AI in Marketing

For the past two years, tracking AI visibility and citations has largely been a black box.

Third-party tools like Semrush and Ahrefs introduced ways to estimate AI visibility by monitoring search results and citations. But until recently, we didn’t have first-party data directly from the search engines themselves confirming when and how often content was used inside AI-generated answers.

That changed a few weeks ago.

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The problem with competitor content analysis isn’t that it doesn’t work. It’s that doing it manually at any meaningful scale is unsustainable. 

You either do it rarely enough that it’s useless, or you burn your team’s time on a process that collapses under its own weight the moment you try to run it across more than a handful of keywords.

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Most educational content shows you the polished end result. The best practices, the final framework, the “what works.”

But what often gets left out are the setbacks, the failed experiments, and the messy process that actually leads to those results.

That’s the gap we’re closing with this new series.

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Nobody disputes that SEO fundamentals still work. Valuable content. Quality backlinks. Domain authority. These aren’t going anywhere.

It’s how people find information, and the infrastructure determining what gets surfaced that has fundamentally shifted.

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People aren’t just Googling anymore; they’re asking ChatGPT, Claude, and Perplexity to make decisions for them. “What’s the best CRM for startups?” “Which landing page builder should I use?”

The challenge is that you can’t see where ChatGPT looks before it picks a winner: the queries it uses to retrieve information and build AI recommendations are completely invisible.

Until now.

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The problem with B2B AI marketing tactics right now is that everyone’s selling certainty in a system that’s changing weekly.

LinkedIn is drowning in posts about “guaranteed” AI strategies: screenshot after screenshot of viral wins, tools that “definitely” get you ranked, and carousels that “always” perform. 

But when you actually test them, most fall apart. Not because the tactics are completely wrong, but because they’re presented without context, caveats, or an honest assessment of what changed since they stopped working.

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Planning content around search volume data alone while LLMs are running numerous backend queries is the fastest way to miss out on content opportunities.

Seer Interactive found that 95% of the queries that answer engines actually execute have zero tracked search volume. Zero.

So every time you open your keyword tool and filter by volume, you’re systematically ignoring what ChatGPT, Perplexity, and Gemini are searching for.

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