The doctrine is domain-agnostic by design. It has been shaped against financial institutions first, because their decisions are audited the hardest — but the same seven stages apply anywhere capital, regulation, or reputation are on the line.
Every use case below shares the same shape: a material amount of capital, a decision-maker who will be asked to justify it later, and an AI or automated system somewhere in the reasoning chain that needs its own evidence trail, not just the human’s.
Build vs. buy vs. partner for an AI system with a material budget and operational blast radius.
Adopting or retraining a model that changes who gets approved, and at what price.
Valuation, synergy claims, and integration risk under a fixed close deadline.
Reassigning material budget across business units with competing sponsors.
Hedging, funding mix, and counterparty exposure decisions with model-driven inputs.
Multi-year commitments to a vendor whose model or algorithm you cannot fully audit.
Credit decisioning, fraud models, and capital models under direct supervisory scrutiny.
Underwriting and claims models where explainability is a regulatory requirement, not a preference.
AI-assisted allocation decisions that fiduciaries must be able to justify to their own clients.
If it involves material capital and a defensible-reasoning requirement, it likely fits.