce workload strain, the technology has the potential to further exacerbate existing problems. Worse still, the well-documented advantages of AI mean higher demands are placed on teams.
“AI doesn't solve the burnout problem. If anything, it amplifies it,” he said.
“I would add, especially because there is genuine pressure that happens, because we know you can generate more code now, so we expect more code out the door.”
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