Builder first,
operator throughout.

Adam C. Higdon is an applied AI engineer and enterprise technology operator based in Chicago.

After more than a decade in enterprise software, he’s spent the past several years as a hands-on builder of production AI systems — generative engine optimization and AI-search-visibility platforms, call-intelligence and intake automation for law firms, voice AI, and analytics that help operators understand where their results actually come from.

His focus is less on AI as a headline and more on AI as something that has to work, in production, for people who have a business to run. He approaches it the way an operator does: start from the outcome, ship something real, measure it honestly, and keep what survives contact with the real world.

He writes for owners, marketers, and executives trying to separate what’s real in applied AI from what’s noise.

Outcome before architecture. The interesting question is never “what can AI do” — it’s “what does this business need to be true,” and then how little it takes to get there.

Ship, then refine. Working systems in production teach more in a week than planning does in a month. I build in tight loops and let reality do the editing.

Honest measurement. If you can’t see where a result came from, you can’t improve it. Most of my work ends in instrumentation as much as features.