McKinsey Is Right About the Moat. Here’s the Half It Misses.

McKinsey published a piece this month that gets the hardest part right. Its argument, in one line: the advantage in AI is not the tools, it is the operating model, and the operating model is the one thing a competitor cannot buy. I agree with almost all of it. I want to push on the part it leaves out, because that part is where most companies are about to get hurt.

Start with what McKinsey gets right, because it is a lot. Efficiency gains from AI, they argue, will become table stakes as the technology spreads. Operating models, unlike software, cannot be purchased or copied overnight, so the durable moat is the organization, not the model. They have the numbers to go with it. Only about a fifth of companies have fundamentally redesigned how they work around AI. Top performers are three times more likely to have done that redesign, and twice as likely to redesign the workflow before choosing the tool. And AI programs run as technology projects fail at more than an eighty percent rate, because they optimize the tool instead of changing how the company works.

If you have read anything I have written, you know why I would agree. This is the commoditization argument. Capability is becoming universal, so capability stops being the edge, and the advantage moves to what the organization can do that a competitor cannot copy. McKinsey and I are looking at the same shift.

Here is where we part.

The scissors cut both ways

McKinsey’s best image is what they call the complexity scissors. As a company grows, revenue grows not on a line but a curve – fast initially and flattening later. But coordination costs, the meetings and committees and management layers, keep climbing. Plot the two lines and they open like a pair of scissors. The gap between them is why big companies slow down, and why fewer than one in ten sustain returns above their cost of capital over a decade.

Their prescription is to use AI to close the scissors. Route decisions through a central orchestration layer. Hand coordination work to agents. Flatten the org. Get faster.

This is the step I want to stop on. AI can close the scissors. It can also open them wider, and nothing in the redesign itself tells you which one you are going to get.

McKinsey is right that agents let you route around the old coordination layer. What they underplay is that agents build a new coordination surface underneath, invisible, machine-speed, and owned by no one.

Every autonomous system you add to speed up a workflow is also a new thing that has to stay consistent with every other autonomous system. A support agent and a billing agent that make different assumptions about the same customer have not reduced coordination cost. They have created a new kind of it, one that does not show up in a meeting because no human is in the loop to notice. McKinsey is right that agents let you route around the old coordination layer. What they underplay is that agents build a new coordination surface underneath, invisible, machine-speed, and owned by no one. Their own report admits the danger in a single line: when agents run inside workflows that were not redesigned for them, errors propagate across the company at machine speed. That is the coordination trap, and it is produced by the very rewiring they recommend.

So the redesign is not the safe move and the caution. The redesign is the risk. Done with the discipline to keep the new systems coherent, it closes the scissors. Done as a race to orchestrate and flatten, it opens them, and it does so faster than the old human version ever could, because now the coordination failures happen at the speed of software.

This is not just my read of the mechanism. IBM’s 2026 study of two thousand CIOs and CTOs found the same trap from the inside. Companies that chase speed let business units move ahead while governance falls behind, gaining local velocity and losing containment. Companies that chase safety slow deployment under review until oversight becomes unmanageable. Both paths, in the study’s own words, accumulate strategic debt. That is the point. The rewiring does not have a safe default. It has two ways to fail and one narrow way to work, and the narrow way runs through coherence.

The rewiring does not have a safe default. It has two ways to fail and one narrow way to work, and the narrow way runs through coherence.

Their own examples make the point

Look closely at the cases McKinsey uses, because they prove the thing the article does not quite say out loud.

The copper miner they profile did not win by deploying more AI. It won by building modular models where roughly sixty percent of the code from the first site was reusable across the next six, so each deployment got faster and cleaner than the last. That is a coherence story wearing a productivity headline. The reuse is only possible because someone designed the systems to fit together before scaling them. The car maker they profile shrank a planning team by more than eighty percent, but the win was not the headcount. It was that the coordination layers between data and decision compressed, because the workflow was redesigned as one coherent thing instead of a chain of handoffs.

In both cases the value came from making the systems cohere, not from the number of systems shipped. McKinsey files this under operating-model redesign. I would file it more precisely: the redesign worked because it was coherent, and it would have failed if it were not. The article treats coherence as a happy property of good redesign. I think it is the whole variable, and that a redesign optimized for speed without it produces the opposite result in the same enterprise.

What to actually do differently

If you take McKinsey’s advice and only McKinsey’s advice, you will redesign for speed and measure yourself on how fast you moved. That is the eighty-percent-failure path wearing better clothes, because deployment speed is exactly the vanity metric that hides the debt building underneath.

The addition is small to state and hard to do. Before you rewire a workflow around agents, decide how those agents will stay consistent with the rest of the company as they multiply. Build the ability to see what your autonomous systems are doing in aggregate, contain them so a failure in one stays in one, and set in advance how much each is allowed to decide. Then measure the redesign not by how fast it shipped but by whether the organization got more coherent or less as it grew. A team that retired four brittle systems and shipped nothing new may have improved your position more than the team that shipped fourteen agents into the trap.

McKinsey is right that the operating model is the moat, and right that most companies are getting this wrong by treating AI as a tool to buy rather than a business to redesign. The correction I would add is that the redesign has a failure mode of its own, and it is the one nobody is watching for. The winners will not be the companies that rewire fastest. They will be the ones that rewire coherently, which is a slower thing to say and a harder thing to build, and the only version that closes the scissors instead of opening them.

The winners will not be the companies that rewire fastest. They will be the ones that rewire coherently.

That is the subject of my book, Coherence, and of everything I am writing here between now and launch. If the rewiring is on your desk right now, the one-page tool I use to sort what to automate, what to redesign, and what to leave alone is the first thing I send when you join the list at coherise.com.