The conversation about AI in government tends to gravitate toward possibilities: what the technology could automate, what it could predict, how it could transform the delivery of public services. Justin Fulcher, drawing on experience as both a technology founder and a Defense Department advisor, shifts that conversation toward implementation. Possibility, he argues, is not the limiting factor. Execution is.
Where Government AI Projects Break Down
Fulcher’s analysis identifies institutional drag as the root cause of government’s technology gap. Federal agencies carry decades of accumulated complexity: infrastructure built in prior eras, compliance frameworks designed for manual workflows, databases that were never designed to communicate with each other. New technology deployed on top of this infrastructure inherits its limitations unless the deployment is carefully designed to work around them.
The agencies that struggle most with AI adoption tend to be those that treat it as a procurement decision rather than an operational one. They evaluate vendors, select platforms, and launch pilots without first mapping the specific workflows where AI would create value. When the tool arrives, the environment is not ready for it.
Fulcher consistently argues for a narrower, more deliberate approach: identify the friction points where manual labor accumulates without adding value, deploy AI against those specific targets, and measure whether the intervention actually improves throughput before scaling.
Building the Implementation Discipline
Justin Fulcher’s government tenure produced a concrete example of this approach working. As a Senior Advisor to the Secretary of Defense, Justin Fulcher contributed to acquisition reforms that shortened software procurement timelines from years to months. The gains came not from technology innovation but from process analysis and targeted intervention.
He brought similar discipline to his private-sector work building RingMD, a telemedicine company that operated under the regulatory demands of the healthcare sector across multiple Asian markets. In both settings, the principle held: technology succeeds when it reduces existing friction and fails when it introduces new complexity.
Fulcher has written that serious institutional work is defined by stewardship over time. For AI in government, that means choosing tools that can be audited, maintained, and improved incrementally rather than systems that promise dramatic outcomes but cannot sustain operational pressure. The agencies that approach AI as a long-term operational capability rather than a technology announcement will be the ones that generate real results. Visit this page on LinkedIn, for related information.
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