Designing an AI Operating Model
Deploying an AI application introduces responsibilities that continue long after the first successful demonstration. Someone needs to maintain integrations, monitor output quality, manage costs, and respond when the system fails.
An AI operating model defines how that work gets organized. It connects technical delivery with business ownership, funding, and governance so teams understand who makes decisions and who remains accountable.
Centralized, Embedded, or Federated?
A centralized model places AI expertise, platforms, and delivery within one team. This can help establish consistent engineering practices and make good use of limited specialist resources. However, growing demand can create a queue that slows delivery.
An embedded model places AI specialists directly within business units. Working close to users helps developers understand workflow requirements and respond to local priorities. The challenge is maintaining shared standards and avoiding duplicated infrastructure.
A federated model combines a central hub with delivery teams inside business units. The hub manages shared capabilities and standards, while local teams develop solutions for their own workflows. Clear decision rights help prevent confusion between those responsibilities.
Define Ownership Before Deployment
A working system needs an identified owner. Teams should establish who approves changes, maintains dependencies, reviews performance, and handles incidents.
For an AI assistant, that might include responsibility for updating its knowledge sources, evaluating retrieval quality, and deciding when human review is required. For an automated workflow, it also means knowing who investigates failed executions or incorrect outputs.
Documenting those responsibilities makes ongoing support easier to coordinate.
Connect Funding to Responsibility
AI costs can span shared infrastructure and individual applications. Model usage, hosting, storage, and maintenance all need an agreed funding arrangement.
One approach is to fund shared platforms centrally while business units fund their own applications. Whatever arrangement an organization chooses, teams need visibility into costs and authority to manage the resources they use.
Review the Model as Adoption Grows
The structure that supports a few pilots may need to change as more teams adopt AI. A centralized team can gradually distribute delivery responsibilities as departments develop the necessary skills.
Review delivery speed, quality, cost, and risk regularly. Those measures help reveal whether teams need more autonomy, stronger shared support, or clearer standards.
My full article, “Designing an AI Operating Model,” explores how to choose an approach that fits your organization and adapt it as your capabilities grow.
Comments
Join the discussionNo comments yet. Be the first to comment.
