High model accuracy is not the same as trustworthy AI, and the gap between the two is where most enterprise deployments fall apart. In this ODBMS Q&A, Biplab Banerjee breaks down why AI trust is ultimately an architectural problem, not a model problem. Drawing on real-world experience with telecom operators at scale, he explains the critical difference between an AI recommendation and a system decision, what happens when those two things get tightly coupled without a deterministic decisioning layer in between, and what the infrastructure actually needs to look like when AI outputs must drive operational actions in milliseconds across millions of events.
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