You’re evaluating a new AI visibility tool for your team. The vendor provides a demo showing impressive-looking visibility scores but can’t explain how many prompts they run, how frequently they test, or how their scoring methodology works. How should you proceed?

The correct answer is to reject the tool and prioritize those that can explain their methodology. Without understanding how the AI visibility scores are calculated (e.g., prompt volume, testing frequency, scoring logic), the data is opaque and unreliable. You cannot trust or act upon metrics if their underlying methodology is a black box. Adopting it without this transparency would be a leap of faith, and supplementing with manual testing wouldn't validate a fundamentally unknown process. While AI responses can be inconsistent, dismissing all measurement is an oversimplification; the goal is to find reliable measurement tools, not abandon measurement entirely.