A practical framework to select, run, and evaluate AI pilots using a value hypothesis, risk assessment, data readiness checklist, governance guardrails, and scaling criteria.
How to select, run, evaluate, and scale AI pilots — a practical playbook. AI pilots should be short learning loops that answer a few clear questions, not mini-production projects with vague promises. Use a tight hypothesis, fast measurement, and explicit guardrails. This guide walks you through four decisions you must...
Governance Guardrails — practical rules to reduce risk during pilots. Pilots inevitably explore unknowns. Guardrails are lightweight rules that keep experiments contained and protect people, data, and trust. They should be enforceable, simple, and matched to risk. Core guardrails. Human-in-loop requirement: For...