As artificial intelligence begins to reshape how people discover, learn, and evaluate information online, traditional SEO metrics alone no longer tell the full story.
Marketers are realizing that visibility in AI answers—not just on search engine results pages—will soon define brand reach.
To fill this measurement gap, SEMrush has introduced a new set of AI metrics that track where, how, and how often brands appear within AI-generated responses. These measurements—centered around the AI Visibility Score—represent one of the first structured attempts to quantify performance in the era of conversational search.
This chapter explains what those metrics mean, how SEMrush calculates them, and how you can use them to monitor and grow your brand's presence in AI-driven discovery.
Why AI Visibility Matters
When someone types a question into ChatGPT, Gemini, Perplexity, or Copilot, they're no longer browsing ten blue links. They're getting answers.
The brand that gets cited—or even mentioned—inside those answers has earned the new form of digital shelf space.
The ones that don't appear are effectively invisible.
AI visibility measures how frequently a brand is:
- Mentioned in AI-generated responses
- Cited as a source for information
- Associated with key topics or industries
In other words: It quantifies your brand's footprint inside the AI layer of the internet.
Inside SEMrush's AI Visibility Toolkit
SEMrush's AI metrics suite is designed to capture how brands perform within LLM-powered search environments. It tracks not only your keyword rankings, but also your presence in AI answers, including where your brand appears, how often, and under which topics.
The primary components are:
- AI Visibility Score (0–100) – The overall measure of how visible a brand is in AI-generated answers.
- Mentions – The number of AI prompts where your brand is referenced by name.
- Monthly Audience – The estimated search audience behind those prompts.
- Cited Pages – URLs from your site that are directly cited by AI assistants.
- Share of Voice (SOV) – Your brand's share of mentions compared to competitors.
- Prompt-Level Tracking – The average position or context in which your brand appears.
Together, these data points form a snapshot of how machines talk about your company.
How SEMrush Calculates AI Visibility
SEMrush's AI Visibility Score is based on a proprietary scoring model that measures brand frequency and prominence across a wide range of AI platforms and queries.
Step 1: Data Collection
SEMrush uses a library of prompts that simulate real user questions across industries and topics.
It then runs those prompts through leading AI systems such as ChatGPT, Gemini, Copilot, and Perplexity to see which brands appear in responses.
Each mention, citation, or link from these generated answers is logged.
Step 2: Mention & Citation Weighting
Every time a brand is named or cited, SEMrush assigns a weighted value based on:
- Frequency of appearance
- Position within the answer (first mention = higher value)
- Whether the brand was cited with a link or simply referenced
- The estimated monthly audience for that topic
A mention in a highly searched topic carries more weight than one in a niche query.
Step 3: Scoring and Normalization
The AI Visibility Score (0–100) represents a normalized value that combines:
- Number of AI mentions
- Quality and context of those mentions
- Estimated audience reach
- Citation density (how often your pages are sourced)
It functions similarly to SEO's "visibility percentage," which used ranking positions and click-through probability—but instead of SERPs, it measures answer presence.
For example:
A brand cited in 40% of AI responses about "workflow automation tools" with multiple linked references would score higher than one mentioned once or not cited at all.
Step 4: Share of Voice (AI Context)
Finally, SEMrush calculates a competitive visibility ratio by comparing your mentions and citations to those of other brands in your category.
This shows how you perform relative to peers inside AI-generated content—not just search results.
"AI visibility is becoming the new search share. It reflects how often your brand is surfaced by generative AI tools when people seek answers, not just when they search on Google."
How AI Metrics Differ from Traditional SEO
Traditional SEO visibility tracks keyword rankings and organic impressions.
AI visibility, on the other hand, measures brand presence in machine-generated answers—a fundamentally different environment.
| SEO Metric | AI Metric | Core Difference |
|---|---|---|
| Keyword rankings | Mentions & citations | LLMs don't use keywords—they generate answers. |
| SERP impressions | Monthly audience | Based on search intent, not just volume. |
| Click-through rate | Answer position | Visibility within AI responses. |
| Backlinks | Cited pages | AI citations as digital authority signals. |
In short: SEO measures discoverability through search.
AI metrics measure discoverability through intelligence.
How to Use AI Visibility Metrics Strategically
1. Audit Your Brand's AI Presence
Start by identifying where your brand is already being mentioned or cited in AI-generated answers.
You may find that competitors dominate certain topics, even if you rank higher in search.
2. Benchmark Against Competitors
Use Share of Voice data to understand how your brand compares within your industry's AI ecosystem.
Who's winning the "AI shelf space"? Who's missing?
3. Identify Source Opportunities
Review which pages are being cited. If none are, it's a signal to improve:
- Schema markup
- Clear definitions and entity references
- FAQ and "how it works" content
- Authoritative insights or data tables
AI systems favor structured, factual, and unambiguous content—the kind your SEO team may already be building.
4. Optimize for Citation Likelihood
Treat AI visibility as a natural extension of AEO.
That means writing content that answers questions precisely, uses schema, and cites credible evidence.
5. Integrate AI Metrics with Traditional Reporting
Include AI Visibility and Mentions alongside organic traffic, impressions, and conversions in your analytics dashboards.
Over time, this helps reveal whether LLMs are reinforcing your brand—or forgetting it.
Limitations & Considerations
AI metrics are still early-stage.
They provide directional insights—not absolute measurements.
Things to keep in mind:
- AI models frequently update, so visibility fluctuates.
- Prompt sampling can't represent every user query.
- Mentions ≠ clicks or conversions.
- Some AI systems summarize data without explicit citations.
Use these scores as signals to shape strategy, not as a definitive truth.
Practical Checklist: Improving Your AI Visibility
- ✅Claim your brand's entity definitions (Organization, Product, Person schema).
- ✅Publish definitive, answer-ready explanations about your solutions.
- ✅Add FAQs to every key page.
- ✅Cite reputable third-party data.
- ✅Refresh content frequently—LLMs learn from recency and consensus.
- ✅Track your SEMrush AI Visibility Score monthly.
- ✅Cross-reference AI mentions with web analytics to validate awareness lift.