Co-Intelligent Co-Operation
How to build the Human+AI Company of the Future…Today
Preface
We came to the same conclusion from opposite directions.
Jesse was in his office when a developer who had been with him for years put in his notice. He realized he’d just lost something he couldn’t replace. Not just the person, but their knowledge. Every system the developer understood, every shortcut he’d built, every decision embedded in the codebase: walking out the door in a banker’s box. Jesse had paid for years of that. It was awful. And awfully expensive.
That moment taught Jesse that knowledge in an organization is an asset. He’d been treating it as background — always there until it wasn’t. That started a years-long effort to manage knowledge deliberately. He built knowledge management systems. He trained the team to treat documentation, institutional memory, and structured information as real infrastructure, not an afterthought.
AI runs on knowledge: it’s only useful in proportion to the institutional context it can reach, and a company that has invested in curating that context can put it to work in ways a company whose knowledge lives in people’s heads can’t. (Chapter 5 makes the case in full.)
By the time AI entered the mainstream, the architecture was in place and the practices were habits. Double-digit growth on revenue and profitability followed.
In 2022, Julie was Global CHRO at a global food safety company when it completed the acquisition of a major division from a Fortune 500 company. Overnight, the organization expanded across 44 countries. The challenge wasn’t technology. In neither company had the work been explicitly designed before the acquisition: who owned what, how decisions moved, what knowledge lived where. It lived in people’s heads.
When Julie asked leadership teams to describe their operating model, they described their org chart. An org chart names the boxes. An operating model describes how work moves through them.
Both stories point to the same gap. AI is a new kind of worker that can be assigned tasks and held accountable for work with supervision (in varying degrees). It takes a task, produces an output, hands it off. And like any worker, it’s only as competent as the systems and knowledge it can reach: the documentation, the data, and the institutional context that let it act on more than what’s in the prompt.
When AI arrives inside your company, it arrives the same way a new employee does: looking for a seat, a scope, a set of handoffs, and the information it needs to do the work. The problem is that the work was never explicitly designed in the first place. There’s no seat for AI to occupy.
That’s the same gap that turned the developer’s departure into a fire drill, and that kept the operating model invisible until the acquisition forced it into the open. That’s why this book exists.
This book is about a framework, not a tool. Run it and you get top-line and bottom-line growth without headcount growing in lockstep, because AI is finally doing the work it was bought for and your team is doing the work only humans can. The hours your team gets back don’t disappear; they get redirected into the higher-judgment work people were hired for in the first place. The quality of work improves for everyone whose week used to be eaten by routing, formatting, and reporting.
The slope you’re bending has a name: revenue per employee. In most companies it stays roughly flat as they grow. Every productivity technology bends that slope a little; AI bends it dramatically, because agency lets it take on work the previous wave of tools could only assist with.
Operating design is how you get there. If you’ve bought tools and seen no return, this is why. If you want to know exactly what to do instead, read on.
By the last page, you’ll have a six-step Sequence: Signal, Source, Design, Build, Deliver, Compound — and the sprint structure to run it. You can pick this book up and run the framework yourself.
Your company runs on information, mediated by humans and, increasingly, by AI agents. The software you run, the databases you maintain, the documents your team creates hold that information. The people who manage them are the operators. That’s the change underway right now: information work no longer belongs only to humans.
Jesse builds AI systems inside operating companies. That’s how the framework in this book got tested before it was written. He also teaches AI-driven systems and organizational design at Michigan State University.
Julie was Global CHRO at a global food safety company when it absorbed a Fortune 500 division and expanded into 44 countries overnight. Over twenty years of leading people functions, she’s seen this same pattern in nearly every company she’s worked in: the way work actually moves is never what the org chart shows.
Between the two of us, we’ve worked inside companies of every size and most kinds of change management initiatives. AI is the next step in that arc.
A spreadsheet runs your formulas, a CRM tracks your contacts. Neither decides what to do next. AI does, but only inside the context you design for it: the workflow’s boundaries, the data it can reach, what it’s permitted to act on, when it must escalate. Humans set those boundaries and hold the authority to change them. That makes installing AI a technology problem and a management problem at the same time. That’s why we’re coming at it together. We call this new type of work Co-Intelligent Co-Operation.