Model Dependency Map: Locate, Limit, and Lose the Wrong AI Fast

Core insight: as AI moves from experiments into operations, business processes quietly become coupled to models—when a model drifts, a hidden network of decisions, automation, and customer touchpoints can fail together. The Model Dependency Map is a ten-minute habit that makes those couplings explicit: list the model, its primary consumers (human or agent), the decision class it influences, the observable impact band, and the immediate rollback handle. In this episode I give the exact one-line Map row, three enforcement patterns that prevent cascade (shadow-mode releases, one-click local kill-switch, impact‑band throttles), and two constrained AI patterns to auto-discover likely dependents from logs and contracts. You get a 7‑day pilot to map five models, run a shadow‑release for one, and one immediate action: add the Map row to your next high-impact model change memo. CTA: subscribe. Stay agentic.