Ask yourself one question:

When someone asks ChatGPT, Perplexity, or Gemini to recommend a solution in your category, does your brand show up?

Most marketers have no idea. They’re tracking rankings, impressions, and click-through rates while a completely new discovery channel is forming underneath them.

In AI search, there is no page two. If the model doesn’t mention you, you don’t exist.

Visibility is no longer about being ranked. It’s about being retrieved.

That’s what “Share of Model” measures: how often AI recommends your brand when users ask questions in your space. It’s the metric that will define competitive advantage in 2026 — and almost nobody is tracking it yet.

Traditional Share of Voice measured how much of the conversation you owned in media or search results. You could be on page three of Google and still technically “visible.”

AI doesn’t work that way.

When a user asks an LLM for a recommendation, the model generates a short list. Three to five brands, maybe. Everyone else is invisible. Not buried — absent.

Research from INSEAD found that many brands with high consumer awareness are surprisingly weak in AI responses. Strong with humans. Invisible to machines. And the variance between models is massive — one brand showed 24% share on Meta’s Llama but less than 1% on Google’s Gemini.

Your Google ranking doesn’t predict your AI visibility. They’re two different games now.

You can have strong brand awareness and still be invisible to AI.

1. Audit your current AI visibility

You can’t improve what you don’t measure. And right now, most brands are measuring nothing.

Start with 50–100 prompts that reflect real customer queries in your category. Run them across ChatGPT, Claude, Gemini, and Perplexity. Record whether your brand appears, where you rank in the list, and how you’re described.

Critical: run each query multiple times. Research shows only 30% of brands remain consistently visible between successive AI responses. One mention means nothing. Consistent presence is what counts.

✅ Cheat Code: Build a simple spreadsheet: prompts in rows, AI platforms in columns. Track mention (yes/no), position, and sentiment. Run monthly. That’s your Share of Model baseline — and you’ll immediately see where competitors are showing up instead of you.

2. Feed the models what they need to recommend you

AI models recommend brands they can verify. That means your content needs to be the kind of material models trust: original research, structured data, expert-cited content, and pages that answer specific questions with specific claims.

Models cite what they can verify. Original data, expert sources, and fresh updates win. Generic content gets ignored.

But here’s what most people miss: third-party sources matter more than your own site. If authoritative comparison sites, analyst reports, and industry roundups mention your brand — the models pick that up. Getting mentioned on the pages AI already cites is the fastest path to Share of Model growth.

✅ Cheat Code: Identify the top 10 URLs that AI models cite in your category (you’ll see them in your audit). Get your brand mentioned on those pages — through partnerships, contributed content, product reviews, or directory listings. This is the new link building.

3. Track competitively and act on gaps

Share of Model is only useful as a competitive metric. Your absolute mention count matters less than whether you’re gaining or losing ground relative to competitors.

Map your visibility across different prompt types. You might dominate “enterprise solutions” queries while being invisible in “small business tools” prompts. These segment-specific patterns reveal where you have leverage and where you’re losing deals before a human even enters the picture.

✅ Cheat Code: Build a “lost prompt” list — queries where competitors are mentioned and you’re absent. That’s your content and authority roadmap for the next quarter. Each lost prompt is a deal you never got the chance to compete for.

The Share of Model Tracking System

Profound / Peec → monitor AI citations and brand mentions across ChatGPT, Perplexity, Gemini, Claude

Ahrefs / Semrush → track which source URLs AI models are citing in your category

SparkToro → understand where your audience’s attention lives (and where models pull from)

Google Search Console → measure branded search lift as a proxy for AI-driven awareness

Notion / Google Sheets → “lost prompt” tracker and monthly audit log

👉 Result: You know exactly how often AI recommends you, how that compares to competitors, and which content gaps are costing you invisible deals. Then you close them.

Screenshot this. Run it quarterly.

☐     50+ prompts tested across ChatGPT, Claude, Gemini, Perplexity

☐     Each prompt run 3+ times to test consistency

☐     Brand mention rate tracked per platform

☐     Competitor mention rate tracked per platform

☐     Sentiment logged (positive / neutral / negative / inaccurate)

☐     Top-cited source URLs identified per category

☐     “Lost prompt” list built with content plan to close gaps

☐     Branded search trend tracked month-over-month as proxy metric

If you’re not doing this, your competitors might be. And in AI search, you won’t see them overtake you — because there’s no ranking page to check.

Marketing measurement was built for a world where people click. That world is shrinking.

The brands that win in 2026 won’t just track traffic and rankings. They’ll track how often AI recommends them — and systematically build the content, authority, and presence that makes recommendations inevitable.

Rankings tell you where you stand in search. Share of Model tells you whether you exist in the conversation.

Send this to your CMO. If this isn’t on your dashboard yet, you’re already behind.