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TL;DR: The AI Routing Policy
You moved your routine AI work to a cheaper model, so you think the cost question is handled; however, often, that is not the case. The decision lives in one person’s head and produces nothing that the person accountable for the invoices can read. Worse, it is an architectural choice nobody documented. The AI Routing Policy is the missing artifact of Stage 2 of the Delegation Lifecycle: it records which execution path, from a cheaper model to a frontier model to plain code, handles each class of work, what counts as good enough output to meet the AI Definition of Done, and who owns the call. The skill it needs to work is one you already have: You write acceptance criteria.
Thesis : An AI routing policy is not about picking a cheaper model at the moment of executing an AI task. It is a written, repeatable team decision that assigns each task class to the cheapest sufficient execution path: a model, human review, deterministic code, or no automation. Paired with a minimal routing log, it creates the spend-by-task-class record that your finance team will eventually request. You can draft the first three lines in twenty minutes.
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🇩🇪 Zur deutschsprachigen Version des Artikels: Wenn Sie Akzeptanzkriterien formulieren können, können Sie auch eine KI-Routing-Richtlinie erstellen.
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The Question Nobody Can Answer
Not every company has a board, but every company has a person in the chief financial officer seat: a controller, a chief accountant, or the owner who signs the invoices. At some point, that person looks at the AI line on the budget and asks a simple question: What does this spend buy, and how would we know it creates a return on...