Every AI centre of excellence I have walked into has the same three pieces of furniture. An intake form. A scoring rubric. A monthly meeting where the rubric is applied to the forms. The form asks for a business case before anyone has touched a model. The rubric is written by people who will not do the work. The meeting is monthly, which sets the fastest possible path from idea to permission at thirty days.
In the first quarter of 2026, Gartner surveyed 12,004 employees and managers across 40 countries. Of the ones whose employer had already given them enterprise AI access, 88% were using their own personal AI tools for work anyway.
That is a verdict. The people the centre exists to serve have already priced its output and routed around it.
The argument
A centre of excellence can only supply the things it controls. It controls tools, standards and approvals. It does not control workflows. Workflow redesign is the one variable that separates the companies getting AI value from the companies getting AI invoices. So the centre is structurally incapable of producing the outcome it was funded to produce. It is not a capability function. It is a permission desk with a budget.
What the numbers actually say
McKinsey ran its global AI survey between 4 May and 8 June 2026: 1,719 respondents across 97 nations, 36% of them at companies above one billion dollars in revenue. Thirty-seven percent attribute at least some EBIT impact to AI, which is about the same share as the year before. Six percent are high performers who attribute 5% or more of EBIT to AI use. The thing that separates them is not their model choice. Nearly three quarters of the high performers report fundamentally redesigning workflows, against 25% of everyone else.
Now put the spend next to it. BCG’s AI Radar 2026, published January 2026, surveyed 2,360 executives including 640 CEOs across 15 markets. AI investment is set to roughly double this year, from about 0.8% of annual revenue to about 1.7%. On a one billion dollar business that is a move from roughly 8 million dollars to roughly 17 million dollars in twelve months. In the same survey, 94% of CEOs said they would keep investing even if 2026 initiatives underperformed.
The spend doubles. The share of companies reporting any EBIT impact does not move. The marginal nine million dollars bought no measured change in the odds of impact.
Here is why. That money is being spent by a function whose authority stops at the tool. A centre of excellence can buy a model, set a standard, run an evaluation, approve a vendor and publish a prompt library. It cannot tell the claims team to stop doing the second review. It cannot collapse three handoffs into one. It cannot change who signs. Those are the acts that move a line in the P&L, and they belong to whoever owns the line.
MIT’s NANDA study on the GenAI divide ran from January to June 2025 across 52 structured interviews, 153 survey responses and more than 300 publicly disclosed initiatives. Its finding on organisational design is one sentence long and almost nobody quotes it: the organisations that succeeded sourced their AI initiatives from frontline managers, not central labs. The same study found pilots built through external partnerships reached full deployment 66% of the time, against 33% for pilots built internally. Task-specific internal tools reached production 5% of the time. General purpose tools bought off the shelf reached production 40% of the time.
A centre of excellence is an internal build shop staffed by people whose standing depends on internal builds.
I stopped opening engagements with the AI strategy deck about two years ago. I ask for two artefacts instead: the list of people who can change a business process without seeking approval, and the central team’s intake queue. The list usually fits on one hand. The queue usually runs to dozens. Everything that goes wrong over the following year is already legible in that ratio, and thirty years of writing software has not shown me a tool that repairs it.
The mechanism
A use case is found by the person doing the work. Everything after that is decided by one question: who is allowed to change the process. If the central team holds the tools and nothing else, the idea goes onto an intake form, waits for a monthly rubric, and comes back as an approved tool sitting on top of an untouched workflow. That is a pilot with no line in the P&L. If the unit that owns the process holds the decision, the workflow is redesigned first and tooled second, and the impact lands in that unit’s own numbers. Same idea, same model, same vendor. Different owner, different outcome.
Three things that have already been measured
The shadow economy was counted twice, a year apart, and it grew. In mid 2025 MIT found that only 40% of companies had bought an official LLM subscription while workers at more than 90% of surveyed companies were using personal AI tools for work. The obvious reading was a procurement gap: buy the licences and the shadow disappears. Gartner’s first quarter 2026 data says it did not. Among employees who had enterprise access, 88% still used personal tools, and those hybrid users were 1.7 times more likely to report significant time savings than the compliant ones. The shadow is not a licensing failure. It is people avoiding the latency of the approved path, and they are right to, because it makes them faster.
Uniform governance is the only kind a central body can produce. On 26 May 2026 Gartner predicted that 40% of enterprises will demote or decommission autonomous AI agents by 2027 after governance gaps surface in production. Shiva Varma, the analyst behind it, put the cause plainly: enterprises treat agent governance as binary, either locked down or fully trusted, and that is the root of the failure. Over-restriction of simple agents, in his words, slows delivery and drives shadow development. A central body cannot hold the context of forty different workflows, so it writes one rule and applies it to all of them. That is not a staffing problem. It is what centralisation is.
The talent leaves. On 13 May 2026 Gartner predicted that half of enterprises without a people-centric AI strategy will lose their top AI talent to competitors by 2027. In the same research, 27% of executives reported having a comprehensive AI strategy and 20% of leaders believed their workforce was AI-ready. A centre of excellence takes the scarcest engineers in the company, concentrates them in one team, and hands them a queue of intake forms to triage. There is no faster way to lose them.
Where this breaks
Four places, and I will not pretend they are small.
Comparing BCG’s spending figure with McKinsey’s impact figure is not a panel study. Different samples, different questions, different months. The doubling and the flat line are directional, not a measurement, and anyone who wants to reject that inference is entitled to.
The MIT sample is small, partly recruited at conferences, and the fieldwork is now more than a year old. Its 95% headline has been over-read by almost everyone who has quoted it, including people arguing my side. The frontline-managers finding is qualitative and it is one sentence.
Regulated industries are a real exception. Model risk management in a bank, clinical safety in a hospital, anything touching defence. There a central body is a legal requirement and removing it is not an option. In those settings the right question is how fast the central body can say yes, not whether it should exist.
And the strongest objection, which I hold myself: some things must be central. Identity, the evaluation harness, data contracts, model procurement, the audit trail. That is a platform team, and platform teams belong in the middle. The failure mode is not centralisation. It is merging the platform team with the permission desk, so that the group who builds the substrate is also the group who decides who is allowed to use it. Below roughly two hundred people none of this applies, because there is no centre to dismantle.
What I would do on Monday
Write down every person who can change a business process without asking anyone. If that list does not contain a person who touches the workflow you want AI inside, nothing you buy this year will show up in the P&L.
Split the central team on paper into two functions. Platform keeps identity, evaluation, data contracts and model procurement. Permission is deleted, and the decision moves to whoever carries the P&L line.
Set a dollar threshold under which no approval is required, only a note afterwards. Pick the number that makes your CFO slightly uncomfortable and publish it where everyone can see it.
Measure approval latency. Count the days from a named person having an idea to that same person being allowed to try it. If the answer is longer than five days, the 88% is your number too.
Default to buying. Sixty-six percent against thirty-three percent. Put the ratio into the intake criteria and make anyone proposing an internal build argue against it in writing.
The intake form is not the problem. The form is the confession. It says the company has decided that the risk of one person trying the wrong thing is larger than the risk of nobody trying anything, and most of the money burned in the last three years sits inside that sentence.
Keep the platform. Keep the evaluation harness. Burn the form.
I spend most of my week inside delivery organisations working out why an approved tool never reached the P&L. If that is the gap you are looking at, book a call.