The Evolution of Search and the Importance of "Share of Model" for Brands

(Originally published on Ayzenberg.com )

As generative AI reshapes how consumers search and engage with brands, a new key metric is gaining importance: "share of model," or SOM. For marketers, this concept goes beyond traditional SEO and share of voice, reflecting how AI-powered tools influence brand visibility and consumer decisions.

Understanding "Share of Model"

The rise of large language models (LLMs) like ChatGPT, Google’s Gemini, and Meta's LLaMA represents a shift from traditional SEO metrics. Instead of focusing solely on optimizing content for search engines, brands now need to ensure their content is understood and recommended by AI models. Share of model measures how well a brand is represented by these AI systems relative to its competitors. As noted by Adweek , marketers need to treat these AI systems as new "audiences," whose perception directly affects consumer trust and visibility.

According to the "U.S. usage and trust of AI-powered online search engines 2024" survey by Statista , 25% of respondents found that AI-powered search engines delivered more specific results, while 12% reported these results were more trustworthy. As LLMs begin handling billions of search queries daily, brands that fail to optimize their content for these models risk falling behind, as AI increasingly influences consumer recommendations and product discovery.

Moreover, a recent survey from Search Engine Land supports this behavior, indicating that 27% of participants have begun using AI-powered search tools for product discovery.

The AI-Driven Shift in Consumer Behavior

Consumers are now relying more on AI-powered assistants for tasks ranging from product research to customer support. This shift moves search beyond simple keyword matches to more contextual, conversational queries. Marketing Week highlights that brands must adjust to this by ensuring their content is well-structured for AI models, so these systems can properly represent the brand and convey its value.

As AI strategist Peter Mangin emphasizes, brands need to proactively measure and optimize their SOM, much like they would track net promoter scores or brand sentiment. Without doing so, AI systems may misinterpret the brand or favor competitors, affecting consumer perception and sales.

Why Brands Must Embrace Share of Model

AI and search are becoming increasingly intertwined, making it essential for brands to adapt. As AI search assistants dominate more interactions, the share of model will have a direct impact on both market share and brand perception. To remain competitive, brands need to incorporate the following into their listening and measurement practices:

Audit Existing Content for AI Interpretation

• Brands should review all their digital content—such as websites, blogs, and social media—through the lens of how AI models might interpret and index it. This can be done by prompting LLMs (like ChatGPT) to see how they respond when asked about the brand or its products.
• Track the brand’s presence in zero-click searches , where LLMs provide direct answers without users clicking through to a website. Understanding how often and how accurately the brand is mentioned in these interactions will give a snapshot of the current share of model.

Monitor Competitor Comparisons

• Since share of model is competitive by nature, marketers should regularly ask LLMs about competing brands and products to see how their brand fares in comparison. This can involve asking the model to recommend products in a category or explain differences between two brands.
• If AI models are favoring competitors, this signals that the brand’s share of model is lacking, indicating the need for better optimization of its content.

Optimize Owned, Paid, and Earned Media

• Owned Media: Ensure that the brand's website, blog, and other owned assets are optimized for AI models. This includes making content clear, structured, and enriched with the kind of data AI models can easily interpret (e.g., FAQs, structured data like Schema.org markup).
• Paid Media: Brands should ensure that their ads are compatible with AI-driven platforms, such as ads integrated into chatbot interactions, and monitor how effectively AI models prioritize paid content.
• Earned Media: Marketers should track how frequently LLMs reference third-party content, such as reviews and mentions, to understand how earned media influences the model's recommendations.

Analyze Model Outputs Regularly

• Brands can prompt LLMs regularly to track how their products are mentioned, represented, and ranked. By setting up regular checks, brands can establish a baseline and then track progress as they optimize content and media strategies.
• Asking progressively more complex questions about the brand's audience, product attributes, and market positioning will reveal how the models perceive the brand's strengths and weaknesses.

Leverage AI Tools for Data Insights

• Using AI-powered SEO tools can help brands track visibility within AI models. Some tools may soon offer features that help brands audit their SOM across multiple platforms, providing data on how their content is indexed by LLMs and how well it is performing in search results driven by AI.

Share of model is not just a new metric but an essential strategy for ensuring that AI-driven systems correctly represent a brand’s identity and value.

The Impact of AI on SEO

Just as share of model is redefining brand visibility in AI-powered search, AI is also transforming SEO strategies. Many AI-powered tools are now integral to SEO platforms, improving tasks like keyword research, competitive analysis, and performance tracking. These tools help marketers gain deeper insights into search patterns and behaviors, which can inform more precise SEO strategies. This shift highlights how AI is streamlining tasks that once required significant manual input.

Search engine algorithms are also evolving in response to AI, becoming both more complex and less transparent. OpenAI’s ChatGPT is expected to soon access and surface a brand’s website content directly, which could lead to more zero-click searches—where users get answers directly from the search results page without visiting the site. As a result, brands will need to consistently refine their SEO tactics to stay aligned with these changing algorithms and maintain visibility (Adweek ).

Google’s Search Generative Experience (SGE) is already changing the search landscape, providing highly specific answers that rank prominently on search engine results pages. However, this introduces a challenge for brands, as E-E-A-T guidelines—which prioritize content from trusted and authoritative sources—are now in tension with SGE’s emphasis on user-generated content from platforms like TikTok, Reddit, and Quora. This means that user-generated content is competing with traditional sources for top visibility, according to Marketing Week .

Marketers’ Growing Investment in AI Tools

Many marketers are integrating AI tools more deeply into their SEO strategies. A Semrush survey reports that 68% of small and medium-sized businesses (SMBs) plan to invest in AI tools soon, recognizing the potential for AI to improve content creation, audits, and structured data. Additionally, a Capgemini survey from October 2023 shows that 45% of chief marketing officers (CMOs) plan to use generative AI for over half of their SEO activities within the next two to three years.

This growing adoption of AI highlights its increasing role in helping brands manage SEO tasks more efficiently and improve their visibility within AI-driven search environments.

One thing is clear: As search continues to evolve with AI at the forefront, brands must prioritize optimizing their SOM.

By understanding how AI models perceive and represent their content, and by integrating AI tools into their SEO strategies, brands can ensure they remain competitive in a search landscape that is increasingly dominated by generative AI. Monitoring and optimizing this metric will be as crucial as any traditional marketing KPI in the future of search.