AI and devops – a natural fit
In many ways, AI and devops seem made for each other. Any automation that teams can add to the software development process is a plus.
“At this point, most of the enterprise teams I work with have moved well beyond experimenting and AI is part of the daily workflow,” says Jackie Swanson, managing partner at research firm Gartner. “The on-ramp for most has been AI-assisted coding. Tools like GitHub Copilot and Amazon Q Developer are showing up everywhere, helping developers knock out boilerplate, write unit tests faster, and scaffold infrastructure-as-code.”
But the more interesting shift is happening further down the pipeline, Swanson says. “Teams are leaning into AIOps platforms for smarter monitoring, anomaly detection, and incident triage that used to eat up hours of an engineer’s week,” she says. “The real story right now is the move from adopting individual AI point solutions to thinking about AI as a layer across the entire delivery chain.”
Teams using AI-assisted coding and automated test generation are compressing cycle times by 20% to 40%, Swanson says. They are also resolving incidents more efficiently, with AI platforms correlating alerts, flagging probable root causes, and suggesting fixes. This means “on-call engineers aren’t spending their nights sifting through dashboards,” Swanson says. “Mean time to resolution drops and so does burnout.”
