Deploying artificial intelligence in the private sector is difficult enough. In government, the complexity multiplies: stricter data security requirements, civil service protections, public accountability standards, and procurement rules that were written long before machine learning existed. Justin Fulcher, a technology entrepreneur who went on to serve as a Senior Advisor to the Secretary of Defense, has spent time on both sides of this divide.
What Regulated Environments Demand
Fulcher’s experience building RingMD, a telemedicine company that operated across Asia, gave him early exposure to the demands of technology deployment in regulated healthcare settings. Users were patients and clinicians. Data handling required strict compliance. The margin for operational error was narrow. That experience shaped his view that technology succeeds in constrained environments when it is designed for those constraints from the start, not adapted to them after the fact.
The same logic applies in government. Federal agencies operate under obligations that private-sector organizations do not. AI systems must be explainable to oversight bodies. They must integrate with legacy infrastructure that cannot simply be replaced. They must handle sensitive data under strict access controls. And they must earn the trust of a workforce that has seen technology initiatives come and go without delivering lasting change.
Justin Fulcher has argued that institutional drag, the compounding inefficiency created by outdated systems, siloed data, and analog-era workflows, is the core problem AI can meaningfully address. Tools that automate document handling, compliance verification, routine correspondence, and data aggregation absorb manual burden without requiring organizational restructuring.
From Procurement Reform to AI Strategy
Justin Fulcher’s tenure at the Department of Defense produced concrete results in this area. Working on acquisition reform and technology modernization, his team helped reduce software procurement timelines from years to months. That improvement came not from deploying new technology wholesale but from identifying specific process failures and addressing them directly.
He has written that serious institutional work is defined by stewardship over time. For AI in government, this means prioritizing tools that can be maintained, audited, and improved iteratively rather than systems that promise dramatic transformation but prove brittle under sustained operational pressure.
The agencies most likely to benefit from AI are those that approach adoption as an operational discipline, not a procurement event. Refer to this article to learn more.
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