Government agencies have no shortage of processes that consume time without adding value. Filing, sorting, routing, verifying, logging, and reporting occupy significant portions of the working day for federal employees at every level. Technology entrepreneur and former Defense Department advisor Justin Fulcher has argued that this is where artificial intelligence should enter the picture: not as a headline-grabbing transformation, but as a practical tool for reducing the work that slows institutions down.

The Right Frame for AI in Government

Fulcher draws a meaningful distinction between AI as a transformative platform and AI as a workflow tool. The former carries high expectations and tends to generate resistance, especially in public-sector environments where workforce concerns and accountability requirements are ever present. The latter offers a more realistic entry point. When AI handles document processing, data synthesis, routine compliance checks, and scheduling, it creates capacity without creating controversy.

Justin Fulcher has argued that this more modest framing is actually the more ambitious one in practice. The aggregate efficiency gains from improving a hundred routine processes across a large agency dwarf the impact of any single dramatic technology deployment. And those gains are more durable because they are embedded in workflows rather than standing apart from them.

The challenge facing government modernization, Justin Fulcher has written, is not a lack of ambition or funding. It is institutional drag: the compounding inefficiency that builds up when outdated processes, siloed data systems, and analog-era compliance requirements are left in place while the workload grows. AI does not need to be a revolution to address that problem. It needs to reduce friction.

Implementation Realities

Justin Fulcher’s views on AI in government are shaped by direct experience with the constraints that make public-sector technology deployment genuinely hard. At the Department of Defense, he worked on acquisition reform and IT modernization, contributing to efforts that cut software procurement timelines from years to months. Before that, at RingMD, he built a telemedicine platform across highly regulated healthcare markets in Asia.

In both environments, the principle was consistent. Technology adoption in regulated settings succeeds when the technology reduces existing friction rather than creating new complexity. AI tools that require agencies to redesign workflows around the technology, rather than fitting cleanly into existing operations, will struggle to gain traction. Tools that make current processes faster and more accurate will earn adoption and spread.

Fulcher has noted that successful AI deployment in government also requires a longer time horizon than private-sector implementations. Systems must be auditable and explainable. They must integrate with legacy infrastructure. They must earn trust from the workforce and the public. “Critical work is defined less by certainty at the outset than by stewardship over time,” he has said. That standard, consistently applied, is what distinguishes lasting modernization from a series of underperforming pilots. See related link for additional information.

Visit his page on https://www.instagram.com/justinfulcher/?hl=en, to learn more.