Stop automating inefficiency and scale AI the right way
A commentary argues that government agencies risk wasting AI investments by deploying the technology on top of broken workflows, and outlines four operational conditions agencies should fix before scaling AI across their missions.
Why this matters: Automating a bad process gives you a faster bad process. That is the core problem this piece is pointing at. Agencies are under pressure to show AI results, which creates an incentive to deploy quickly rather than carefully. When AI gets built into government operations before the underlying workflows are sound, the mistakes do not stay small. They scale. The people on the receiving end of those decisions rarely get to see the system that produced them, let alone challenge it.
Who should care: General readers · AI governance · Policy
This summary is AI-assisted and may contain errors. It is an original briefing to help you gauge significance quickly — not a reproduction of the source. Always read the linked original before relying on it. See our methodology.