Early in my career, I edited technical documents at a small oil and gas company. I spent my days moving text between spreadsheets, copyediting and reformatting Word docs, and tracking versions that existed in five places at once. I remember thinking, there has to be a better way to do this work.

Years later, I'm still watching people do it the hard way — as a content writer at a scrappy digital marketing agency, as a content manager for a team of freelance writers, and as a senior copywriter and strategist at a marketing agency serving clients across industries: managed IT, regional food brands, behavioral healthcare, commercial interiors, dental, security. The work was varied. The operational pain was consistent.

I watched account directors spend eight hours a month in task-entry meetings, punching work into ClickUp that already existed in two other places. I saw talented writers and designers lose their afternoons to administrative process work that seemed to multiply every year, because no one had time to slow down and develop a better system.

Then I moved in-house. Without the pressure of a retainer, scope exploded. My team became order-takers — reacting to every department's requests rather than driving strategy. People with decades of combined experience were spending their time on custom decks and quick-turn assets that should have taken minutes. Everyone was working hard, but very little of it felt like the work we'd been hired to do.

When ChatGPT and its peers arrived, everyone hoped they would save time. Mostly, what we got were drafts that sounded generic, misunderstood the audience, or introduced nearly imperceptible errors that required careful review to catch. Rewriting output ate up the time AI was supposed to save. The tools have improved since then, but the essential problem remains: many teams haven't engaged in the foundational work of teaching AI what good looks like for their particular business.

That's the work I do now. I build structured knowledge systems — governed, documented, AI-ready foundations — for marketing and revenue teams hired to create, strategize, and lead.

The discourse around AI tends to get stuck in a binary — it's either leading us to a promised land of ease or a post-apocalyptic hellscape. I myself am a conscientious pragmatist who thinks between the poles. I'm interested in when, where, why, and how? With what guardrails? For whose benefit, and to what effect on humans and the environment?

John Dewey wrote that "reflective thinking ... means judgment suspended during further inquiry." Most AI adoption fails because the reflective thinking never happens. Teams get the tools, work around them, patch the outputs, and either convince themselves things are improving or give up on the system altogether. My goal is to slow the process down just enough to build what's needed — and then speed everything up, permanently.