New technology in government rarely fails because it doesn’t work. It fails because the people who need to use it, or the public who depend on it, never come to trust it. Justin Fulcher, a technology founder and former Department of Defense advisor, has made that distinction central to how he talks about bringing artificial intelligence into federal agencies.

Two Audiences, Two Sets of Doubts

Fulcher has pointed out that AI deployment in government must earn confidence from both the workforce using the tools and the public the agency serves. Employees worry about job security, added complexity, or being blamed when an automated system makes a mistake. The public worries about privacy, fairness, and accountability when decisions that affect their lives run through software they cannot fully see into.

Justin Fulcher has argued that meeting both sets of concerns requires systems built to be auditable and explainable from the start, not bolted on after deployment. A tool that can show exactly how it reached a decision, and that fails in predictable, recoverable ways, gives both employees and citizens a reason to accept it rather than resist it.

Building Confidence Gradually

Rather than a single rollout, Fulcher’s approach favors gradual expansion, starting with lower stakes tasks like scheduling or document processing before moving toward anything that touches benefits decisions or enforcement actions. Early wins in low stakes areas can demonstrate reliability without exposing agencies to major consequences if something goes wrong.

Justin Fulcher’s advisory work at the Pentagon, focused on acquisition reform and technology modernization, reinforced this incremental view of adoption. Large institutions rarely change their minds about a new tool based on a single success story; they need repeated evidence over time. For agencies hoping to expand AI use beyond pilot programs, his framing suggests that patience and transparency will do more to build lasting trust than any single feature or capability the technology itself provides. See related link for more information.

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