AI Procurement Needs Security Review First
Signing an AI vendor contract before security review means the risk decision happens after the money has already moved.
AI Explainability Needs an Audience
An explanation that satisfies an engineer will not satisfy a regulator or an affected customer. Explainability has to be built for the person asking.
AI Risk Tiering Needs Consistent Criteria
Risk tiers only work when every team applies the same criteria. Inconsistent tiering lets high-impact systems slip through as “low risk.”
Third-Party AI Tools Need a Vetting Process
Vendor AI claims are marketing until verified. A repeatable vetting process is what turns a sales pitch into an informed procurement decision.
AI Use Case Registers Need a Single Source
Scattered spreadsheets cannot govern AI. A single, maintained use case register is what makes an AI policy enforceable across a company.
AI Policy Needs a Decision Owner
An AI policy only works when one named owner can approve, reject, and log decisions. This guide sets out how to assign that role and keep it accountable.
AI Model Retirement Needs a Decommission Checklist
AI model retirement is safe when dependencies, data retention, and user communication are checked before the model is switched off, not discovered afterward.
AI Vendor Switching Needs a Portability Plan
AI vendor switching is manageable when prompts, evaluation data, and integration code are kept portable from the start, rather than tightly bound to one provider’s interface.
AI Scaling Needs a Cost and Quality Tradeoff
AI scaling decisions hold up when the cost and quality tradeoff is measured explicitly, instead of assuming that more usage automatically justifies more spend.
Human Review Loops Need a Bounded Queue
Human review loops for AI output work when the queue has a bounded size, a defined service time, and clear escalation, instead of growing until nobody trusts it.