Tarek Reslan

Search isn’t just about finding the right website anymore.

Google can answer a question directly through AI Overviews. ChatGPT can compare five products without sending someone to a single review page. And buyers can research an entire category, narrow their shortlist, and decide which brands are worth considering before visiting a company’s website.

That’s a fundamental change to how discovery works.

AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization) are the names marketers have given to adapting to that shift. There’s still plenty of hype around both. But the changes to rankings, citations, brand visibility, and organic traffic are measurable.

The more useful question isn’t whether AEO/GEO is just SEO with a different name. It’s how AI has changed the search journey, and what marketers need to do differently.

Here’s what the data tells us.

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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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The AI search conversation has devolved into a mess of conflicting data points, breathless predictions, and cherry-picked statistics. 

One report tells you to panic while another says nothing’s changed. Meanwhile, marketing buzz swings between “Google is dead” and “AI is overrated,” depending on which study dropped that week.

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SEO used to be about visibility. Now it’s about credibility.

Discovery is shifting from keywords and rankings to AI-driven chat experiences.

The real advantage, however, isn’t getting AI to remember your brand. It’s creating content that buyers trust and that search systems have a reason to reference.

AI search systems retrieve and reference content to help answer questions. Clear explanations, relevant information, and credible supporting evidence give them useful material to work with. And for buyers, that same credibility can make the difference between reading your content and actually trusting what it says.

In this article, we’ll walk through an AI content gap analysis of your own pages: the AI content audit approach Andy Crestodina, co-founder of Orbit Media, uses to find what your content fails to say. We then show how to layer channel-level content gap analysis on top to turn insights into intent-aligned content that drives consistent, high-quality B2B leads, without relying on a “publish more” hamster wheel. (If you specifically want to compare a page against what Google’s AI Overview says for a query, that’s a different exercise: see our guide to AI Overview gap analysis.)

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The old funnel’s basically dead. Buyers aren’t clicking their way through journeys anymore. They’re discovering brands in places where clicking is optional or nonexistent: AI summaries, LLMs, LinkedIn feeds, and Reddit-style threads. That’s where the momentum starts: where people shape what’s trusted, repeated, and remembered.

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Buyers are asking AI assistants to help research products, compare vendors, and make purchasing decisions.

Users ask AI everything from product recommendations to internal research questions. Whether marketers realize it or not, LLMs are already part of the buying process.

When an AI assistant becomes part of the buyer’s research process, there’s an interesting opportunity: give visitors a way to explore your product through a pre-written prompt. You can frame the question, highlight a relevant use case, and let the AI explain the offer.

Most marketers pour energy into CTAs that nudge visitors down the funnel, yet rarely test what happens when a CTA opens an AI conversation instead.

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