AI Evaluation Evidence Pack — Complete Edition

Move beyond asking questions. Build a defensible AI project decision.

AI evaluation results can look convincing without answering the questions that matter most. Is the system being measured against the right criteria? Can the results be trusted? Do they represent real-world use? And what decision does the evidence actually support?

The AI Evaluation Evidence Pack — Complete Edition is an 18-page working toolkit designed to help cross-functional leaders examine AI evaluation evidence, identify gaps, and turn findings into documented decisions.

You don't need to conduct the technical evaluation yourself to ask whether the evidence is sufficient for the decision at hand.

What's inside:

• Five Questions: A framework for examining what is being measured, how it was measured, whether the result can be trusted, whether it represents the real use case, and what decision the evidence supports.

• Six Key Areas to Explore: Deeper questions covering Construct & Definition, Data & Sample, Study & Evaluation, Analysis & Statistics, Validity & Limitations, and Generalizability & Use Case.

• Six Role-Specific Guides: Tailored questions, success tips, common traps, and space to identify evidence needs and decisions for Product / Program, Tech Project Management, Data / Analytics, Research / Insights, Operations / Chief of Staff, and Leadership / Stakeholders.

• The Learning Loop: A step-by-step guide and companion worksheet covering eight stages of building, evaluating, deploying, and improving AI systems.

• Human Evaluation & Inter-Rater Reliability: A practical guide and worksheet for designing, calibrating, testing, and improving human evaluation processes.

• Validity & Real-World Fit: A guide and worksheet for assessing what evaluation results support, examining samples and failure modes, and determining how far findings can reasonably extend to real-world use.

• A Worked Example: See how a fictional AI billing-support team examines evidence gaps, chooses a decision, sets guardrails, and assigns follow-up actions.

• The Decision Record: A reusable worksheet to document the claim, evidence reviewed, unresolved gaps, decision, next action, owner, and follow-up date.

Who it's for:

• Product, program, and technical project managers

• Data, analytics, and research leads

• Chiefs of Staff and operations leaders

• Business leaders and stakeholders accountable for AI success

How to use it:

Use the pack to prepare for an evaluation review, guide a working session with your team, investigate gaps in reported results, or document the rationale behind a project decision. Work through the relevant guides as needed, then use the Decision Record to capture what the evidence supports, what remains unresolved, and who owns the next step.

This is a practical reference you can return to throughout an AI project—from the first review through pilot decisions, deployment considerations, and follow-up.

Better questions. Smarter decisions.