Where the capability sits

Centralized, federated, or hub-and-spoke - and what each shape can and cannot do.

65documents on this topic
27organizations represented
4issues named
15sourced citations
0sourced statistics

The state of it

One of 4 topics within Operating model redesign.

The shape question - one central team, capability federated into the businesses, or a hub coordinating spokes - has a measured answer, and it is not the fashionable one. Across a survey of more than 600 Chief AI Officers, those running hub-and-spoke or centralized operating models report 36% higher AI ROI than those managing decentralized ones, and move roughly twice as many pilots into production. The same survey observes the direction of travel: organizations generally start decentralized and consolidate as they scale, not the reverse. The figures are self-reported by the people running the models, so they carry the usual generosity - but the gap is large and the mechanism is plain: without a concentrating point, every function buys its own tools, learns the same lessons separately, and nothing compounds.

Sitting beside that finding is an observation that complicates it. AI does not arrive through one door. It comes embedded in software already owned, it is brought in by individual departments as best-of-breed point tools, and it is built or blended in-house - three inflows, only one of which a central team actually produces. A pure central model is therefore a fiction: the realistic design is a small concentrating core that sets standards and manages demand, wrapped around inflows it does not control.

The trap in this band is treating the shape as the whole answer. A structure with no operating rhythm - no defined cadence for who decides what, at what level, how often - reproduces the old organization's speed with a new org chart. The documents that describe working models describe the cadence in as much detail as the boxes.

The issues, by agreement

How many independent organizations name each issue as a problem. An issue is only as real as the number of separate publishers that identify it, so the count is the ranking. Bars are organizations, not documents. Where the count reads ours, no publisher here states the issue and the analysis is our own.

names it as a problemdisputes itqualifies it
The center of excellence becomes the next silo
2
The structure is redrawn but the rhythm is not
2
The shape question has a measured answer, and it favors concentration
1
AI arrives through three doors, and the operating model watches one
1

Who takes which position

The chart above counts positions; this shows whose they are. Read down a column for what one organization holds across the whole topic, and across a row for who lines up on one issue. Where a cell carries more than one position, the strongest is shown and the rest are in the tooltip.

Ddisputes it Qqualifies it Nnames it as a problem Pproposes a fix
Where the capability sits: 4 issues against the 7 organizations cited on them. The number under each name is how many of these issues it is cited on.
Issue Microsoft · 3 Gartner · 2 IBM · 2 IDC · 2 KPMG · 2 Australian Government · 1 EY · 1
The center of excellence becomes the next silo P · N P · · N
The structure is redrawn but the rhythm is not P · · · N N ·
The shape question has a measured answer, and it favors concentration · Q N P · · ·
AI arrives through three doors, and the operating model watches one P N · · Q · ·

A dot means this organization is not cited on that issue. It does not mean they are silent on it: an organization is cited where its document takes a position we could locate, and the absence of a citation is the absence of a finding, not a finding of absence. Who is represented lists everyone working on this topic, including those not cited above.

Where they disagree

No contradictions recorded on this topic yet.

The issues in full

Each issue carries the organizations that name it, the numbers behind it, and the remedies proposed - with the concrete steps under each. Every citation points at a section of a named document, so any count here can be checked.

Issue 012 organizations name it2026 evidence

The center of excellence becomes the next silo

A new AI center of excellence is stood up beside the existing data, automation and security functions. Within a year it is a parallel organization competing for the same work, and integration is harder than before it existed.

The warning comes from the survey with the most Chief AI Officers in it: a team that duplicates the existing technology workforce becomes a shadow IT department that makes integration more complex, and smaller, complementary teams embedded across the organization do better. The alternative to a new silo is expansion of what exists - organizations with a working governance body for automation or data extend its scope to agents and models rather than founding a rival. The test is simple: if the new function's org chart mirrors the old one's, it is a competitor, not a center.

How to fix it — 1 approach, 3 steps

Extend an existing body before founding a new one

Before creating an AI function, test whether an existing governance or platform team can take the scope - and if a new team is created, keep it small and embed its people in the functions.

Done when The bodies whose scope overlaps the proposed center are mapped with what each owns, and either the scope went to an existing body or the reason it could not is written down alongside a capped central headcount.

  1. Map the existing bodies whose scope overlaps the proposed center, and what each owns.0-30 daysCIO
  2. Give the scope to an existing body where one fits; write down why if not.30-90 daysCEO
  3. Cap the central team and embed specialists in functions rather than pooling them.90-180 daysChief AI officer
The evidence — 4 documents
OrganizationDocumentPosition
EYConsultancy · January 2026EY’s AI-first managed servicesOur reading Names siloed execution as one of seven readiness gaps that stop AI reaching business processes, stating it flatly rather than as a caveat, and does not distinguish a new AI function from any other silo.Siloed execution as a barriernames it
IBMHyperscalerC-suite synergies for AI ROIOur reading Warns that a team duplicating the existing technology workforce becomes a shadow IT department focused on AI and makes integration more complex, and finds the average team of a Chief AI Officer is five people, with smaller teams less successful.Complement, not duplicatenames it
IDCConsultancy · March 2025Six priorities for AI-fueled firmsOur reading Proposes the center of excellence as the mechanism for sharing practice and coordinating cross-functional teams, in service of moving work out of the pilot stage.What the center is forproposes a fix
MicrosoftHyperscalerThe CIO playbookOur reading Proposes expanding an existing governance body's scope to cover agents rather than founding a new one, and integrating it with the data, security and compliance bodies to avoid duplication and fragmented oversight.Extend the governance that existsproposes a fix

Issue 022 organizations name it2026 evidence

The structure is redrawn but the rhythm is not

The org chart changes; the meeting cadence does not. Strategic decisions still queue for a quarterly steering committee while the technology and the vendors move monthly.

The working models described in the documents specify cadence as precisely as structure: a leadership tier setting direction quarterly, a senior tier translating it monthly, a governance tier resolving issues weekly, and builders working daily with fast feedback loops. Each tier has named inputs and outputs, so an escalation has somewhere to land inside a week rather than a quarter. The observation underneath is that traditional delivery rhythms - annual planning, quarterly review - were tuned for technology that changed yearly, and this one does not.

How to fix it — 1 approach, 3 steps

Give every decision type a tier and a clock

Assign each recurring decision - funding, approval, standards, escalation - to a tier with a stated cadence, so nothing waits a quarter that needs a week.

Done when Every recurring decision type is assigned to a tier with a stated cadence, the wait times from the last quarter are on record as the baseline, and the escalation path is published so a blocked team knows which tier unblocks it.

  1. List the decisions made in the last quarter and how long each waited for a meeting.0-30 daysChief of Staff
  2. Assign each decision type to a tier: quarterly direction, monthly priorities, weekly issues.30-90 daysCEO
  3. Publish the escalation path so a blocked team knows which tier unblocks it.30-90 daysChief AI officer
The evidence — 3 documents
OrganizationDocumentPosition
Australian GovernmentInstitution · November 2025Gov Australia published a CAIO handbookOur reading Explains the role by tempo: the technology evolves monthly rather than annually and outruns the usual technology adoption cycles, and the answer offered is dedicated leadership matching that pace rather than a change to how decisions are timed.Pace as the reason for the rolenames it
KPMGConsultancy · June 2026The CIO’s moment of reinventionOur reading Argues that traditional structures, roles and delivery rhythms are not equipped for the speed, autonomy and cross-functional orchestration this technology requires.Rhythms tuned for a slower technologynames it
MicrosoftHyperscalerThe CIO playbookOur reading Describes a working structure of leadership setting vision quarterly, senior leadership translating priorities monthly, a governance group resolving issues weekly, and builders working daily, each tier with named inputs and outputs.Four tiers, four cadencesproposes a fix

Issue 031 organization name it1 qualifies itnewest evidence Mar 2025

The shape question has a measured answer, and it favors concentration

Where the AI capability sits is treated as a matter of taste or culture. The one large survey that measures it finds the more concentrated shapes deliver materially more, and that organizations consolidate as they scale.

The mechanism matters more than the number. A concentrating point lets one team carry lessons between functions, negotiate with vendors once, and kill duplicated efforts early - which is exactly the work that produces the pilot-to-production difference. The figures are self-assessed by the leaders running each model, so treat the 36% as a direction rather than a promise. The direction is consistent: scaling organizations move toward hub-and-spoke, not away from it, and the survey's own practitioners argue that without centralization there is no clear ownership.

How to fix it — 1 approach, 3 steps

Write down which shape you are running, and what would change it

State explicitly whether the model is centralized, hub-and-spoke, or federated, which decisions sit at the hub, and what evidence would trigger a change - rather than letting the shape be whatever grew.

Done when A written statement names the model as centralized, hub-and-spoke or federated, records which decisions sit at the hub and which stay federated with reasons, and names the evidence that would trigger a change.

  1. Document the current de facto model: who funds, who approves, who builds, per function.0-30 daysChief AI officer
  2. Decide which of those decisions move to the hub, and which stay federated, with reasons.30-90 daysCEO
  3. Review the shape against pilot-to-production numbers twice a year.ongoingChief AI officer
The evidence — 4 documents
OrganizationDocumentPosition
IBMHyperscalerC-suite synergies for AI ROIOur reading Reports 36% higher AI ROI for Chief AI Officers running hub-and-spoke or centralized operating models than for those managing decentralized ones, and roughly twice as many pilots reaching production, from a survey of more than 600 such officers fielded in the first quarter of 2025.Operating model versus reported ROInames it
IBMHyperscalerC-suite synergies for AI ROIOur reading Observes that organizations generally move from decentralized toward centralized or hub-and-spoke models as AI scales, rather than the reverse.The direction of travelnames it
GartnerConsultancy · March 2025Build, buy or blendOur reading Qualifies the case for concentration by holding that AI now arrives from three directions at once - embedded in software already owned, bought by individual business units, and built centrally - so the centre's job is to govern the mix rather than to own all of it.What the center can actually controlqualifies it
IDCConsultancy · March 2025Six priorities for AI-fueled firmsOur reading Proposes a center of excellence to centralize expertise and coordinate cross-functional teams, citing organizations that ran an average of 24 pilots in a year and moved 3 into production.A concentrating point for expertiseproposes a fix

Issue 041 organization name it1 qualifies itnewest evidence Jun 2026

AI arrives through three doors, and the operating model watches one

The operating model is designed around what the central team builds. Most AI actually enters embedded in software the organization already owns, or brought in by departments as point tools - inflows the model never sees.

Embedded features switch on when a vendor ships an upgrade, not when a committee approves them. Department-bought point tools each look harmless; their cumulative effect is overlap, duplicated spend and technical debt that nobody chose. An operating model that only governs the in-house pipeline is governing the smallest of the three inflows. The corrective is unglamorous: an inventory that covers all three, a demand-management function that decides which inflow serves which need, and standards that apply regardless of where a capability came from.

How to fix it — 1 approach, 3 steps

Inventory what arrived through every door, not just the pipeline

Count the embedded features switched on, the department-bought tools in use, and the in-house systems - and apply one standard to all three.

Done when One list covers embedded features, department-bought tools and in-house systems together, the same security and data standards are applied to all three, and new demand arrives through a single intake that chooses the inflow deliberately.

  1. List AI capabilities in use by inflow: embedded, department-bought, built in-house.0-30 daysCIO
  2. Apply the same security and data standards to all three, whatever their origin.30-90 daysCISO
  3. Route new demand through one intake that chooses the inflow deliberately.90-180 daysChief AI officer
The evidence — 4 documents
OrganizationDocumentPosition
GartnerConsultancy · March 2025Build, buy or blendOur reading Names three distinct inflows - AI features embedded in existing applications, independent best-of-breed tools brought in by individual departments, and capability built or blended in-house - and describes embedded AI as the largest and fastest-growing of them.Three sources of AInames it
GartnerConsultancy · March 2025Build, buy or blendOur reading Proposes a central AI committee to manage demand for organizations scaling ten or fewer initiatives, and argues human governance alone will not be fast or reliable enough beyond that.A central committee for demandproposes a fix
KPMGConsultancy · June 2026The CIO’s moment of reinventionOur reading Qualifies the cause rather than the effect: the damage is attributed to initiatives led from the technology side without a matching investment in the people using them, not to decentralised buying as such.The cost of ungoverned inflowsqualifies it
MicrosoftHyperscalerThe CIO playbookOur reading Proposes a zoned model in which isolated experimentation, team-level work and production systems each get a different level of control, so oversight follows risk rather than applying uniformly.Oversight scaled to riskproposes a fix

Who is represented

This dossier is drawn from 29 organizations working on the subject, 7 of which are cited directly in the issues above.

Consultancy — 15

EY 4 KPMG 3 Gartner 1 IDC 1 Boston Consulting Group 10 McKinsey & Company 7 Accenture 4 Deloitte 3 Capgemini 2 Arthur D. Little 1 Booz Allen Hamilton 1 Cognizant 1 Genpact 1 Heidrick & Struggles 1 The Hackett Group 1

Institution — 6

Australian Government 1 Cloud Security Alliance 3 World Economic Forum 2 Citi 1 FinOps Foundation 1 The Conference Board 1

Academic — 2

Carnegie Mellon SEI 1 National Bureau of Economic Research 1

Hyperscaler — 4

IBM 6 Microsoft 4 Google Cloud 2 AWS 1

Frontier lab — 1

OpenAI 1

Vendor — 1

TechWolf 1