Fortune covers the fourth year of the Wharton-GBK study, the first wave in which AI usage and enthusiasm moved in opposite directions. Wharton's Prasanna "Sonny" Tambe, who provides the article's title, describes the shift as amazement giving way to caution.
Mindspan Labs founder Jeremy Korst reads the cooling as a transition rather than a retreat — the move from experimenting with a technology to making it work inside an organization, from an "Oh, wow" level of excitement to the question of how the transition actually works.
That question lands somewhere specific. The checking, approving and deciding that AI creates falls on a layer that was already busy. Korst said it is "putting a bunch of strain in the middle of the organization," describing "middle managers and teams who are tasked to lead this transformation and do it right" — a layer that "doesn't have a lot of capacity."
And expanding capability makes it harder, not easier, because being able to do more does not tell anyone what is worth doing:
"How do I prioritize what I should be doing? Or else I can very quickly find myself running a new treadmill because of the technology."
The way we see it, that treadmill is what a static Core looks like from the inside. The capability arrived; the capacity to absorb it did not. Middle-management enablement is a Core element for exactly this reason — it is where strategy either becomes changed work or becomes additional work, and the second outcome is indistinguishable from the first on an adoption dashboard.
So what: If enthusiasm is falling while usage rises, look at what you have added to the middle of the organization without removing anything. Prioritization is the deliverable, and it is a leadership decision rather than a training problem.