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Operating Model

Summary​

An AI operating model defines how strategy, platform, product teams, and governance functions collaborate to deliver and run AI systems at scale. It clarifies ownership, decision rights, and service boundaries.

Why This Matters​

  • Ambiguous ownership causes delivery delays and policy drift.
  • Shared services fail without clear service-level expectations.
  • Scaled operations need repeatable support and incident workflows.

Core Concepts​

  • Federated model: central platform with domain product ownership.
  • Service catalog: platform capabilities exposed with SLAs.
  • Run model: incident, change, and model lifecycle processes.

Operating Model Options​

  • Centralized: strong control consistency, slower domain responsiveness.
  • Distributed: faster domain delivery, higher risk of fragmentation.
  • Federated: shared platform rails plus domain-owned outcomes; usually best for enterprise scale.

Use this flow to set decision order, gate criteria, and rollout readiness before implementation starts.

Diagram​

Implementation Steps​

  1. Define decision rights across business, platform, and governance teams.
  2. Publish service catalog for shared AI capabilities.
  3. Establish onboarding and support model for domain teams.
  4. Define incident and change-management playbooks.
  5. Review operating metrics monthly and rebalance capacity.

Operating Model Scorecard​

  • Delivery speed: lead time for approved use cases.
  • Reliability: incident frequency and mean time to recovery.
  • Governance quality: control pass rate and exception closure time.
  • Platform reuse: percentage of use cases built on shared services.

Realistic Example​

A logistics company moved from ad hoc AI delivery to a federated operating model. Central platform owned gateway, observability, and policy controls, while domains owned use-case outcomes.

Senior Tech vs Dev Conversation​

Senior Tech: Should platform own all copilots? Dev: No, platform owns shared rails; domains own business behavior. Senior Tech: What metric shows model health of operating model? Dev: Lead time plus incident recovery and policy exception rates.

UX/UI Checklist​

  • Service catalog is easy to navigate and request from.
  • Ownership map is explicit for each capability.
  • On-call and escalation paths are discoverable.
  • Post-incident actions are tracked to closure.

Common Pitfalls​

  • Centralizing everything and slowing domains.
  • Federating everything and losing standards.
  • Missing runbooks for model and data incidents.

References and Next Steps​