Government agencies are not failing for lack of resources or will. That is the central argument Justin Fulcher has made in a recent piece published by IT Security Guru, and it is a point worth taking seriously. The problem, he contends, is institutional drag: the compounding weight of outdated processes, siloed data systems, and compliance frameworks that were designed for analog workflows and have never been updated to match the digital present.

Friction vs. Replacement

Justin Fulcher, a technology founder who later served as a Senior Advisor to the Secretary of Defense, is careful to distinguish between what AI can realistically do and what it cannot. His argument is not that AI should replace human judgment in public institutions. Rather, AI’s most practical contribution is eliminating the operational friction that keeps government agencies from moving at the speed their missions actually require.

That distinction matters. Policy debates around AI in government often default to either techno-optimism or alarm about automation eliminating jobs. Fulcher sidesteps both extremes. His position is grounded in what he observed firsthand while working on acquisition reform at the Department of Defense, where he helped cut software procurement timelines from years to months. The lesson he draws: technology adoption succeeds in regulated environments not when it adds new layers of complexity, but when it strips away old ones.

A Record That Backs the Argument

Fulcher co-founded RingMD, a telemedicine platform that operated across Asia, before entering government advisory work. That background gives his arguments a practical edge. He has written that “our core systems operate as if it were 1975,” a characterization that captures the frustration many government technology reformers share.

His emphasis on durability over speed is also notable. Implementation discipline, clear objectives, and a willingness to iterate based on user feedback are what separate AI tools that actually improve government operations from those that simply introduce new problems. For agencies exploring AI adoption, his framework offers a measured, experience-tested starting point. Refer to this article, for related information.

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