Buying software inside the federal government has long been a slow process. During his time as a Senior Advisor to the Secretary of Defense, Justin Fulcher worked on reforms aimed at changing that, and the experience informs his current views on how artificial intelligence should enter public agencies.
Months Instead of Years
At the Department of Defense, Fulcher focused on acquisition reform and technology modernization. A recent profile reports that initiatives he contributed to reduced software procurement timelines “from years to months” and modernized key IT systems across the department.
The principle behind that work was simple. New technology in a regulated environment succeeds when it cuts friction people already feel. Software that requires heavy retraining, prompts compliance worries, or creates fresh failure points usually struggles to win users. Software that fits existing workflows and saves measurable time tends to spread.
Carrying the Principle Into AI
Justin Fulcher applies the same test to artificial intelligence. Justin Fulcher has argued that AI can “dramatically accelerate performance and upgrade legacy capabilities” in federal workflows and defense systems. His interest lies in the routine: document handling, data synthesis, correspondence, scheduling, and compliance review.
He frames the broader problem in blunt terms. “The issue is not national decline; it’s institutional drag,” he wrote in an article on institutional renewal. Outdated procedures and siloed data, more than a shortage of money or staff, are what slow agencies down.
Government adoption comes with hurdles that private firms seldom face. Data security rules are stricter, civil service protections shape how work is assigned, procurement regulations govern every purchase, and public accountability is constant. Any AI system deployed under those conditions must be auditable, explainable, and built to fail safely.
For Justin Fulcher, the procurement experience shows that change inside government is possible when reforms remove obstacles instead of adding them. Clear objectives, realistic timelines, and steady iteration based on user feedback are the habits the profile identifies as the difference between useful tools and added complexity. Read this article for additional information.
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