Share of Voice in AI search: a primer

Share of Voice (SoV) in AI search is the share of relevant AI answers in which your brand is recommended, compared with the competitors named alongside you. It is the single clearest way to turn the fuzzy question - "how visible are we in AI?" - into a number you can track over time.

If AEO is the practice, Share of Voice is the scoreboard.

Why you need a dedicated metric

AI answers do not come with a leaderboard. When ChatGPT, Claude, Gemini or Perplexity recommend a few brands in a paragraph, there is no built-in "you ranked 2nd of 8" readout. Without a metric, teams fall back on anecdotes: someone asked the assistant once, saw the brand, and called it a win.

Share of Voice fixes that by making three things explicit:

• How often you appear across the queries that matter.
• Against whom - the specific competitor set the AI keeps naming.
• How that changes week over week and engine by engine.

How Share of Voice is measured

The mechanics are straightforward once you treat it as a measurement problem rather than a one-off check.

1. Define the query set

Start with realistic, buyer-intent prompts a real customer would ask - comparisons, "best X for Y", category recommendations. Unbiased, varied phrasing matters: a handful of cherry-picked questions will flatter you. A broad, representative set tells the truth.

2. Run them across the engines

The same prompts go to ChatGPT, Claude, Gemini and Perplexity, because they answer differently. SoV is only meaningful when it is consistent across a stable set of prompts and engines, repeated on a schedule.

3. Capture appearances and rivals

For each answer, record whether your brand was recommended, in what position, and which competitors showed up with you. That competitor set is not something you guess - it is whoever the AI actually names.

4. Compute the share

At its simplest:

Share of Voice = your brand's recommendations ÷ total brand recommendations across the answer set.

You can slice the same data further - Top-1 presence (how often you are the lead recommendation), Top-3 presence, and SoV per engine or per query theme.

Reading the number well

A Share of Voice figure is most useful in context. A few habits keep it honest:

• Track the trend, not the snapshot. AI answers drift, so a single reading is noisy. The slope over weeks is the signal.
• Segment by engine. A healthy blended SoV can hide the fact that you are strong on one assistant and absent on another.
• Watch the competitor mix. A rising rival's SoV is an early warning, even if yours is steady.
• Pair it with citations. If your SoV is low, the sources the assistants cite for your category often explain why - you may be recommended but not cited, or absent from the domains that feed the answers.

Turning Share of Voice into action

The point of the metric is not the dashboard. It is deciding what to do next. A falling SoV against a specific competitor on a specific query theme is a concrete, prioritisable problem: it points to the content, the citations, or the positioning that needs work.

Used this way, Share of Voice becomes the bridge between "we should care about AI search" and a plan leadership can get behind - a defensible KPI for how your brand shows up in the answers your buyers now trust.