The conversation around AI in government has shifted quickly. What used to be a question of whether agencies should adopt it has turned into a race to deploy it.

But the way that conversation is happening is still off. Most of the focus is on models, vendors, training data and capability benchmarks, while the harder and more consequential questions are getting less attention: how data moves through these systems, how outputs are formed, and whether anyone can actually trace or validate them.

Agencies are accelerating deployment while still working through basic governance questions. That gap is where risk accumulates.

I’ve seen versions of this before. During my time at the FBI, information would exist somewhere in the various systems, but it wasn’t accessible when it mattered. As AI is layered into government workflows, the risk isn’t that data will disappear; it’s what accountability does, at a much larger scale.

Read more in FedScoop: https://fedscoop.com/agentic-ai-in-federal-government-agencies/