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Leading AI at Work: Governance, Rollout and Practice / Unit 3   LAI 067 review, 2 to 3 minutes

A Triage Score Near a Gold Thing

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The idea

A worked example, constructed for this course, uses the book's risk prompt on a feature that sits close to a consequential decision.

In practice

An insurer's claims team wants a triage model that scores each new claim for complexity, so that simple claims move faster. The analyst, Dev, gives the approved AI tool the Claim Decision's Gold Pack and a three-sentence description of the feature, and asks the book's four questions, using only the Pack and the description. The tool answers that the score could influence which handler receives the claim and how quickly it is decided. It names a risk the team had not discussed: if handlers see the score, a low complexity score could be read as a low-value claim. It suggests using the Pack's definition of the Claim Decision as a guardrail: the decision's outcome and reasons must come from the policy clauses, never from the score. Its questions before deploying include who sees the score, whether a handler can override the routing, and how the team will check scores across different kinds of claimant. Dev reviews the draft. He removes one suggested risk about fraud, which the description never mentioned, and keeps the rest. The team decides that the score will fill a Claim Assessment field that routes work, and will not appear on the decision screen.

Where people slip

The tool's draft was useful because it was framed by the Pack, and it still needed a person to remove what it invented. The team's final design kept the score away from the Gold thing itself, which matches the book's insurance playbook: triage fills the assessment, and the decision stays governed by policy.

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