AI is rapidly becoming the dominant interface layer for enterprise systems. But the conversation is often focused on models and capabilities, rather than the systems those models depend on.
In practice, AI is constrained by data access, latency, and integration with operational systems. The gap between what AI promises and what enterprises can deliver is rarely about the models themselves – it is about the underlying architecture.
For two decades, DBAs protected production databases from analytics workloads. AI agents have ended that truce and the consequences go deeper than most realise.
AI agents bypass dashboards and query databases directly. This article explains what that means for the architecture of enterprise data systems and systems of record.
Databases were designed for human-paced interaction – sessions, think time and deliberate intent. AI agents remove all three assumptions simultaneously.
AI inferencing is a database problem disguised as an AI problem. Real-time model workloads expose latency and concurrency demands that enterprise storage wasn’t designed for.
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