A practical framework to select, run, and evaluate AI pilots using a value hypothesis, risk assessment, data readiness checklist, governance guardrails, and scaling criteria.
Begin with a practical hunger: capture reliable AI value without wasting time. Most organizations try an AI pilot because a vendor demo looked impressive or because leadership wants to 'be doing AI.' The real hunger is different: find small, testable AI experiments that deliver clear, measurable business value while...
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...
Data Readiness Checklist for AI Pilots. Short, actionable items to confirm you can run a timeboxed pilot. Data sources identified: List each source and owner. Access path: Confirm where data will be extracted from and who grants access. Sample availability: Can you get a representative sample (weeks of data) within...
Value Hypothesis Worksheet. Use this short template to craft a testable hypothesis that ties AI to a measurable outcome. Pilot name: ________________________ Business outcome we care about (in plain language): Example: Reduce average manual review time for invoices. Answer: ________________________ Value hypothesis...
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...