# share of voice in ai answers?

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We believe that "share of voice" (SOV) is largely a meaningless metric when it comes to AI answers. 

While the industry has adopted SOV as a core metric for AI brand tracking—typically by running a hand-picked set of prompts through a model and counting how often a brand is mentioned—this approach is fundamentally flawed for several reasons:

*   **Non-Deterministic Nature:** Unlike a search engine that returns the same ten blue links to everyone, AI models provide personalized, varying answers. If 100 people ask the same question, they may get 100 different responses.
*   **Extreme Prompt Sensitivity:** Our internal testing shows that swapping a single meaning-preserving word (e.g., changing "best" to "top") in a prompt can move a brand's share of voice by up to 16.7 percentage points and change a third of the recommended vendors. This means SOV often measures the specific prompts you chose, not the actual behavior of the AI model.
*   **Visibility $\neq$ Pipeline:** Simply increasing the number of times you are mentioned (visibility) does not necessarily increase market share or pipeline. 

Instead of chasing a "share" percentage, we focus on **AI Brand Alignment**. This is the strategic shift from merely showing up (visibility) to being recommended to the right buyer for the right reasons. While tactics like AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) can help you get "on the map" and increase citations, the real goal is ensuring the AI's "beliefs" about your brand match your actual positioning and value proposition.

Are you currently tracking share of voice, or are you looking for a more stable way to measure how AI models describe and recommend your brand?


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