AI can write your deployment pipeline in seconds.
But it cannot explain, at 2am, why production just went down.
That's the part nobody talks about. AI hasn't removed humans from DevOps, it's removed the paper trail. When an engineer wrote a bad script by hand, there was a name on it. A code review. A Slack thread you could scroll back through.
When AI generates the pipeline, even a strong team can end up in a strange spot: everyone reviewed it, everyone trusted the process, and somehow the ownership of that one specific decision still slips through the cracks. Not because anyone dropped the ball. Because the tooling changed faster than the workflows built to assign responsibility did.
Here's a blunt way to think about it: if a deployment takes down production, you can't put an AI model on a performance improvement plan. You can't have that hard conversation with a language model. Accountability has to live somewhere with a pulse.
And that's really the risk with AI in infrastructure. It's not that the tools are unreliable. It's that they're fluent and unreliable, which is far more dangerous than a system flagging "I'm not totally sure about this one."
The best DevOps and DevSecOps engineers don't catch problems because they read every log line. They catch them because something feels off about a deploy, built from years of watching systems fail in strange, unglamorous ways. AI has no scar tissue. It's never been called frantically at 2am.
So no, AI hasn't made these roles smaller.
It's made the on-call engineer the last line of defence against decisions that technically got made, but that nobody actually made.
If your team is scaling fast and leaning harder on AI to move quicker, it might be worth asking: who actually owns the deploy when something goes wrong? If that answer isn't obvious, get in touch and let's talk about building that accountability back into your pipeline.
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Sanna Khaled
CEO
Whether you have a question, some feedback, or just want to explore what's possible, I'm always happy to chat.
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