I would evaluate the Copilot across three dimensions:
Product Value: Is the Copilot helping users move from risk identification to informed action?
AI Quality: Are the explanations accurate, grounded in evidence, and appropriately uncertain?
Guardrails: Are we improving speed without introducing unsafe recommendations, poor trust, or excessive system cost?
Risk-to-Action Completion Rate: The purpose of the Copilot is not simply to generate supplier summaries. It should help users move from identifying a potential risk to making an informed decision or taking an appropriate next step.
Time to insight - How long does it take a user to understand the main supplier-risk drivers?
Evidence inspection rate - How often do users review the evidence behind an AI explanation?
Recommendation usefulness - How often are suggested actions accepted, adapted, or dismissed?
Investigation completion rate - How many started supplier investigations reach a documented outcome?
Repeat usage - Do users return to the Copilot for additional supplier reviews?
Evidence-grounding accuracy - Are the AI's claims supported by the supplier data provided?
Unsupported claim rate - How often does the model introduce information that is not present in the source context?
Structured-output compliance - Does the model consistently return the expected format?
Missing-data handling accuracy - Does the system clearly identify when information is unavailable rather than guessing?
Guardrails: