The infrastructure debt AI creates isn't in the code. It's in the operations
Enterprise AI success depends on operational discipline
by https://www.techradar.com/uk/author/lior-koriat · TechRadarOpinion By Lior Koriat Published 3 August 2026
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Enterprise DevOps teams have adopted AI faster than they have adapted the operational systems that support it. Large language models generate Infrastructure as Code, deployment pipelines, and configuration files in seconds.
Development teams are delivering infrastructure changes at a pace that would have been implausible five years ago. Production environments have simultaneously become harder to reproduce, more difficult to govern, and more expensive to operate.
Lior Koriat
CEO at Quali.
The problem is easy to miss at first, because AI looks like a clear win in the early stages. A team asks an AI tool to set up some infrastructure. It works. The team expands how much they rely on it. More teams follow. More infrastructure gets created for testing, demos, experiments, and development. The organization ends up with far more moving parts than it had before, but far less ability to track them.
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Not the full picture
The tools generating this infrastructure are good at describing what something should look like at the moment it's created. They're not built to watch what happens to it afterward, decide whether it's safe to make another change while one is already in progress, or notice when something has quietly drifted out of its intended state over the following weeks.
That kind of oversight used to come from experienced operations people making changes one at a time, in a predictable order, with a clear sense of what else was happening around them. AI removed the slow part of that process without replacing the judgment that made it manageable.
Now there are multiple people and multiple AI tools all making changes to the same systems around the same time, each one doing its job correctly in isolation, none of them able to see the full picture of what's happening across the environment as a whole.
The result looks familiar to anyone running infrastructure today. Environments that worked perfectly last week suddenly don't, and nobody can say exactly why. Cloud costs creep up steadily because nobody remembered to shut something down once it was no longer needed. Teams spend real, billable hours figuring out why two environments that were supposed to be identical have quietly diverged.
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