Operations | Monitoring | ITSM | DevOps | Cloud

Why strong reviews, accountability, and monitoring matter more in an AI-assisted world Artificial intelligence has become the latest fault line in software development. For some teams, it’s an obvious productivity multiplier. For others, it’s viewed with suspicion. A source of low-quality code, unreviewable pull requests, and latent production risk. One concern we hear frequently goes something like this: It’s an understandable fear; and also the wrong conclusion.

There’s a moment every engineer knows. An AI suggests a fix, it looks reasonable,maybe even obvious, but production is on the line and you hesitate before clicking execute. There’s a big difference between an AI that can recommend an action and one you’re willing to let take that action. All it takes is one bad call, one kubectl command that makes things worse, and suddenly every automated suggestion is a potential liability instead of a help.

Generic AI fails in network operations because it lacks the “institutional knowledge” of your specific environment and business priorities. Learn how Kentik’s Custom Network Context encodes your unique operational reality into AI Advisor, turning a generic chatbot into a context-aware teammate.

Automation revealed truth. AI extends it. Machines don’t invent knowledge. They reason from verified data. The result is operational foresight that’s traceable, explainable, and trusted.

We are witnessing a fundamental transformation in how software is built. The industry has moved beyond the experimental phase of Machine Learning Operations and entered a complex new reality: the era of the AI Software Supply Chain. The adoption metrics confirm this shift is irreversible. Google reports that 90% of tech workers are now using AI as part of their daily work. Similarly, McKinsey data reveals that 88% of organizations use AI in at least one business function.