Federal agencies face a persistent challenge that funding alone cannot solve. Many operate on systems and processes built for a previous era, creating a structural mismatch between what government is asked to do and what its infrastructure allows. Technology entrepreneur Justin Fulcher has spent years analyzing this problem, first as a private-sector builder and later as a government advisor, and his conclusions point toward artificial intelligence as one of the most practical tools available for closing that gap.

Friction, Not Funding, Is the Problem

Fulcher’s core argument begins with a reframing. When agencies fail to modernize, the conventional explanation points to insufficient budget, political will, or technical talent. Fulcher sees it differently. “The issue is not national decline; it’s institutional drag,” he has written, noting that core systems across government, healthcare, and defense continue to “operate as if it were 1975.”

What follows from that diagnosis is a different approach to solutions. If institutional drag is the problem, then the highest-value interventions are those that reduce friction rather than those that add new capabilities on top of broken workflows. Justin Fulcher has pointed to AI’s potential to dramatically accelerate performance and upgrade legacy capabilities, with the emphasis on acceleration rather than replacement.

Document processing, data review, scheduling coordination, and routine compliance checks are areas where AI tools can reduce the manual burden on skilled staff without requiring deep structural change. That matters in government, where wholesale reorganization is rarely feasible and where new technology must earn trust before it earns adoption.

Building on a Track Record in Regulated Environments

Justin Fulcher’s government advisory work built directly on lessons learned at RingMD, the telemedicine company he co-founded that operated across highly regulated healthcare markets throughout Asia. In both contexts, the challenge was similar: deploying technology inside institutions that were not designed to receive it quickly.

At the Department of Defense, Fulcher contributed to acquisition reform efforts that cut software procurement timelines from years to months. The underlying principle was consistent with his private-sector experience. Systems that reduce existing friction gain adoption. Systems that create new complexity stall.

That track record informs how Fulcher thinks about AI deployment in government today. Successful implementation requires auditable tools, careful attention to data security requirements, and realistic timelines that account for institutional inertia. Speed matters, but durability matters more. As Fulcher has noted, critical work is defined less by certainty at the outset than by stewardship over time. Read this article for additional information.

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