Research · AI Strategy & Adoption

AI Operating Models

An AI operating model defines who decides, who builds, who pays and who is accountable for AI across the organization. Getting it wrong leads either to bottlenecks or to uncontrolled sprawl.

Common models

• Centralized: a single AI team or center of excellence delivers most work. Strong on standards and control, but can become a bottleneck.
• Federated (hub and spoke): a central team provides platforms, standards and governance while business units build their own use cases.
• Decentralized: business units act independently. Fast, but prone to duplication and inconsistent risk management.

What must be decided

• Which capabilities stay central, such as platforms, security, model approval and vendor contracts.
• How use cases are proposed, approved and funded.
• Who owns each AI system in production and its outcomes.
• How risk, legal, privacy and security teams participate without slowing everything down.
• How AI capabilities relate to existing data, IT and digital teams.

Why it matters

AI now arrives through many channels: internal builds, SaaS features, employee-adopted tools and vendor services. Without a clear operating model, organizations end up with overlapping tools, inconsistent controls and no single view of what AI is in use or what it costs.

Common pitfalls

• A center of excellence with a mandate but no budget or authority.
• Governance that reviews everything the same way, regardless of risk.
• No owner for AI features switched on inside purchased software.
• Treating the operating model as a one-time reorganization.

How to get started

• Map where AI decisions and spending happen today.
• Choose a model that fits your size and risk profile, often federated.
• Define decision rights for approval, funding and retirement of AI systems.
• Revisit the model as adoption grows.