AI Operating Model
An AI operating model defines who owns outcomes, where technical capability sits, how shared controls are supplied, and how workflow teams adopt and improve AI in daily work. It should move accountability toward the business while keeping reusable expertise and governance available across the enterprise.
Key takeaways
- Most AI transformation offices are hired to fail by design.
- Capability gaps are usually decision gaps in disguise.
- Change resistance is a feature of the org, not a bug to bulldoze.
Decision brief
An AI operating model defines who owns outcomes, where technical capability sits, how shared controls are supplied, and how workflow teams adopt and improve AI in daily work. It should move accountability toward the business while keeping reusable expertise and governance available across the enterprise.
Decision criteria
- Outcome ownership sits with the line, not permanently with a central lab.
- Shared platforms and controls reduce duplication without becoming a bottleneck.
- Roles, incentives, skills, and decision rights change with the workflow.
Evidence to check
- Named business and technical owners for each production workflow.
- Time from approved use case to operating adoption.
- Capability transferred, processes retired, and exceptions resolved.
What I would do Monday
Take one production AI workflow and write a RACI for outcome, data, model, control, adoption, and exception handling. Resolve any cell owned only by “the AI team.”
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