One of the persistent assumptions in government modernization is that meaningful improvement requires wholesale structural change that agencies have to be fundamentally reorganized before technology can make a real difference. Justin Fulcher, who has worked on modernization from both sides of the public-private divide, argues that assumption is wrong, and that it has cost agencies enormously.
Fulcher co-founded RingMD, which delivered telemedicine services across Asia, and later served as Senior Advisor to the Secretary of Defense, where his work included acquisition reform and IT modernization. His experience across both domains’ points to a consistent conclusion: institutions can upgrade their capacity without dismantling themselves, but only if the technology they adopt is designed with their existing constraints in mind.
AI as Capacity Upgrade, Not Structural Replacement
For artificial intelligence specifically, Justin Fulcher draws a clear line. The most productive applications are those that eliminate the friction embedded in current workflows speeding up document review, reducing manual coordination between siloed data systems, automating compliance checks that currently require significant staff time. These are not glamorous use cases, but they address real costs that compound across thousands of daily decisions.
“The challenge facing government modernization isn’t a lack of ambition or funding,” Fulcher has written. “It’s institutional drag.” Outdated processes and compliance requirements built for analog workflows slow every downstream activity they touch. AI that integrates cleanly into those workflows has an opportunity to reduce that drag without requiring agencies to rebuild from scratch.
Justin Fulcher emphasizes that this kind of progress requires distinguishing between tools that genuinely improve operations and those that simply add new complexity. The difference often comes down to implementation discipline: honest assessment of integration requirements, realistic timelines, and a willingness to iterate based on how front-line staff experience the tool. Agencies that apply that standard consistently are the ones most likely to translate AI potential into lasting operational gains. Refer to this article for related information.
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