Justin Fulcher on What Government Agencies Get Wrong About AI

There is no shortage of ambition when federal agencies talk about artificial intelligence. Pilot programs launch, vendors make presentations, and leadership teams endorse modernization roadmaps. What happens next is often less encouraging. Justin Fulcher, who has advised on technology modernization from inside the Department of Defense, has a clear view of why these efforts frequently fall short, and what a more durable approach looks like.

Speed vs. Stewardship

Fulcher’s central critique of how government approaches technology adoption is not that agencies move too slowly. In many cases, they move too quickly toward tools that look impressive without adequately preparing the institutional environment to absorb them. AI systems that require clean, well-structured data fail when agencies still run siloed databases-built decades apart. Automation tools that need streamlined workflows struggle inside compliance environments designed for manual approval chains.

What agencies often overlook, Fulcher argues, is the importance of stewardship. In his writing, he has noted that serious work is defined less by certainty at the outset than by stewardship over time. Applied to AI deployment, this means the success of a government technology project is measured not at launch but over years of operational use.

Systems must be auditable, maintainable, and explainable to both the workers who use them and the oversight bodies that hold agencies accountable. Those requirements rule out many AI tools that perform well in commercial settings but were not designed for public-sector operating conditions.

Practical Principles From Justin Fulcher

Justin Fulcher’s own background in government technology underscores these points. During his time as a Senior Advisor to the Secretary of Defense, Justin Fulcher contributed to acquisition reforms that shortened software procurement cycles from years to months. That result did not come from deploying cutting-edge tools without preparation. It came from systematic analysis of where the process broke down and targeted interventions at those specific points.

His earlier work building RingMD, a telehealth platform across Asian markets, gave him parallel experience with high-stakes technology deployment in regulated environments. The lesson in both cases was the same: integration discipline matters more than ambition.

For agencies considering AI investments, Fulcher’s framework suggests starting with workflow analysis. Find the bottlenecks where manual labor accumulates, assess whether AI can absorb that load cleanly, and measure results against realistic baselines before scaling. Refer to this article for related information.

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There is no shortage of ambition when federal agencies talk about artificial intelligence. Pilot programs launch, vendors make presentations, and leadership teams endorse modernization roadmaps. What happens next is often less encouraging. Justin Fulcher, who has advised on technology modernization from inside the Department of Defense, has a clear view of why these efforts frequently…