Show Notes
In Episode 3, Chris Bradley introduces a three-habit framework built for continuous AI fluency: the AI Pulse, a monthly calibration that keeps your mental model of AI's capabilities current; the Workflow Lab, a weekly thirty-minute session dedicated to examining and redesigning one workflow at a time; and the Workflow Pause, an in-the-moment reflex that catches automation opportunities before you execute on autopilot.
The episode also shows how ODIF — the agentic direction framework from Episode 2 — maps directly onto workflow redesign, making every Lab session more rigorous and every output more immediately usable.
More from AI Transformation Lab
This episode starts in a supermarket. In 1956, Taiichi Ohno saw shelves that were never piled high — restocked only as customers took what they needed. The customer pulled. The store replaced. That became just-in-time, the second pillar of the Toyota Production System.
The episode opens on hoshin kanri, the Japanese practice documented by Bridgestone in 1965: leadership sets a fixed direction, and the people closest to the work discover the path. Lean calls that direction True North, and distinguishes two speeds of moving toward it — kaizen, continuous improvement, and kaikaku, radical change.
Chris Bradley argues that distinction now decides whether an enterprise AI program compounds or stalls. Ohno's rule was that without a standard there can be no kaizen. The AI version is stricter: you cannot automate a process that was never standardized, because there is nothing for the agent to execute. Most failed pilots, he suggests, are operations problems that AI happened to expose.
Sakichi Toyoda's breakthrough was not a loom that could handle a broken thread. It was a loom that noticed the thread had broken and stopped itself — which meant one operator could run dozens of machines instead of standing guard over one. That principle became jidoka, and Chris Bradley argues it is the most useful idea available to anyone deploying AI agents in an enterprise today.
Chris Bradley argues the churn is hiding something worth understanding. Those terms don't replace each other; they stack. And each one names a layer of the same discovery: that the model is not what separates an AI program that works from one that doesn't.