A survey of 900 engineers, platform leaders and technical managers published today finds nearly two thirds (63%) report their organization is shipping code faster since adopting artificial intelligence (AI).
Conducted by Coleman Parkes on behalf of Harness, a provider of a DevOps platform, the survey also suggests, however, that same code is creating more downstream DevOps issues, with 72% of respondents noting their organization has already suffered at least one production incident caused by AI-generated code.
In total, 45% of respondents said that deployments involving AI-generated code have been problematic. More challenging still, only 6% of respondents said their continuous delivery process is fully automated. A full 83% say AI must extend across the entire software delivery lifecycle to unlock its full potential.
Additionally, 63% said vibe coding using AI agents is “a disaster waiting to happen” in the sense that DevOps engineers will be overwhelmed by downstream rework, including reviewing code that may often be fundamentally flawed because it was created using a general-purpose large language model (LLM). Nearly three quarters (73%) are concerned unmanaged AI assistants could significantly widen the blast radius of failed releases.
Nearly half (48%) of respondents also expect AI to increase software vulnerabilities and only 41% are confident their existing governance tools and platform will reliably catch issues before release. Nearly three quarters (73%) also said they believe organizations that fail to integrate AI safely and securely across their software development lifecycle (SDLC) in the next year will “go the same way as the dinosaurs.”
Finally, most survey respondents (70%) are also concerned inefficient AI-generated code will drive uncontrollable cost overruns once it is deployed in a production environment.
Trevor Stuart, senior vice president and a general manager for Harness, said the survey makes it clear that much of the code being created by AI tools is being sent back to the proverbial kitchen to make it palatable enough to deploy. At the same time, many DevOps teams now feel like AI has created a six-lane highway for application developers that leads to a two-lane bridge operated by a DevOps team, he added.
That bottleneck will inevitably require DevOps teams to modernize their pipelines and workflows to make them efficient enough to manage higher levels of throughput, noted Stuart. However, the goal is to not necessarily add more DevOps pipelines so much as it is to enable DevOps teams to leverage AI to more efficiently manage the same or even fewer number of reusable pipelines, he added.
Achieving that goal will require those DevOps teams to not only better manage their pipelines, but in many cases rationalize their existing tools, said Stuart. On average, the survey finds development and engineering teams are already using eight to 10 distinct AI tools, while 36% of respondents juggle even more. A full 71% say context switching between those AI tools actually drains productivity.
It’s not clear how long it might be before DevOps teams are able to take greater advantage of AI to manage the current onslaught of code moving through their pipeline, but the one thing that should be clear to everyone by now is that writing more code does not directly equate to deploying more applications faster.
