The AI visibility score / index

Measuring AI visibility produces several distinct dimensions — presence, share of voice, recommendation, citation authority, accuracy, sentiment. That’s rich, but it’s a lot to watch at once, and hard to report up or trend at a glance. A composite score solves that by combining the dimensions into one number that summarizes your overall standing, so you have a single headline to track over time and compare against competitors.

A well-built index weights the dimensions by how much they matter to outcomes. Being recommended, for example, is more decisive than simply being mentioned, so it typically carries more weight than raw presence. The exact composition matters less than the principle: a credible score reflects the things that actually determine whether you win the AI answer, not just whichever metrics are easiest to count. And because it’s computed against your question set, the score inherits the quality of that set — another reason the question set is foundational.

A single index is genuinely useful for three things. It gives you a headline to track — is the number moving up or down over time? It gives you a benchmark — how do you compare to competitors, and to what “good” looks like in your category? And it gives you something reportable — a clear figure for leadership that doesn’t require explaining six dimensions at once. For watching the trend and communicating it, the score earns its place.

But a score is a summary, and summaries hide detail. A flat or rising index can mask a specific problem — say, a falling recommendation rate offset by rising mentions — and the number alone never tells you what to do. Acting on AI visibility always requires drilling from the score into the dimensions and the specific questions beneath it: which questions you’re absent from, where a competitor is out-recommending you, what inaccuracy is dragging you down. Treat the score as the top of a funnel of detail, not the whole picture.