Technology founder and former government advisor Justin Fulcher has made a clear case for how artificial intelligence can serve public-sector modernization not by replacing human decision-makers, but by cutting through the procedural slowdowns that keep agencies from operating efficiently. His argument centers on a concept he calls institutional drag, the accumulated weight of outdated processes and incompatible systems that quietly undermines government performance.

The Core Diagnosis

Fulcher has been direct about where the problem lies. Writing on institutional renewal, he observed that the core challenge is not national decline but institutional drag. “Across government, healthcare, defense, and infrastructure, our core systems operate as if it were 1975,” he wrote. The implication is sharp: ambition and funding are not what is missing. What is missing is a willingness to address the structural inertia that slows agencies down.

His perspective is shaped by genuine experience on both sides of the public-private divide. Justin Fulcher co-founded RingMD, a telemedicine platform that scaled across Asia, before transitioning into government service as a Senior Advisor to the Secretary of Defense. In that role, he focused on acquisition reform and technology modernization, working on initiatives that compressed software procurement timelines from years to months.

What AI Can Realistically Do



The lesson he drew from that work is practical and transferable. Technology adoption in regulated environments tends to succeed when it removes friction rather than introduces it. AI tools that demand extensive retraining, raise compliance concerns, or bring new failure modes into bureaucratic systems will face resistance regardless of how capable they are. The tools that gain traction are those that slot into existing workflows and make them faster, cleaner, and less prone to bottlenecks.

This is the lens through which Justin Fulcher evaluates AI’s role in government: not as a transformative leap, but as a precision instrument for institutional improvement. Agencies exploring AI adoption would benefit from applying that same discipline, setting clear objectives, building in realistic timelines, and staying committed to iteration when early implementations fall short. Refer to this article for more information.

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