
What Owning the Layer Actually Costs
Two months of telling you to own your coordination layer. Here's the part I left out: what it actually costs, and why two days of building isn't proof you need to be a software company to do this.
A CEO/CTO dialogue on shipping product with Skip Marshall & Chuck Greiss
Subscribe:.rss.atom.json.md.m3u.pls
Live Last read · last published · next check

Two months of telling you to own your coordination layer. Here's the part I left out: what it actually costs, and why two days of building isn't proof you need to be a software company to do this.

CRAFT OS started with the money and the pipeline. This is the rest of the map, and where it's headed next.

CRAFT OS was built to run client engagements. It runs ours now too, and the first proof of that is the most boring workflow we have: how our own team requests time off.

Six months of bringing CRAFT public. What we got right about coordination, what we would revise, and the question that decides the next three years: who owns the layer the business runs on.

Last week it was an advisor's office. This week, trucks and a dispatch board. Same operating system underneath. The hard part was never the technology. It was getting people to trust it.

Every AI tool this firm had tried worked in the demo and died in the building. The tools were fine. Nothing connected them. The fix was not a better tool. It was an operating system.

We built CRAFT to ship software. Then we ran it on a client's operations and almost nothing had to change. Same five phases, same artifacts. Here's what carried over, and where it stopped.

Conventional wisdom says big companies win the AI race: bigger budgets, more data. I think it is backwards. The advantage that matters most right now is structural, and owner-operators have it.

Customer service is where most mid-market companies try AI first. It is also where it most visibly fails. The problem is almost never the model. It is that nobody decided, on purpose, which work the A

Everyone is pricing AI by what it automates. After three years of watching it get bolted onto real businesses, I'm convinced the value sits somewhere else entirely: in the layer that coordinates the w

Two paths in front of you, and the wrong choice costs years. The diagnostic is not what you want to buy. It is where your business is actually breaking.

AI-Native and AI-enabled sound interchangeable. They are not. One is a label retrofitted onto old architecture. The other is a foundation. The gap compounds for years.

Most AI roll-outs stall in the same place. The model works. The team is willing. What's missing is the operating system that turns scattered tools into how the business actually runs.

This is the sixth and final article in "How We Actually Build," a series where we open up the CRAFT methodology one layer at a time. Three months in, this is the honest retrospective on running it.

The pre-deploy checklist that turns "done" from a vibe check into a question with a real answer.

This is the fourth article in "How We Actually Build," a series where we open up the CRAFT methodology one layer at a time as we roll it out to real client teams.

This is the third article in “How We Actually Build,” a series where Skip and I open up the CRAFT methodology one layer at a time as we roll it out to real client teams.

This is the second article in “How We Actually Build,” a series where Skip and I open up the CRAFT methodology one layer at a time as we roll it out to real client teams.

This is the first article in “How We Actually Build”...

The demo worked. The product didn't. Here's the question nobody asked.