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AI search is moving beyond citations and recommendations. As consumers use AI to discover businesses (and personal agents begin browsing the web on their behalf), brands need to think about the entire information environment surrounding them.

In this episode of Found in AI, Cassie Clark breaks down Google’s updated guidance on main content and fake authors, new data showing rapid growth in AI-powered local search, and Profound’s research into personal agents like Muse and Instinct.

Together, these developments point to a larger shift: AI visibility is becoming less about optimizing for a single answer and more about whether humans and AI agents can discover, understand, verify, and act on information about your brand.

In this episode:

• What Google’s new “good main content” guidance means for SEO and GEO
• Why content structure matters without becoming another GEO hack
• Why brands cannot manufacture authority with fake experts
• How AI is changing local business discovery
• Why AI recommendations still trigger additional verification
• How personal agents like Muse and Instinct browse the web
• What agent-driven discovery could mean for websites and marketers
• Why AI visibility and agent readiness are becoming connected problems

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Cassie Clark is an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at cassie@cassieclarkmarketing.com.

Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail

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Join the weekly The Visibility Report: subscribe

In this episode of Found in AI, Cassie Clark talks with Peter Rota, an SEO professional with 15 years of experience, about the rapidly growing market for AI visibility tools — and where those platforms still have room to improve.

Cassie and Peter discuss why actionability remains one of the biggest challenges with AI visibility software, what teams should look for when evaluating a platform, and why a visibility score alone may not tell you very much.

They also dig into one of the biggest AI search measurement questions: should brands care more about being cited, mentioned, or actually recommended?

Peter shares his own hierarchy — recommendation, mention, then citation — and explains why citations can sometimes come dangerously close to becoming a vanity metric if they aren't connected to meaningful business outcomes.

In this episode:

• Where AI visibility tools are useful — and where they still fall short
• Why AI visibility data needs to lead to action
• What to consider before paying for an expensive AI visibility platform
• Why prompt-based visibility scores don't tell the whole story
• Why mentions and citations shouldn't be combined into one metric
• The difference between being recommended, mentioned, and cited
• When citations risk becoming a vanity metric
• Why the intent behind the prompt matters
• How to identify a perception gap between your website and an AI system's understanding of your brand
• Why lower-funnel prompts may be more valuable to track
• Whether your next AI search investment should be software or a person who can actually turn the data into strategy

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Cassie Clark is an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email me at cassie@cassieclarkmarketing.com.

Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/

Send us Fan Mail

📬 You like this podcast? You’ll love the newsletter.
Join the weekly The Visibility Report: subscribe

Google is expanding AI Mode’s monitoring capabilities, letting users have Google continuously watch the web for new information instead of repeatedly running the same search. In this episode of Found in AI, Cassie Clark looks at what persistent search could mean for content freshness and AI search visibility.

Cassie also breaks down new research from Graphite analyzing nearly 500,000 AI shopping responses across ChatGPT and Google. While large and small retailers appeared at similar rates among cited sources, major retailers were significantly more likely to become the actual recommendation — another example of why citation presence and recommendation presence should not be treated as the same AI visibility metric.

Plus, Google Search Console adds reporting for multimodal searches originating from tools including Google Lens and Circle to Search, and new research from Perplexity explores why retrieval systems may need more than the single passage containing an answer to provide enough context for a useful response.

In this episode:

• Why Google AI Mode monitoring changes the traditional search journey
• What persistent search means for content freshness
• What Graphite found after analyzing nearly 500,000 AI shopping responses
• Why being cited by an AI system does not guarantee your brand will be recommended
• How prompt language can affect which retailers AI recommends
• Google Search Console’s new multimodal search reporting
• Why search intent increasingly exists without a traditional typed query
• What Perplexity’s contextual embedding research tells us about AI retrieval
• Why structuring content for AI shouldn’t mean removing useful context

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Found in AI covers AI search visibility, generative engine optimization (GEO), answer engine optimization (AEO), AI search measurement, and the changes marketers need to understand as platforms like ChatGPT, Google, and Perplexity reshape search and discovery.

Cassie Clark is an AI search visibility consultant and a fractional content strategist for startups and enterprise brands. If that sounds like the kind of help you're looking for, email her at cassie@cassieclarkmarketing.com.

Or request your AI Search Visibility Audit: https://cassieclarkmarketing.com/ai-search-visibility-audit/