The velocity paradox: Why AI governance breaks at machine speed
By Anand Madhav, Vice president – Analytics & AI, Straive
An agentic AI system recently processed 400 insurance claims overnight. The committee responsible for overseeing it meets once a week, on Thursdays.
That mismatch reveals something more fundamental than a scheduling problem. It exposes a governance model built for decisions that moved at human speed. Most oversight frameworks were designed around quarterly reviews, weekly stand-ups and after-the-fact audits. That cadence worked when AI systems were recommendation engines: a model proposed something, a person reviewed it, and the workflow had natural pause points for judgment to enter.
Agentic AI changes that model by removing those pause points altogether. These systems execute across applications, sequence their own decisions and act on them before anyone has opened a dashboard. A content moderation pipeline can flag and remove posts in milliseconds. A supply chain agent can reroute inventory based on demand signals. Execution has...
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