Ed is a partner at Machine & Folk, an AI strategy and development firm with 20 years of Silicon Valley runs on the board. More importantly, he's one of those rare humans who is fun to be around, relentlessly curious, and brilliant without being the least bit annoying about it. In New Zealand, he’s what we call a bloody good bloke.

Here's what's been rattling around in my head since we recorded.

Most business processes weren't designed around what's best. They were designed around what was possible. When the constraint disappears, most businesses keep the process anyway.

And that’s an underlying reason AI transformation so often fails.

I’m going to take you back, way back. I’m going to talk about cars. I’m going to talk about post-war Japan. I promise this is about AI. Stay with me!

But if, in the meantime, you want to take a step back and think through where AI and marketing can do strategic work in your business, I'd like that conversation.

In 1950, an obscure American statistician named W. Edwards Deming flew to Tokyo. Japan had just lost a war. Its factories were rubble. Japanese businessmen were desperate, and they turned to Deming to help them rebuild an economy shattered by World War II. His big idea: stop inspecting for quality at the end of the line. Build quality into the process from the start. Fix problems where they happen, not downstream after they've multiplied.

The Japanese took him extremely seriously. Toyota won the Deming Prize in 1965, and its chairman later said: "There is not a day that goes by that I do not think about what Dr. Deming means to our management."

America, meanwhile, was busy winning the postwar economy and largely ignored him.

Out of Deming's thinking came the Toyota Production System, and with it, the Andon cord. Every worker on the line could pull it the moment something went wrong. Teammates would swarm, fix the problem at the source, and keep moving. Across Toyota's plants, that cord was pulled once every few seconds. Stopping constantly made them faster.

American factories ran on the opposite logic: the line never stops. Faults got carried forward. Cars rolled into holding yards with problems baked in from station one.

Workers told stories of Monte Carlos with Regal front ends. Engines put in backwards. Cars were towed off the line because they wouldn't start.

In 1984, GM handed Toyota the keys to their worst plant: a factory in Fremont, California, notorious across the industry for drugs, absenteeism, and general industrial carnage. On any given day, one in five workers didn't show up. Workers brought thermoses of orange juice and vodka to the assembly line.

Toyota took the plant. Kept almost the entire workforce. Then, they flew them to Japan in groups of 30 to learn a completely different way of working.

Same people and Fremont, but defect rates dropped to match Toyota's plants in Japan.

That thinking eventually became Kaizen, which informed a 1986 Harvard Business Review article on iterative teams, which directly shaped the Agile Manifesto in 2001.

Deming. Toyota. Kaizen. Agile. A straight line from a postwar Tokyo lecture hall to your sprint planning. And now we’re here at AI (see: I promised we would get here).

In Slideshow (which you are 100% going to watch or listen to after this), Ed and I watched a tool called Pencil build a fully designed CRM interface from a single prompt. About ten minutes, start to finish.

But the more interesting conversation was about why the designer/developer handoff exists in the first place. Here come those constraints again.

Designers work in vectors. Developers work in code. The briefs, the markups, the JIRA tickets, the "this corner should be 8 pixels, not 4…." back-and-forth: none of that was a design decision. It was a constraint. Two different professional languages with no shared ground, and an expensive process built around the gap between them.

Pencil makes design and code the same thing. Ed’s team are already working in this new paradigm, and they’re sprinting a straight line while everyone else is jogging a circuitous route.

Ed also walked through a case study where his team automated an enterprise-scale data extraction system. Halfway through, they realised the entire workflow had been built around a human bottleneck that AI had just dissolved. The database structure, the roles, the whole process: all of it was shaped by a limitation that no longer existed. Once they saw it, they couldn't unsee it.

This transformation liberated the knowledge workers from repetitive work that AI, automation, and machine learning are frankly way better at. Now they’re focused on higher-value tasks that are not only more satisfying but also more billable.

Cognitive science is clear on this: every decision, strategic or trivial, draws from the same limited pool of mental energy. When people are freed from work that only existed because of a constraint, you don't just get time back. You get thinking back.

The ex-GM workers weren't bad workers. They were good workers trapped in a bad system. Sound familiar?

GM ended up sending their own people to Fremont. They watched the transformation happen with their own workforce. They had every opportunity to take it back to their other plants.

Most of them didn't. Or couldn't. The constraint was gone, but the habits, the assumptions, the org structures: all of it stayed.

That's the real risk with AI right now. The tools are changing fast. The processes, less so.

Ed goes deep on all of this in the aforementioned Slideshow with Dave Hayward, including a live CRM build in Pencil, from scratch, while I try to keep my jaw off the floor.

Cheers, Dave