Why siloed AI fails in government

I keep seeing the same pattern. A few teams spin up separate AI tools to fix local problems. Each tool helps a little, but nothing changes in the day to day. Data sits in different places, security reviews multiply, and leaders cannot see one picture of value. After a year, people say AI never delivered.

The issue is not the tech. It is the way we introduce it. When every office buys its own tool, you get duplicate work, conflicting answers, and five different vendors to manage. It feels fast at first and then slows everything down.

What has worked is boring but effective. Pick one or two workflows that touch multiple groups. Put AI inside the systems people already use. Give it shared identity, logging, and cost controls. Measure cycle time, accuracy, and rework. When those numbers move, expand to the next workflow.

I am not against pilots. I am against pilots that cannot graduate. If the data cannot be shared and the controls are all different, the pilot was set up to stall.

Curious what others here have seen. Where did AI actually stick in production, and what made the difference? If you tried the scattered approach, what would you do differently next time?