Notes
What enterprise AI adoption actually looks like between the demo and the dashboard. One enterprise rollout at OneDigital, plus eight side projects I run after hours.
I keep taking jobs I'm not qualified for on paper. It looks like nerve. It is actually a rule: cap the downside, and tell them exactly what you are not. The honesty is what makes the reach safe.
The two loudest stories about enterprise AI, that it deploys itself or that it replaces everyone, are both wrong. The real work is the boring middle. Notes from inside a 6,000-person AI program.
Meaning is retrospective. The operators who actually ship meaningful work didn't start by finding their why. They aimed at fun, new, smart people, and not letting down the people who bet on them. Here's the pattern up close.
I run an AI program for a 6,000-person company. I have tried three ways to measure whether people are using it well, and every clean metric measured something slightly off. Here is where my thinking has landed, for now.
Eight side properties as a curriculum, not a strategic portfolio. The GTM gap I'd lived with for fifteen years is closing, using the same AI-leverage that built the eight things in the first place.
The 2010s operator gospel (ideas are cheap, execution is everything) inverted sometime around 2023. Articulation became the bottleneck. Here's what that looks like up close.
Harvard published a case study on the AI program I run. I'm a self-described mediocre operator. I'm still trying to figure out what changed.