Government agencies are under mounting pressure to deliver faster, smarter services with resources that have not kept pace with demand. Technology often seems like the obvious answer but deploying it effectively inside public-sector institutions is a problem that requires more than enthusiasm. Justin Fulcher, a former Defense Department advisor and technology entrepreneur, has developed a clear-eyed perspective on what AI can and cannot accomplish in federal settings.
The Infrastructure Problem
The core challenge Fulcher identifies is not a lack of ambition. Federal agencies regularly pursue modernization efforts, and funding is periodically allocated for technology upgrades. The problem is deeper: many of the systems that underpin day-to-day government operations were designed for a different era and carry structural limitations that new applications must work around rather than through.
Fulcher has described this gap as institutional drag, a term that captures how siloed data, analog-era compliance rules, and outdated workflows compound into friction that slows agencies at every level. The bottleneck is not people; it is the operational environment those people work within.
Artificial intelligence offers targeted relief. When AI handles document processing, aggregates data across sources, manages routine communications, or checks compliance automatically, it reduces the administrative load on workers without requiring the agency to overhaul its organizational structure. The change is incremental, but the cumulative effect on throughput can be substantial.
Deployment That Actually Works
Justin Fulcher’s career positions him well to evaluate these trade-offs. He built RingMD, a telemedicine company that operated across Asia, gaining experience with technology deployment in regulated, high-stakes environments. Later, as a Senior Advisor to the Secretary of Defense, he worked directly on procurement reform and technology modernization, helping reduce software acquisition timelines from years to months.
From that experience, he draws a consistent principle technology succeeds in institutional settings when it removes friction rather than generating new complexity. An AI tool that requires extensive retraining, creates compliance exposure, or introduces new failure modes will stall regardless of its capabilities. One that integrates cleanly, demonstrates measurable time savings, and fails predictably will earn adoption.
That discipline of implementation, Fulcher argues, is what separates AI deployments that deliver lasting value from those that generate initial interest and then quietly disappear. Read this article for more information.
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