The conversation about AI in government tends to focus on what new things technology can do. Justin Fulcher flips that frame. Writing for IT Security Guru, the technology founder and former Defense Department advisor argues that the most valuable application of AI in public-sector modernization is what it can take away: the friction that prevents government institutions from doing the work they already know how to do.

That is not a minor rhetorical distinction. It has direct implications for how agencies should evaluate AI products, structure procurement, and measure success. Tools that promise transformation but require agencies to rebuild workflows from scratch will hit the same institutional resistance that has slowed technology adoption in government for decades. Tools that quietly eliminate a bottleneck, or that automate a compliance step that currently requires three people and two weeks, will actually get used.

Lessons From Defense Procurement

Fulcher’s background gives him credibility on this point. He co-founded RingMD, operating a telemedicine platform across multiple Asian markets, before moving into federal advisory roles. At the Department of Defense, he focused on acquisition reform, contributing to initiatives that cut software procurement timelines from years to months.

The core principle he took from that work: technology adoption in regulated environments works best when it reduces existing friction rather than creating new complexity. That principle applies directly to AI. A system that generates compliance concerns, demands extensive retraining, or introduces new failure points is unlikely to succeed in an environment where institutional drag is already a persistent obstacle.

What Success Actually Looks Like

Justin Fulcher points to implementation discipline as the differentiating factor between AI programs that stick and those that stall. Clear objectives, realistic timelines, and consistent iteration based on actual user feedback are the hallmarks of government technology efforts that endure. He has described durable public service work as defined by stewardship over time rather than confidence at the start, a standard that applies just as well to AI deployment as to any other institutional undertaking. Read this article for additional information.

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