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3.3 · Reformulate: A Better Proposal, Not Automatic Compromise
Goal: conserve useful objectives while modifying the mechanism that makes a plan inadequate.
A false business dilemma
An assistant claims a service desk must either hire more people or accept worse service. But perhaps calls repeat because the website is unclear; perhaps demand clusters in certain hours; perhaps complex cases need better triage. None of those alternatives is automatically correct. They reveal variables omitted by the initial two-choice framing.
A serious reformulation specifies what remains valid, what is rejected, what new dependency emerges, the direct and indirect costs, and when the alternative would be worse. If an option violates safety or law, “meeting halfway” does not rehabilitate it.
Keep the legitimate objective [X] and examine the demonstrated tension [Y]. Propose three distinct reformulations. For each, identify what is preserved, what changes, the key assumptions, possible harms, a reversible first step, and a disconfirming test. Do not invent costs or success rates.
Build something testable
Translate an AI recommendation into an operational specification: inputs, conditional rules, exceptions, outputs, metrics and triggers for human review. If a coding capability is available, generate a small script and test it with normal and boundary cases. The fact that the assistant can write code does not mean it implemented your intended process.
Exercise: revisit a store with too many inventory items for its premises. Compare stock reduction, order-on-demand and supplier-based replenishment. State what each retains and what would make it unwise. Deliverable: a decision matrix grounded in evidence rather than enthusiasm.
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