Show Notes
The gap between AI users who've adapted to 2026 capabilities and those still using 2024 techniques is creating a significant productivity divide and the difference isn't about being smarter, it's about understanding what changed.
In this episode of the AI Transformation Lab Podcast, host Chris Bradley, Chief Marketing Officer at Veritiv and head of their AI Transformation Lab, reveals how agentic AI tools have fundamentally shifted how we work with artificial intelligence. Bradley walks through a real example: a competitive analysis that took one to two weeks in early 2025 now takes thirty minutes, with exponentially better results. He unpacks the techniques driving this transformation and explains why elaborate prompting strategies have become obsolete.
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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.