In January 2021 an interviewer asked me for the biggest roadblock to AI adoption. My answer came down to one thing: expectations. I called them uninformed. Organizations believed AI was a drop-in component that would improve whatever process it touched. That belief, mixed with a fear of missing out, pushed companies to rush. I wrote that they were reaching for “a new shiny hammer looking for nails,” and that disillusionment would follow.
I still think that was right. The disillusionment arrived on schedule. But I was also wrong.
Two versions of one mistake
The 2021 belief was easy to state. Buy the model and the value follows. AI was a part you slotted into an existing workflow. Reality punished that belief fast, because building anything real was hard. You needed engineering time, data science help, and a business case strong enough to justify the spend. Projects that underestimated the work usually stalled before they shipped. The return died at the front of the pipeline, where the building happened.
The belief has since turned inside out. Building is easy now. A product manager can stand up an agent in an afternoon with no engineering queue in sight. So the new expectation is that easy building means easy value. If a workflow takes a day instead of a quarter, the returns should take care of themselves.
They don’t. And the reason traces back to the same root as before.
Capability was never the outcome
Both beliefs make the same move. They treat capability as if it were the outcome. Capability is what your AI can do. The outcome is a separate thing: what your organization still has once the AI has done it. The space between those two is where the return leaks away.
In 2021 that space was easy to see, because scarcity kept it visible. To build anything, a team had to win scarce engineering time. Winning it meant convincing people outside the team. A budget owner. An architect. A security reviewer. Nobody designed that as oversight. It was just the price of a scarce resource. It still worked like a filter. Weak ideas died in the queue, and only the ones someone could defend reached production. Scarcity was doing quiet work that never showed up on an org chart.
That filter is gone. When building costs almost nothing, nothing stops a weak idea from becoming a running system. Fifty teams can each ship their own agent, every one of them green on its own dashboard, and no single person owns the question of what they add up to. The return still leaks. It leaks at the far end of the pipeline now, in the cost of coordinating systems nobody mapped.
The newest version of the old belief
What is the belief being sold right now? The frontier model vendors have told the market that the gap to ROI is expertise. You have the models. What you lack is people who know how to wire them into your environment. So the labs send forward deployed engineers. They embed at your site, build the integrations, tune the configurations, debug the odd behavior, and leave a working system behind.
The role exists because my diagnosis is right. Building the model was never the hard part. Deploying it inside a messy enterprise is. FDEs are a real answer to that, and a good one. They are also an answer to the wrong problem, and their structure guarantees it.
Start with the incentive. An FDE works for the vendor. Success for them means adoption and a satisfied customer. They have no reason, and usually no mandate, to tell you that a deployment conflicts with a system three departments away that they cannot see, or that it will cost you more in complexity than it returns in efficiency, or that the right call is to not build it. The most valuable act of coherence is sometimes the word no. You cannot buy that from the party paid to say yes.
Then there is what they can see. An FDE embedded in one business unit knows that unit. They have no view of the other agents running across the company, or the coordination surfaces their new system quietly creates. The failures I worry about do not come from one bad deployment. They come from the sum of many reasonable ones. No FDE is positioned to see the sum.
And there is what they leave behind. When the engagement ends, the deepest understanding of why the system was built that way, what it assumes, and how to change it safely often leaves with them. You inherit a running system and a dependency, not the knowledge to oversee it over time.
So the FDE belief is the 2021 belief again, dressed for 2026. In 2021 it was “buy the model and value follows.” Now it is “add the deployment experts and value follows.” Both stop at capability. Deployment velocity is still capability. It is the thing every competitor can rent from the same labs, on the same terms, in the same quarter. It gets the system live. It does not decide whether the system should have gone live at all, and it does not hold the enterprise together once fifty of them are running. FDEs solve half the problem. The half they cannot touch is the one that decides your return.
Why the old advice still works
Back then I offered a few tips. Go slow. Pick narrow, well-defined use cases. Get a quick win on the board. Kill a project when the evidence says to, and keep sunk cost from making that call for you. I stand behind every word of it. What surprises me is why it still holds.
In 2021 that discipline was prudence. Ignore it and scarcity would punish you, so the advice helped you get through the queue with something that worked. Today the same discipline is close to the only filter left in the building. “Go slow” used to save you from a stalled project. Now it is most of what stands between you and a sprawl of systems you cannot see. “Kill the project” used to fight sunk cost. Now it fights the agent that keeps running because nobody confirmed it should stop.
The advice is unchanged. What used to enforce it for free has disappeared, so the work now falls to you. Scarcity handled a crude version of this by accident. You have to handle the real version on purpose.
The name I was missing
When I wrote that post I was reaching for something I could not name. I knew rushing was dangerous. I knew AI was a means to an end. I had no word for the property that separates a company that gets value from one that gets debt.
The word is coherence. It is the capacity to see what your autonomous systems are doing, judge whether they are doing it well, and correct them when they are not. In 2021, scarcity supplied a crude version of it by accident. In 2026, you build it on purpose or you go without. Once execution gets cheap, coherence becomes the thing that decides whether all that abundance turns into advantage or into cleanup.
The disillusionment I flagged five years ago is still on its way to a lot of organizations. It reaches them from the opposite direction now. Back then it came from systems that were too hard to build. Now it comes from systems that are too easy to build. The belief underneath is the one I named in 2021. Stop mistaking what AI can do for what your organization will keep, and build the coherence that turns the first into the second.
The full argument runs through my book, Coherence, arriving this Fall. If you want to follow the thinking as it develops, join the list at coherise.com. The one-page decision tool from the book is the first thing I send.