No prizes for guessing the biggest topic of discussion at this year’s Meltwater Summit! The impact of AI on brand visibility was mentioned in almost every keynote, panel, and workshop throughout the two day event.
The fact that this topic was so prevalent only serves to reinforce the point that the way consumers discover brands has changed forever, and marketers are reevaluating their strategies in the face of this new reality.
For years, the goal was straightforward; show up in search results, earn credible media coverage, appear in social feeds, and drive clicks. But while those goals haven't gone away, what has changed is where people look for information, with consumers, journalists, executives, and stakeholders increasingly turning to AI assistants for recommendations, summaries, and research. Consequently, the way brands earn visibility is evolving.
In the words of Chris Hackney, Meltwater's Chief Product Officer:
In an AI-first world, the job now is to be the answer.
That idea sits at the heart of Generative Engine Optimization (GEO), the practice of understanding, measuring, and improving how brands appear in AI-generated responses from tools like ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews.
Throughout Meltwater Summit, speakers kept returning to the same theme, GEO is far broader than SEO - it's becoming a discipline that touches reputation, communications, content strategy, and measurement all at once. Here are seven lessons we learned about brand AI visibility at Meltwater Summit 2026.
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Contents
1. Brands now have two audiences: people and AI models
2. GEO is a communications opportunity, not only a marketing tactic
3. Earned media is back at the center of the strategy
5. Creators, LinkedIn, Reddit, and YouTube are becoming citation infrastructure
6. Measurement is moving from coverage volume to answer quality
7. The brands that win will be clear, credible, and consistent
FAQ - AI visibility for brands
1. Brands now have two audiences: people and AI models
During the "Mastering LLM Visibility" workshop, Nate Pallen, Senior Director of Product at Meltwater, framed the challenge simply:
Your brand has two audiences, humans and AI models.
That observation captures a new reality for communicators, in which a buyer may never visit your website, a journalist may begin research with an AI-generated summary, or a customer may ask an LLM which provider they should choose. If the model doesn't understand your brand, recommend your brand, or surface credible sources that support your narrative, the person behind the prompt may never encounter you.
As Pallen put it:
If the model doesn't know you, the human never gets the chance.
Visibility today is tied to understanding and trust, and that’s why why GEO pushes communicators beyond rankings and clicks, because AI systems need enough credible information to confidently reference a brand, explain what it does, and connect it to the topics people are asking about.
2. GEO is a communications opportunity, not only a marketing tactic
Several speakers argued that communications teams are uniquely positioned to lead GEO efforts because LLMs rely heavily on the public record. Earned media, reviews, expert commentary, social conversations, and third-party validation all help shape how AI models understand organizations.
Amanda Coffee, CEO of Coffee Communications, said comms teams have been "deputized, like almost overnight, with being a key stakeholder in AI," because "AI reads earned media."
GEO isn't simply about publishing more content or adding keywords to a page, but depends on the broader body of evidence available online. The conversations, coverage, and references surrounding a brand all contribute to the picture an AI model builds.
Dominic Hawkins, VP of Communications at the NAACP, described the mindset as treating AI almost like another stakeholder:
We look at it as an influencer. How do we influence that influence?
For communications leaders, that framing changes the conversation. If LLMs help shape what people believe, compare, and ultimately choose, then the way a brand appears in AI-generated answers becomes part of reputation management.
Christina Bennett, Head of Communications at Priceline, put it even more directly:
AI LLM strategy is a reputation engine.
3. Earned media is back at the center of the strategy
One of the strongest themes at Summit was the renewed importance of earned media.
Noah Greenberg, CEO of Stacker, argued that communications teams have a major opportunity to lead GEO because earned media remains one of the most influential inputs into AI-generated answers. He highlighted research suggesting that the vast majority of AI citations driving brand mentions come from earned media rather than owned or paid channels.
His message was practical; Brand content can't remain confined to owned channels, but needs credible third-party distribution, because where a claim comes from can be just as important as the claim itself.
Palmer Hutchins, VP of Marketing at G2, made a similar point during a conversation with Meltwater's Jenny Force. He compared LLMs to "an investigative journalist" that attempts to validate a brand's claims across multiple sources. That means consistency across review sites, earned media, social platforms, Reddit, YouTube, and other third-party environments becomes increasingly important. Hutchins said:
Just having your website be SEO optimized is not going to be enough.
Owned content still plays an important role, but it’s not enough by itself, so brands need independent validation, trusted coverage, customer proof, expert commentary, and consistent messaging across the places AI models use as evidence.
4. Authority now beats volume
An important point, reiterated by several speakers, is that one of the most fundamental components of GEO strategy is creating information that is clear, credible, specific, and easy to verify.
Gina Kleiner, Senior Director of Product Marketing at LinkedIn, said brands should start "considering LLMs as a key audience" when creating content. That doesn't mean producing robotic copy designed for machines, but creating content that is genuinely useful, well-supported, and easy to understand.
Hackney shared research from Meltwater's work examining LinkedIn and LLM citations, which showed that content cited by LLMs was often grounded in attributed data and identifiable expertise. In other words, models responded to substance, and named experts, original research, customer stories, credible sources, and specific claims consistently carried more weight than vague assertions.
For brands, that means broad thought leadership without evidence is becoming less effective. Original research, expert commentary, customer proof, and authoritative third-party validation are gaining importance.
Jenny Force, VP of Global Marketing at Meltwater, shared a practical example during the G2 session. Meltwater's marketing team created what she called an "AI language guide" to help teams describe the company consistently across blogs, PR, product marketing, and other content channels.
There's a useful GEO lesson in that approach - clear language builds on itself over time, but if different teams describe a company in different ways, AI models can struggle to form an accurate picture.
5. Creators, LinkedIn, Reddit, and YouTube are becoming citation infrastructure
Several Summit sessions highlighted how AI visibility extends well beyond traditional media.
During the LinkedIn session, Hackney explained that YouTube, LinkedIn, and Reddit are among the most frequently cited open-web sources. A finding surprised the research team, was that on LinkedIn, approximately 75% of citations came from creators rather than company pages.
Company pages still play an important role, but executives, employees, customers, creators, and independent experts may have a much larger influence on how AI models understand a category than many organizations realize.
YouTube also appeared repeatedly in GEO discussions, particularly as a source of rich, long-form content. Hackney noted that detailed videos provide the kind of context and depth that LLMs can draw from when generating responses.
One takeaway from all of this is that AI visibility comes from many places at once. Journalists, creators, customers, communities, experts, review sites, social platforms, and video content all contribute to the information ecosystem. GEO strategies need to account for that broader landscape.
6. Measurement is moving from coverage volume to answer quality
Traditional PR measurement has often focused on volume: placements, reach, impressions, share of voice, and estimated media value.
GEO introduces a different set of questions.
- Are we appearing in AI-generated answers?
- Are we being cited?
- Are those citations accurate and favorable?
- Are we being recommended?
- Which narratives are showing up repeatedly?
- Which sources are shaping those answers?
- How do we compare against competitors across different models?
Hutchins described a useful progression; first, a brand gets mentioned, better yet, it gets cited, better still, it earns a branded citation. The ultimate goal is becoming the recommended solution, he said:
Winning the answer should be the goal.
Andrew Pern, Marketing Analyst at Qualcomm, explained why this kind of measurement is valuable from a customer perspective. Before using Meltwater Gen AI Lens, his team manually tested prompts and tracked responses, a process that was difficult to scale consistently. With dedicated measurement in place, he could answer executive questions about how the brand appeared across LLM responses.
His warning was direct:
If you're not measuring how you show up in those answers, you're handing control of your brand narrative to external sources.
7. The brands that win will be clear, credible, and consistent
The biggest takeaway from Meltwater Summit is that GEO rewards brands that make it easy for AI models to understand them accurately.
That means: clear positioning, consistent language, strong earned media, expert voices, third-party validation, well-structured content, and active measurement. But more than that, it means recognizing AI models as a new audience without losing sight of the people behind the prompts.
GEO is still in its early stages, and speakers were candid about the experimentation involved. Different models have their own unique behaviors, citation patterns can vary significantly between models, and measurement benchmarks are still taking shape as the industry finds its feet. Even so, the broader trend is becoming easier to see.
As Hackney said, the old game was getting your brand discovered and clicked. The new game is becoming the answer.
For PR, communications and marketing teams, that's less a threat than an opportunity. The skills that have always built trust, credibility, and visibility are becoming even more valuable as AI plays a larger role in how people discover information.
FAQ - AI visibility for brands
What is Generative Engine Optimization (GEO)?
Generative Engine Optimization (GEO) is the practice of improving how a brand appears in AI-generated responses from tools such as ChatGPT, Gemini, Claude, Perplexity, and Google AI Overviews. Unlike traditional SEO, GEO focuses on helping AI models understand, trust, cite, and recommend your brand when answering user questions.
How is GEO different from traditional SEO?
SEO focuses on improving visibility in search engine results pages and driving website traffic. GEO focuses on influencing how AI systems interpret and present information about your brand. While SEO often prioritizes rankings and clicks, GEO prioritizes accurate representation, citations, recommendations, and brand inclusion in AI-generated answers.
Why is earned media important for AI visibility?
AI models frequently rely on trusted third-party sources when generating responses. Media coverage, expert commentary, customer reviews, creator content, and independent validation help establish credibility and provide the evidence AI systems use to understand and reference brands. This makes earned media a key component of an effective GEO strategy.
How can brands measure their visibility in AI platforms?
Brands can track metrics such as AI-generated mentions, citations, recommendations, sentiment, source attribution, and competitive share of voice across different AI platforms. Dedicated GEO measurement tools, such as Meltwater's GenAI Lens, help organizations understand how they appear in AI-generated answers and identify opportunities to improve visibility and narrative control.
