Originally published by the Forbes Communications Council, this article was written by Jeremy Fields, vice president of corporate development at Newsmatics, which owns EIN Presswire.
In my previous article on generative engine optimization (GEO), I explored how brands can structure content to get cited by large language models (LLMs) such as ChatGPT, Gemini and Claude. The takeaway was simple: visibility is no longer just about ranking on search engines; it’s about being referenced in AI-generated answers.
But as I’ve continued to study how AI systems surface and synthesize information, another concept has become increasingly important: share of model.
If share of voice defined the media landscape of the past, I believe share of model will quickly become the defining metric of the AI era.
Share Of Voice: The Legacy Visibility Metric
For decades, share of voice (SOV) has been a foundational KPI in marketing and public relations. It measures how much attention your brand commands compared to competitors across channels such as:
• Search visibility
• Social media
• Earned media
• Paid media
The underlying idea is straightforward: The more people hear about you, the more likely they are to trust, remember and ultimately choose your brand. This is why companies invest heavily in campaigns, press coverage and content distribution. Visibility drives familiarity, and familiarity drives demand.
And to be clear, share of voice still matters. In fact, consistent publishing, especially through structured formats like press releases, remains one of the most effective ways to build authority and distribution at scale. But I’ve noticed a shift in the industry. Humans are no longer the only audience that matters. Feed the LLMs!
Share Of Model: Visibility Inside AI Systems
Share of model (SOM) refers to how often and how accurately your brand appears in AI-generated outputs.
Instead of asking, “How often are people seeing our content?” we must now ask:
• Is our brand included in AI-generated recommendations?
• How are we described when LLMs summarize our category?
• Are we cited, quoted or omitted entirely?
This is a fundamentally different layer of visibility and brand narrative control. LLMs act as intermediaries between your content and your audience. They ingest vast amounts of information and then compress it into a handful of synthesized answers. If your brand isn’t part of that compressed knowledge, you’re effectively invisible in that interaction.
In other words, SOM determines whether you’re part of the answer or left out entirely.
Why This Shift Matters
The move from search engines to “ask engines” is changing how discovery works. Traditionally, users would search, scan results and click through multiple links. Today, they increasingly rely on AI-generated summaries that present a small set of options or a single synthesized answer.
This creates three major implications:
1. Fewer Opportunities To Compete: AI responses often highlight only a handful of brands. There’s no second page of results.
2. Compressed Brand Narratives: Your positioning is distilled into a sentence or two. That makes consistency and clarity critical.
3. Linkless Influence: As I noted in my first GEO article, being referenced is the new ranking. Traffic is no longer the only goal.
How Share Of Voice Feeds Share Of Model
While share of voice and share of model are distinct, they are deeply connected.
Strong SOV can contribute to SOM, but only if the content is:
• Structured and easy to parse
• Published across credible, authoritative sources
• Consistent in messaging and terminology
For example, distributing press releases across trusted networks doesn’t just generate immediate exposure. It creates a persistent digital footprint that AI systems can ingest, index and reuse over time.
Similarly, publishing clear, question/answer driven content increases the likelihood that LLMs will reuse your language when answering user prompts. All of this said, not all visibility translates equally. Noise doesn’t become knowledge. Structured, repeatable narratives do.
Practical Ways To Improve Share Of Model
If GEO is about feeding the LLMs, then improving share of model is about feeding LLMs the right signals consistently.
Here are a few strategies I’ve found effective:
1. Prioritize Clarity And Structure: LLMs favor content that is easy to understand and self-contained. Use clear headings, concise explanations and logical formatting.
2. Reinforce Core Narratives Across Channels: Your brand description, positioning and key messages should be consistent everywhere, from your website and press releases to third-party coverage.
3. Publish In High-Authority Environments: Content that appears in trusted media outlets, industry publications and widely syndicated platforms is more likely to influence model outputs.
4. Think In Questions And Answers: As covered in my GEO article linked above, formats like FAQs, how-to guides and summaries align closely with how users interact with AI systems.
5. Monitor AI Outputs Regularly: Search your category in AI tools. See which brands are mentioned, how they are positioned and where you stand.
Expanding Your GEO Strategy
If you’re already investing in generative engine optimization, this is the natural next step. GEO helps ensure your content is structured, credible and discoverable by AI systems. Share of model measures whether those efforts are actually working.
Together, they form a feedback loop. To start, publish structured, authoritative content. Then, distribute it across trusted channels and reinforce consistent narratives. From there, monitor how AI systems respond.
Then, repeat, repeat, repeat.
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This is a great post! I experimented with SoM a few months back. I wanted to see how much, if any, my brand’s SoM would increase with the right inputs you laid out in this post. I tested on Perplexity. For a specific prompt, my brand went from 25% to just over 63% in 24ish hours. Insane how quickly this metric can move with the right inputs!