TL;DR
- AI recommends experts based on consistent, corroborated signals across trusted sources, making earned media more valuable than ever.
- Instead of chasing isolated PR wins, build long-term authority around the topics you want to own.
- The brands that consistently reinforce their expertise across articles, interviews, podcasts, and other earned media are the ones AI is most likely to recommend.
The buyer’s journey isn’t what it once was. More people are asking ChatGPT, Claude, Gemini, or Perplexity for recommendations instead of starting with a traditional Google search. That means they’re increasingly forming their shortlist before they ever click through to a company's website.
So where do those AI-generated recommendations come from? While large language models (LLMs) draw from many sources, they heavily rely on the signals created by PR: earned media, thought leadership, expert commentary, and authoritative content.
During a recent webinar hosted by Qwoted, Gini Dietrich, creator of the PESO Model® and CEO of Spin Sucks, shared insights into why earned media has become an increasingly important part of that equation.
How AI Decides Who to Recommend
AI models don’t independently decide who’s an expert. They look for repeated, corroborated signals across trusted sources.
Muck Rack’s What Is AI Reading? report found that roughly a quarter of AI citations come from earned media — a strong indication that PR plays an important role in shaping AI-generated answers.
“I believe we have a really huge opportunity right now, because earned media is more valuable than ever in a world where AI decides who to trust,” said Dietrich. “The machines are looking for consistent signals from credible sources. That’s literally what you create through earned media.”
Why Earned Media Matters More to AI
There was a time when ranking on the first page of Google was a primary marketing goal. But you need more than a high-ranking website to be featured in AI answers.
Dietrich explained that owned media tells AI what you believe, while earned media tells AI what other people believe about you.
Owned and earned media shouldn’t operate as separate programs. They should reinforce one another. Your website establishes your expertise, while earned media validates it. Together, they create the consistent signals AI platforms look for when deciding what to recommend.
This approach is increasingly referred to as generative engine optimization (GEO) and answer engine optimization (AEO): optimizing your content so AI search platforms consistently recognize, validate, and cite your expertise. Unlike traditional SEO, which focuses on ranking web pages, GEO and AEO focus on ensuring AI systems have enough trustworthy, consistent evidence to confidently include your brand in their answers.
AI isn’t looking for the single best article you’ve ever published. It’s looking for consistent patterns of expertise. When the same expert repeatedly explains the same topic across your website, trade publications, podcasts, analyst reports, and other third-party sources, AI gains confidence that the expert — and by extension, their organization — is a credible authority worth surfacing.
At Look Left, that’s changing how we plan out thought leadership strategies for clients. We’re increasingly ensuring that the topics leaders write about contribute to a larger theme, so every article reinforces the same expertise.
The Trophy Case vs. the Credibility Loop
Not all media coverage sends the same signals to AI. Dietrich illustrated this with two contrasting approaches to PR: one that creates a collection of isolated wins, and another that compounds credibility over time.
In the first example (aka the trophy case), random wins create noise. Consider a PR team that lands a cover story in a trade publication. It’s on message, links back to the website, and quotes the SME. High fives all around.
The problem is there’s no connection back to a real buyer question and no reason a human or an AI would recognize that article as part of a bigger story about how you solve a problem.
In contrast, another team builds a body of work that specifically answers a buyer’s question. Now, when someone asks an AI about this topic, the AI sees the same expert and the same idea appearing consistently across multiple trusted sources. That’s a very strong signal of which definitions to use, whose framework to cite, and which examples to surface, Dietrich explained.
We increasingly evaluate media opportunities through this lens. Rather than chasing every publication, we ask whether a placement reinforces the topic a client wants to own. A contributed article, podcast interview, or expert quote may be valuable on its own, but it’s even more valuable when it’s one piece of a larger body of work on the same topic.
Measure What Actually Matters
Just as AI changes how authority is built, it also changes how success should be measured. Traditional PR metrics like impressions, placement count, and share of voice still matter, but you also need to track AI visibility metrics.
You’ll want to know if you’re showing up in AI answers for your key areas of expertise, how frequently, and if the market is repeating your language.
When you start tracking this, remember that it’s a long process that can take several months to pay off. The first measure of success is to simply be mentioned in an answer; next is being cited in one, and then, eventually, owning the whole answer.
The Future Belongs to Verifiable Expertise
The fundamentals of good PR haven’t changed. You still need expertise, relationships, and great stories to tell. What’s changing is how those pieces work together.
That’s why we’re increasingly helping clients build long-term topic ownership rather than treating each byline or media placement as an independent success. Every earned opportunity becomes another piece of evidence reinforcing the same expertise.
Ultimately, AI isn’t inventing expertise. It’s recognizing patterns of expertise that already exist and repeating them back. You just need to be sure you’re part of that pattern. The good news? That’s exactly what great PR has been doing all along.
Wondering how your brand shows up in AI answers? Let’s talk about building a PR strategy that’s designed for both people and AI.
Frequently Asked Questions
What’s the difference between SEO, GEO, and AEO?
SEO is designed to increase the visibility of web pages in traditional search engines like Google. GEO focuses on increasing your visibility in AI-generated answers inside platforms like ChatGPT or Claude, while AEO is specific to AI answers in search engines or voice assistants, like Google’s AI Overviews. Both GEO and AEO are influenced by authoritative content, earned media, expert commentary, consistent messaging, and trustworthy third-party validation.
How does earned media influence AI-generated recommendations?
LLMs look for repeated, corroborated information across trusted websites. Earned media helps create those trust signals by demonstrating that independent journalists, industry publications, analysts, and other third parties recognize your expertise. When your insights consistently appear across authoritative sources, AI platforms are more likely to view your organization as a credible authority.
Why is consistency important for AI visibility?
AI models look for patterns rather than isolated examples. When the same expert consistently discusses the same topic across your website, earned media, podcasts, webinars, and contributed articles, those repeated signals increase confidence that the person is a credible authority. Consistency across channels is often more valuable than a single high-profile media placement.
How can PR improve my brand’s visibility in AI search?
A strong PR program helps generate the third-party validation AI systems rely on. Contributed articles, interviews, podcast appearances, expert commentary, original research, and media coverage all reinforce your expertise. When those activities support a clear topic strategy and link back to authoritative content on your website, they strengthen the signals AI uses to recommend brands.
