When government agencies debate adopting artificial intelligence, the conversation often tilts toward the transformative. Advocates promise wholesale reinvention; skeptics warn of upheaval. Justin Fulcher, who has worked on technology modernization from inside the Department of Defense, argues for a more grounded approach: focus AI deployment on the friction points that slow existing workflows and measure success by whether things actually get faster.

Understanding Institutional Drag

Fulcher’s analysis of government’s technology problem centers on what he calls institutional drag. Federal agencies operate under a combination of legacy infrastructure, rigid procurement rules, siloed databases, and compliance requirements built for analog-era workflows. These constraints create compounding delays that slow decision-making and frustrate both employees and the citizens they serve.

The systems are not broken in an obvious sense. They function. But they function at a pace that no longer matches the speed at which government must operate. AI, in Fulcher’s framing, offers a practical path to close that gap without requiring agencies to rebuild from scratch.

The use cases he points to are deliberate: document processing, data synthesis, routine correspondence, compliance verification, and scheduling. These are not glamorous applications, but they are where administrative burden accumulates. Automating them allows skilled personnel to redirect their energy toward work that actually requires human judgment.

Lessons From Defense Modernization

Justin Fulcher’s government experience is instructive here. As a Senior Advisor to the Secretary of Defense, Justin Fulcher contributed to acquisition reform efforts that cut software procurement timelines from years to months. That kind of improvement does not come from wholesale organizational change. It comes from identifying specific bottlenecks and systematically removing them.

He co-founded RingMD, a telemedicine company that scaled across Asian markets, before transitioning to public-sector advisory work. That background in regulated, high-stakes technology deployment shaped his view that successful AI adoption depends on integration discipline: clear objectives, clean system interfaces, and realistic implementation timelines.

Writing on public service, Fulcher has observed that enduring work is defined by stewardship over time rather than certainty at the outset. For government AI projects, that means building tools that agencies can maintain, audit, and improve rather than deploying systems that look impressive at launch but prove fragile under operational pressure. Read this article for additional information.

Find more information about Justin Fulcher on https://x.com/JustinFulcher