AI Visibility: GEO, AEO, AI Search & SEO

Why On-Page SEO Strategies Now Need Intent and AI | RiseOpp

• RiseOpp • Season 2 • Episode 76

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Full Transcript: On-Page SEO Strategies and Tactics for Marketers

On-page SEO has moved beyond keyword placement into a system for matching search intent, technical quality, and AI-driven discovery.

This episode breaks down how On-Page SEO Strategies use semantic mapping, topic clusters, Core Web Vitals, internal linking, structured data, and schema markup to improve visibility.

Marketers, founders, SEO professionals, and growth leaders will learn how to align technical optimization with useful content and long-term organic growth.

👉 Read the full guide:

https://riseopp.com/blog/on-page-seo-strategies-and-tactics-for-marketers

SPEAKER_01

Welcome to a new deep dive. Today, uh, we're looking at a stack of sources to figure out how to survive the rapid evolution of on-page SEO. We've got fresh data from SEMrush, RIFS, and Digital Silk. And well, back in the day, optimizing a page felt a bit like stuffing a turkey.

SPEAKER_00

Aaron Powell Right, just cramming it all in.

SPEAKER_01

Exactly. You just crammed as many keywords as possible into the text, you know, and hoped for the best. And getting it right still matters because organic search drives a massive 53% of all website traffic. That's from the digital silk data. But uh that old stuffing playbook, totally obsolete.

SPEAKER_00

Aaron Powell Oh, completely gone. Now, optimizing your site is, I mean, it's more like hosting a dinner party. And your VIP guest is an AI algorithm.

SPEAKER_01

Aaron Powell Okay, let's unpack this. How do we cater to this new AI guest without ruining the experience for human readers?

SPEAKER_00

Aaron Powell Well, we really have to start treating modern SEO as a marketing operating system rather than just a mechanical checklist. Right. Search engines and AI models like Gemini and ChatGPT, they aren't just scanning text for keyword matches anymore. They are mapping semantic user intent.

SPEAKER_01

Aaron Powell So they're looking for context.

SPEAKER_00

Aaron Ross Powell Exactly. They analyze the relationships between words and look at behavioral signals, things like previous searches or geographic location, just to understand the underlying problem you are trying to solve.

SPEAKER_01

Aaron Powell So instead of just looking for the exact phrase like best running shoes, the model calculates that a user is probably looking for trail running shoes because of their recent search history about hiking.

SPEAKER_00

Aaron Powell Spot on.

SPEAKER_01

Right, zero click because users are getting their answers synthesized directly on the page, right?

SPEAKER_00

Yeah, exactly. The Sumrush data actually found that over 13% of Google searches now include AI-generated summaries. And uh 88% of those are triggered by informational queries.

SPEAKER_01

Wow.

SPEAKER_00

Yeah. So the engine just reads the web, writes the summary, and the user never even needs to leave Google.

SPEAKER_01

Aaron Powell Wait, I have to challenge this whole strategy for a second, though. If the AI just answers the question immediately on the page, don't we lose the traffic? Like the a refs report in our stack shows the top organic link loses over 34% of its clicks when an AI overview appears.

SPEAKER_00

Aaron Powell It's a huge hit for sure.

SPEAKER_01

Right. If I'm losing a third of my clicks right off the bat, why even bother optimizing?

SPEAKER_00

Well, that drop in traffic is exactly why this force is a complete pivot in our strategy. The central metric of success is shifting, you know, away from standard click-through rates. Now it's all about citation frequency.

SPEAKER_01

Citation frequency, like getting mentioned.

SPEAKER_00

Yeah. If we can't guarantee the click, our goal is to become the AI's source material. We want the language model to quote our declarative statements directly and link back to us as the origin of that information.

SPEAKER_01

Oh, I see. But if we need them to quote us directly, it sounds like we need to feed the AI our credentials in a language it natively understands rather than just, you know, writing a good article and hoping it extracts the right data.

SPEAKER_00

Aaron Powell Which brings us to schema markup. This is basically what translates your human content for algorithms by wrapping your text in invisible code tags.

SPEAKER_01

So you are specifically tagging a string of text as author or FAQ or whatever.

SPEAKER_00

Exactly. Or product review. That way the AI doesn't have to parse natural language to guess if a name belongs to a writer or just some person mentioned in a quote.

SPEAKER_01

It's basically using the Dewey Decimal system for your website. You're handing the AI a perfectly categorized map of your article before it even reads it. Like, here are my credentials, here's the main takeaway, and here's the data.

SPEAKER_00

Aaron Powell That is a great way to put it. And algorithms demand that structural map because they are looking for EET, you know, experience, expertise, authoritativeness, and trustworthiness.

SPEAKER_01

Right, the acronyms.

SPEAKER_00

Yeah. They need literal proof of experience. So when your schema metadata points directly to verifiable authors, custom data, and original research, you prove a credible human wrote the content. And that naturally elevates your trust score in the model's eyes.

SPEAKER_01

So whether you run a personal blog or a massive e-commerce site, your new mandate is what the sources call data-driven creativity. You have to write compelling stories for the human, but tag everything structurally for the machine.

SPEAKER_00

Application is everything here. You have to blend the technical framework of schema with an authentic human voice. That ensures the algorithm can validate your expertise while the reader, you know, actually enjoys the content.

SPEAKER_01

That makes total sense. But here is where it gets really interesting and maybe a little unsettling to think about. If AI systems rely so heavily on this structured schema data to decide which human experts to cite, will we soon reach a point where AI models start generating their own structured data?

SPEAKER_00

Oh, like just to cite other AIs.

SPEAKER_01

Yeah. Could we see a future where human creators are cut out of the discovery loop entirely, just algorithms talking to algorithms? Definitely something to ponder as we wrap up today's deep dive.