AI-Powered Operating Model Playbook (AI-Native TOM & Use-Case Prioritization)
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Redesign roles, decision rights, and workflows around AI capabilities, and separate the AI use cases worth funding from the demo-ware, using a value/feasibility/risk scoring method.
How is this different from a standard AI use-case prioritization exercise?
Most prioritization exercises stop at scoring use cases on value and feasibility. This playbook adds risk as a formal gating dimension and pairs every prioritized use case with an explicit role redesign — without the role redesign, even a well-scored use case tends to stall at the pilot stage.
Do we need a dedicated AI governance function before we start?
No, but you need governance participation — legal, risk, or compliance — in the design step before any use case moves to production build. A small cross-functional governance checklist is enough to start; a dedicated function can follow once volume justifies it.
How do we decide the human-in-the-loop threshold for a given use case?
Set it based on the cost of an AI error and the reversibility of the decision, not on a generic confidence-score cutoff. A $50 pricing error and a $500,000 credit decision warrant very different thresholds even if the underlying model's confidence score looks similar.
What happens to roles that are eliminated by the exception-handler pattern?
Plan redeployment as part of the business case, not as an afterthought — the Alderbrook example redeployed reduced headcount into a new function funded by the productivity gain itself, which is a far easier conversation than a reduction announced without a redeployment plan.
How long before a prioritized use case should reach production?
Six to twelve weeks for a well-scoped use case with existing data infrastructure. If a use case is tracking past twelve weeks without a production date, revisit its feasibility score — it was probably scored optimistically at intake.
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