AI Share of Voice Explained: Why a Low Number Might Be Fine

I was reading through a guide on AI visibility metrics from nenawow (https://nenawow.com/blog/ai-share-of-voice-explained) and caught something most people miss. The author points out that a low share of voice number is not automatically bad news. It depends entirely on what is driving it.

This stopped me. Because the instinct for most SMB owners is to see a metric, see it's low, and immediately want to fix it. Higher must be better. Right?

Not always. Let me walk through what AI share of voice actually measures, and why context matters more than the raw number.

What AI Share of Voice Actually Means

AI share of voice tracks how often your brand gets mentioned or recommended when people ask AI tools questions related to your market. If someone asks ChatGPT for CRM recommendations and your name comes up in the response, that counts. If Perplexity surfaces your competitor but not you, that counts against you.

The math is straightforward. Take all the AI responses for a set of queries in your category. Count how many times each brand appears. Your share is your mentions divided by total mentions across all brands.

Simple enough. But here's where it gets tricky.

When a Low Number Is Perfectly Fine

Let's say you run a niche B2B SaaS company doing $3M ARR in a specific vertical. You have maybe 8 real competitors. Your AI share of voice sits at 12%.

Is that bad?

Maybe not. If your category is small and fragmented, 12% could be second or third place. If the market leader has 40% and everyone else scrapes by with single digits, your 12% looks pretty good.

Or consider this scenario. You sell into enterprise healthcare systems. Your buyers are not asking ChatGPT for vendor recommendations. They're running formal RFPs, checking Gartner reports, calling their network. Your AI share of voice could be near zero and it would not matter one bit for your pipeline.

The number only matters if the channel matters.

When a Low Number Is Actually Bad

Now let's flip it. You sell DTC supplements or run a local service business. People absolutely ask AI tools for recommendations. "Best protein powder for muscle gain" or "reliable plumber in Austin." If your share of voice is low here, you have a real problem.

The buyers in these markets use AI as a discovery tool. They trust the answers. If you are not showing up, you are losing deals to competitors who are.

Same metric. Different context. Completely different urgency.

What Drives Your Share of Voice

If you decide your number matters and needs work, you need to understand what pushes it up or down.

AI models train on the open web. They pull from articles, reviews, forum discussions, product pages, and comparison content. If your brand has more high quality mentions across these sources, you show up more often in AI responses.

This is why content that ranks well in traditional search also tends to perform well in AI. The underlying sources overlap. SEO basics still apply. Clear product pages. Helpful articles. Real customer reviews on third party sites.

But there is a twist. AI tools also weight recency and authority differently than Google. A mention in a detailed Reddit thread from six months ago might carry more weight than a press release from your company last week. AI models value what real people say about you over what you say about yourself.

How to Improve Without Gaming It

You cannot pay your way into AI recommendations. These models do not have ad slots. You earn visibility by being talked about in places the models trust.

Start with three things:

Get mentioned in roundups and comparison content. Find the articles that already rank for queries like "best [your category] software" or "top [your category] tools." Reach out to the authors. Offer a genuine perspective or case study. A single mention in a strong piece can move your share of voice.
Build presence where your buyers talk. Industry forums, Reddit communities, LinkedIn threads. Participate honestly. Answer questions. When real people discuss your brand naturally, those signals get picked up.
Make your own content reference worthy. Publish original data, case studies, or genuinely useful guides. Other sites link to and cite strong content. Those citations become training data.

Do not chase the metric directly. Chase the underlying signals. The number follows.

The One Question to Ask First

Before you do anything, answer this. Do my actual buyers use AI tools to find vendors like me?

If yes, then track your share of voice and work to improve it. If no, then you can safely ignore this metric and focus on channels that actually move revenue.

Most SMBs I talk to have never asked this question. They see a new metric and assume they need to optimize it. That is how you end up with a full time SEO person chasing AI visibility for a company whose buyers never touch ChatGPT.

Context first. Optimization second.

What We See at Helix

We run GTM automation for revenue share clients. When we audit their digital presence, AI share of voice comes up. But we treat it as one signal among many. Sometimes it points to a real gap. Sometimes it is just noise.

The clients who win are the ones who understand their own buyer journey. They know which channels matter. They invest accordingly.

If you want to see where you stand, check your AI visibility for a handful of buyer queries. See who shows up. Then decide if closing that gap is worth your time.

The number tells you something. But you have to know what question you are asking first.