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For most companies, a federated “hub-and-spoke” model is the practical destination: a central team owns the platform, identity, security baseline, architecture standards, registries, and risk controls, while business teams build, run, and improve use cases. Start with more central control when expertise is scarce, maturity is low, regulation is demanding, or agents will face customers or execute actions in enterprise systems. Move delivery outward only when teams can operate the full lifecycle and automated controls make governance enforceable.
This is not a one-time choice. Microsoft notes that most organizations run two or three patterns at once, blending centralized and federated arrangements; at scale, the common pattern is a central platform with federated delivery, also called hub and spoke. Microsoft’s operating-model guidance and AWS guidance on generative-AI operating models both describe this progression.
What the three AI operating models mean
An operating model defines who decides, who builds, who runs production systems, and who is accountable when an AI system fails. The labels describe where authority and capability sit; they do not dictate a particular technology stack.
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One enterprise team sets strategy, selects models, builds agents or machine-learning systems, provisions environments, and monitors production. Business units request work through that team. Centralization creates a single quality bar and a clear escalation path, which is valuable when an organization has only a few specialists or must prove consistent compliance.
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- Typical ownership: strategy, architecture, data access, model selection, deployment, monitoring, incident response, and improvement.
- Main advantages: consistent controls, easier audit evidence, concentrated expertise, less duplicated tooling, and stronger strategic alignment.
- Main risks: a growing request queue, slow domain feedback, limited local experimentation, and a central team that becomes responsible for business decisions it cannot fully understand.
Decentralized AI teams
Each business unit owns its AI strategy and usually its delivery and operations. A claims department, marketing group, plant, or regional subsidiary can select tools and release use cases without waiting for an enterprise queue.
- Typical ownership: local product roadmaps, data preparation, model or agent development, deployment, and day-to-day support.
- Main advantages: fast iteration, close domain knowledge, direct accountability for local outcomes, and room for specialized solutions.
- Main risks: duplicated platforms, inconsistent evaluations, fragmented visibility, standards drift, uneven security, and uncertainty about who owns a cross-enterprise incident.
Hybrid or federated (hub-and-spoke) model
A central platform or center of excellence provides the paved road: identity, approved model access, environments, security controls, policy, evaluation methods, registries, logging, and reusable patterns. Domain teams are the spokes that own product decisions and operate approved use cases.
- Central hub: platform engineering, architecture standards, security and privacy controls, risk appetite, model and agent registries, common evaluations, and enterprise observability.
- Business spokes: use-case discovery, domain data stewardship, prompt or workflow design, user adoption, operational metrics, and first-line incident response.
- Trade-off: the model balances speed and control but requires explicit decision rights and active coordination.
How the models compare
Choose by examining authority, risk, delivery economics, and lifecycle capability—not by counting how many people report to a central vice president. The table below shows the usual pattern; an individual company can combine rows differently.
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| Decision axis | Centralized | Federated / hybrid | Decentralized |
|---|---|---|---|
| Decision rights | One enterprise team decides most technical and delivery questions. | Central team decides shared controls; domain teams decide product and workflow details. | Business units decide strategy, delivery, and often operations. |
| Governance and auditability | Uniform policies and evidence are easiest to enforce. | Common controls are built once and applied locally through the platform. | Evidence and policy interpretation can differ by unit. |
| Time to market | Fast for a small portfolio; slows as the central queue grows. | Fast after paved roads and self-service controls are in place. | Fast for local experiments, with more rework when standards conflict. |
| Domain proximity | Specialists may be distant from frontline workflows. | Product ownership stays with the people who know the process. | Highest local proximity. |
| Talent utilization | Scarce experts are concentrated and reused. | Platform specialists and domain specialists have distinct roles. | Similar skills may be hired repeatedly in several units. |
| Reuse of platforms and patterns | High if the central team publishes usable components. | High when reusable services are mandatory on the paved road. | Variable; duplication is common. |
| Operating cost | Lower duplication, but a large central team can become expensive. | Shared platform cost plus local product teams. | Lower central overhead but potentially high duplicated spend. |
| Data and regulatory risk | Consistent controls are simpler to review. | Central guardrails with local data stewardship. | Control quality can vary materially between units. |
| Incident accountability | One team owns most incidents, which can hide business-process responsibility. | Spokes handle first response; the hub handles platform and systemic issues. | Local ownership is clear until a failure crosses business boundaries. |
| Lifecycle monitoring | One dashboard can cover the portfolio if intake is disciplined. | Central telemetry plus domain-level outcome metrics. | Visibility is fragmented unless standards are imposed. |
Use these tests to select a starting point
1. Assess AI and platform maturity
Low maturity favors centralization. If teams lack deployment, evaluation, security, or incident-response skills, a center of excellence can consolidate scarce expertise and establish repeatable patterns. Higher maturity supports federation when teams can independently use approved environments and still meet enterprise controls.
Look for evidence rather than job titles: a team is ready to operate a use case when it can version prompts and code, test against representative data, monitor quality and cost, roll back a release, respond to an incident, and document a material change.
2. Map the trust boundary
Risk rises sharply when an agent speaks directly to customers, changes records, approves transactions, sends messages, or executes actions in enterprise systems. Sensitive personal, financial, health, or confidential data also narrows the acceptable operating envelope. Keep design reviews, access approvals, evaluation thresholds, and release authority closer to the center until automated controls and proven runbooks exist.
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3. Account for regulation and data sensitivity
Regulated industries benefit from common policy interpretation, durable audit trails, retention rules, model inventories, and evidence that the same control applies across business units. Federation can still work, but the hub must make the compliant path easier than an improvised local path.
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Prototype skill is not production ownership. Ask each candidate spoke to name the product owner, data steward, on-call responder, security contact, rollback method, and budget owner. If any answer is missing, retain that responsibility centrally or pair the team with a platform specialist.
5. Balance speed against control with operating signals
A long central approval queue, repeated requests for similar tools, and business teams waiting weeks for access indicate that delivery should move outward. Conversely, duplicated vector stores, incompatible evaluations, unknown model versions, or inconsistent privacy reviews indicate that standards and platform capabilities need to be re-centralized.
What current evidence says
Financial-services data illustrates why the answer is not simply “decentralize for speed.” A 2024 McKinsey banking study reported that about 70% of banks and other institutions with highly centralized generative-AI operating models had progressed to production, compared with about 30% using a fully decentralized approach. This is an association in a financial-services sample, not proof that centralization alone caused the difference; funding, leadership, risk discipline, and technical capability may also explain the gap.
The same McKinsey study reported that more than 90% of institutions represented at its forum had established a centralized generative-AI function to some degree. That finding supports a central capability, but it does not imply that every use case should remain centrally delivered.
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McKinsey’s 2025 State of AI report describes selective centralization: risk and compliance and data governance are often fully centralized, while AI talent and adoption more often use hybrid or partially centralized arrangements. The pattern is consistent with a hub that owns controls and a distributed network that owns adoption.
A practical evolution path
Stage 1: Establish a central platform or center of excellence
Begin centrally when the portfolio is small, expertise is scarce, or risk is high. Give the initial team a clear charter and a finite service catalog rather than an open-ended mandate to “do AI.” The first deliverables should make safe delivery repeatable:
- Identity, role-based access, secrets management, and separate development, test, and production environments.
- Approved model access, data-classification rules, retention settings, and network controls.
- Evaluation methods for accuracy, grounding, harmful output, privacy, latency, and cost.
- An agent and model registry recording owner, purpose, data sources, version, risk tier, approvals, and retirement date.
- Central logging, audit evidence, incident severity definitions, escalation contacts, and rollback procedures.
- Reusable “golden paths” for common patterns such as retrieval-augmented generation, human approval, and read-only system integration.
Stage 2: Add controlled spokes
Invite business teams with a concrete product owner and a measurable use case. Keep central approval for high-risk data, external-facing behavior, write access to systems, and exceptions to policy. Let the spoke own workflow design, domain testing, user training, and adoption metrics inside the approved path.
Stage 3: Federate delivery through automation
Move routine releases outward when controls run automatically in the delivery pipeline. A spoke should be able to request an approved model, create a monitored endpoint, run required evaluations, obtain policy evidence, and roll back without a manual central handoff. The hub remains accountable for platform reliability, baseline security, registry integrity, and risk policy.
Stage 4: Govern by exception
At higher maturity, central reviewers focus on new risk classes, policy exceptions, material model changes, and systemic incidents. Business teams operate within published thresholds. Review the boundaries quarterly because a previously low-risk text assistant can become high-risk when new tools, data, or write actions are added.
Define decision rights before assigning teams
Publish a one-page responsibility matrix for every production use case. At minimum, name one accountable executive and separate these responsibilities:
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- Platform and architecture: central team owns environments, identity integration, approved services, reliability targets, and reference architectures.
- Policy and risk: central security, privacy, legal, and compliance functions set non-negotiable controls and risk appetite.
- Product outcome: the business spoke owns the user problem, process design, adoption, benefits, and domain acceptance criteria.
- Data stewardship: the domain owner approves data use, quality, lineage, and retention for its process.
- Operations: the spoke handles first-line triage and user communication; the hub handles platform defects, shared services, and cross-unit incidents.
- Release authority: define which risk tiers can self-release and which require central sign-off.
Use a simple RACI or equivalent, but do not stop at a diagram. Attach service-level targets, escalation paths, and an exception process that records who approved a deviation and when it expires.
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Implementation checklist
- Inventory the portfolio. Record every pilot and production system, its owner, model or agent version, data classes, users, connected tools, decision impact, and current monitoring.
- Classify risk. Create tiers based on customer exposure, sensitive data, autonomy, system write access, regulatory impact, and potential harm. Link each tier to required tests and approvals.
- Publish the paved road. Provide approved model endpoints, templates, evaluation harnesses, logging libraries, secrets handling, and rollback automation. A control that is difficult to use will be bypassed.
- Set intake and prioritization. Require a problem statement, expected outcome, data owner, risk tier, budget, and named operator before work enters a roadmap.
- Run representative evaluations. Test quality and failure modes with realistic data, adversarial prompts, privacy cases, latency, and cost limits. Store results with the model or agent version.
- Instrument production. Capture requests, responses or traces appropriate to privacy rules, model version, tool calls, latency, cost, user feedback, and policy events. Define retention and access for logs.
- Prove recovery. Exercise rollback, key revocation, model replacement, data-source disablement, and user notification before declaring the system operational.
- Delegate deliberately. Transfer a responsibility only when the receiving team has skills, runbooks, on-call coverage, budget, and authority to act.
- Review quarterly. Reassess risk, adoption, incidents, central-queue time, reuse, and business value; change the operating boundary as evidence changes.
Metrics that reveal whether the model is working
Measure both delivery flow and control quality. A central team that reports only the number of pilots can hide a serious queue; a decentralized network that reports only launch speed can hide operational debt.
- Lead time: elapsed time from an approved idea to a monitored production release.
- Central-queue wait: time spent waiting for architecture, security, data, or release decisions.
- Reuse: percentage of systems using approved shared components, evaluations, and platform services.
- Control coverage: percentage of production systems with current risk classification, registry records, logging, and tested rollback.
- Exceptions: number, age, owner, and expiry date of policy exceptions.
- Incidents: severity, time to detect, time to contain, recurrence, and whether the cause was model, data, platform, or process.
- Production adoption: active users, task completion, override rate, and sustained usage rather than launch count.
- Business outcomes: cycle-time reduction, quality improvement, revenue, loss avoidance, or another metric agreed with the process owner.
Common situations and the right response
Early adoption with few experts
Start centralized. Concentrate platform and safety skills, standardize access, and avoid having every business unit learn deployment and monitoring independently. Open a small number of spokes only after the center can support them with documented patterns.
High-risk or regulated customer-facing agents
Use centralized governance with controlled delivery. Keep approval of data access, tool permissions, evaluation thresholds, human-override design, and release criteria central. A business team can still own conversation design and user operations within those boundaries.
Several business units are beginning to build
Use a federated model before a central queue becomes the bottleneck. Offer self-service environments and reusable components, require registry and telemetry integration, and assign each spoke an accountable product and operations owner.
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Mature teams with reliable platform controls
Federate delivery and govern by exception. The hub should focus on platform reliability, identity, security baseline, architecture standards, registries, and risk appetite while spokes optimize workflows and adoption.
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Duplicated tools and inconsistent evaluations
Re-centralize standards and platform capabilities. Consolidate approved services, publish one evaluation approach for each risk class, migrate teams in stages, and retire redundant components with a dated plan. Do not remove a local tool until an equivalent supported path exists.
Failure modes to avoid
Calling a team “federated” without delegating authority
If every model choice, prompt change, or release still waits for central approval, the organization has a centralized queue with extra meetings. Delegate explicit decisions and automate the evidence required for low-risk changes.
Building a center of excellence that only consults
A central group that publishes principles but no usable platform leaves teams to solve identity, logging, evaluation, and rollback repeatedly. Fund platform engineering and measure adoption of the paved road.
Allowing local speed to outrun accountability
A pilot becomes production when people depend on it, even if the team still calls it an experiment. Require an owner, risk tier, monitoring, incident route, and retirement condition before broad rollout.
Keeping risk decisions separate from product decisions
Security and compliance controls must be designed into the workflow, not added after a business team has committed to a launch date. Pair risk specialists with product owners during discovery and evaluation.
Measuring activity instead of outcomes
More prompts, models, or pilots do not prove value. Tie each use case to a process metric and review whether users continue to rely on it after the initial launch.
Bottom line
Choose centralization first when you need to build capability, control risk, or make scarce expertise reusable. Choose federation when business teams can own production outcomes and automated guardrails make the safe path fast. In most large companies, the durable answer is selective centralization: keep platform, identity, security, standards, registries, and risk appetite in the hub, and place use-case delivery and adoption with accountable business spokes. Revisit that boundary quarterly as maturity, regulation, and AI capabilities change.
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