Editorial
In SEO, visibility is everything. Presence in organic search engine results is the prime conduit between the brand and user.
But just showing up in organic search is no longer enough to support brand visibility. Large language models (LLMs), like those utilized in tools such as ChatGPT, Gemini and the AI overviews (AIOs) that dominate Google SERPs, have revolutionized users’ interactions with brands. Visibility now requires a more expansive approach that builds upon traditional SEO strategies to ensure brand representation within AI outputs. To do that, brands have to understand how generative AI works.
Generative AI search models form an understanding of concepts based on patterns — the statistical relationships between words, phrases and topics across billions of documents. In addition to the base models, when generating responses to queries, these models retrieve additional sources and evaluate their relevance, authority and consistency to inform responses about brands.
To understand their positioning in this new landscape, brands need to measure a new set of KPIs: narrative inclusion, citation frequency and sentiment.
Brand Visibility in the Age of AI
Before the internet, brand visibility was achieved through advertising in impression-based media like TV, billboards and magazines. Survey results and sales numbers provided broad data about efficacy, but the customer journey was largely opaque.
With the internet, services like Google Search Console and Google Analytics enabled detailed attribution. This allowed brands to track customer journeys and conversion sources to quantify online performance through attribution models like last click and linear attribution. In short, brands could see where customers originated, which customers converted and where.
As use of AI channels increases, we are returning to a state of less visibility into consumer journeys. Reliable market-size data (like MSV) is limited, consumer journeys are less trackable and attribution is less direct. Generative AI outputs often satisfy users’ informational queries directly, which lowers click-through rates (though they may increase CTRs for transactional queries).
Let’s take a look at some other data that shows how LLMs have changed user behavior:
- In April 2025, Google had an 86.7% share of the US search engine market, with Bing at 7.5%. This marks a fairly sustained dip in Google usage, down 2.3% over the past six months.
- According to Apple, Safari searches have dropped for the first time ever in 22 years, likely due to increased usage of generative platforms.
As features like AIOs displace organic results and users begin turning to generative models over online search engines, organic rankings matter less. Consumers still have questions that need answers, though they are getting those answers through AI channels. Brands still need visibility and impact metrics — and a strategy to address them.
Related Article: Google’s AI Mode Signals a New Era in Search
KPIs for Generative Search: What Really Counts
Monitoring these metrics can be challenging without detailed click data or insight into source retrieval algorithms (how AI models rank and “choose” the best sources). What’s more, in some cases, brands may succeed in appearing in the text of AI outputs, but don’t receive a link citation.
To overcome these challenges and gauge visibility in generative AI outputs, some companies have built proprietary tech to track relevant signals at scale. The cornerstone of these signals is narrative inclusion: whether an AI-generated response to a query mentions or features your brand.
The next level metric is citation frequency, which indicates how often AI models directly link to your content. Another important metric, generative share of voice, tracks a brand’s frequency and positioning in AI-generated outputs relative to competitors.
The tone or perception conveyed when your brand is included in AI answers is also measurable. Sentiment and framing metrics indicate how well your brand narrative is aligned with the target query’s intent.
Building Authority That LLMs Can’t Ignore
To harness the rapid evolution of LLMs and measure the success of connecting with consumers through generative engines, brands need to align their strategy with the ways LLMs synthesize and present information. Collaboration between PR, SEO, content and data science departments is imperative to influencing brand representation across generative models.
Brands must prioritize proactive reputation and narrative management. In other words, they must provide the content and context signals necessary for their brand information to be accurately represented in AI outputs. Investment in topical authority and consistency across digital content is paramount to achieving true visibility in the landscape of LLMs and AIOs.
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Ignore AI at Your Own Risk
It’s important to remember that traditional SEO is still an effective strategy to reach consumers. But representation in the LLM knowledge space is a crucial and growing aspect of visibility. Without it, your brand will be left out of conversations and you miss opportunities to connect with the user. In the absence of your brand in AI outputs, competitors define the narrative and your brand. And a failure to place relevant content means that inaccurate or outdated information can populate AI outputs in place of the real value your brand provides.
The very definition of brand visibility is evolving as AI channels gain market share. Measuring the impact of brand strategy needs to evolve, too.
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