“I still think good AI search and good GEO are just good SEO. It’s good search marketing.” — Kaylee Peterson, Director of SEO and AI Search at Idea Grove
AI search has introduced a new vocabulary for marketers: GEO, AEO, AI visibility, citation tracking, query fan-out.
But underneath the terminology, the fundamentals of good search marketing haven’t disappeared. If anything, they’ve become more important.
Traditional SEO began with a relatively straightforward question: How do I get my webpage found? Answer engine optimization expanded that question to consider how information could be structured and extracted as an answer. Generative engine optimization adds another layer: How do I make my brand and expertise understandable and credible enough to be cited?
“So it’s not necessarily a new practice,” Peterson says. “It’s refining what already exists.”
What has changed is the scope of the job. Search now extends beyond ranking a webpage on Google. Customers can encounter brands through AI-generated answers, search features, videos, social platforms, reviews, media coverage and other sources before they ever visit a company’s website.
For marketing and communications leaders, that means SEO, content, PR and measurement increasingly have to work together.
Peterson organizes that work around four pillars: be found, be understood, be trusted and be chosen.
For years, SEO success was often reduced to rankings. Pick a keyword, optimize a page and try to move it toward position one.
That model is increasingly incomplete.
A Google results page can include traditional organic listings alongside AI-generated answers, images, videos, Reddit discussions, People Also Ask results and other search features. Outside Google, customers may turn to AI tools, YouTube, social platforms, maps or other sources depending on what they're researching and how they prefer to search.
The right mix is different for every audience. A technical B2B buyer may rely heavily on Google and AI tools, while someone researching a consumer product might be more influenced by YouTube, TikTok or creators.
That’s why Peterson describes the shift as “search everywhere optimization.” Instead of optimizing one page for one ranking, the objective is to build a presence wherever the audience is actually searching.
“Now I’m thinking about: Does my brand appear everywhere people are searching?”
And:
“Winning a search term is less about one position and more about: Am I appearing in all of the possible positions someone could click into?”
That matters even more as the website visit becomes only one possible part of the search journey. A customer's first encounter with a brand might happen in a search feature, an AI response, a video or a third-party conversation.
So visibility has to be a broader question than traffic alone.
Expanding the definition of search doesn't mean abandoning traditional SEO. A brand still needs to make its information accessible and understandable.
“They still have to find your content,” Peterson says. “You still have to have a site that can be crawled by these engines. You still need internal links. The structure has to be really clear, and AI still has to understand your content.”
In practice, that means many of the same fundamentals marketers have worked on for years: crawlable and indexable pages, descriptive titles and metadata, logical H1/H2/H3 structure, strong internal linking, useful content, structured data where appropriate and pages that actually satisfy search intent.
For location-dependent businesses, the same principle extends into local search. Unique local pages, maintained Google Business Profiles, customer reviews and consistent business information across relevant directories all reinforce where a company operates and what it offers.
The question for marketers is no longer simply, “Where do we rank?”
It’s “Where does our audience search, and can they find us there?”
Being discoverable only matters if people — and the systems helping them search — can quickly understand what you know and why it's relevant to their question.
That requires a shift in content strategy.
There was a period when SEO programs could generate significant traffic by publishing about almost anything tangentially related to a business. Today, Peterson argues for concentrating on subjects where a company has a legitimate reason to demonstrate expertise.
“Now it’s more about: Am I an expert in my industry and my field? Am I an authority on that?”
More importantly:
“I’m really only talking about what my audience would care to know about — and care to know about from me specifically.”
That distinction has become especially important as generative AI makes producing large amounts of content easier than ever.
The ability to publish more does not necessarily create a reason to publish more.
“Just because you can churn out 100 pages a day with AI does not mean that should be happening.”
Peterson's preference is the opposite: a smaller number of thoughtful pieces rooted in genuine expertise can be more valuable than a large volume of AI-generated content without strategy behind it.
One way to determine what deserves coverage is to map the questions customers ask throughout their journey.
Imagine someone shopping for shoes to wear on winter walks. Their first question may be broad: What should I look for in winter walking shoes?
As they learn, the questions change. Do I need boots or waterproof sneakers? Which shoes have good traction? Where can I buy the option I've chosen? What do other customers say about it?
Those aren't disconnected keywords. They're stages of the same decision.
A strong content strategy anticipates those questions and provides useful information as a customer moves from awareness through consideration, decision and loyalty.
AI search gives marketers another way to investigate those relationships through query fan-out: the related questions an AI system may explore when answering a broader prompt.
For a question about waterproof winter walking shoes, for example, related questions might involve sneakers versus boots, traction on snow and ice, comfort during long walks or the features winter footwear should have.
But the lesson isn't to create six new articles because six related questions appeared.
“You’re not necessarily going to write content for every single piece of these, but this might help you understand: If I want to write one guide that answers this question, these are all the things I might need to include in it.”
In other words, query fan-out can help marketers identify what a comprehensive answer should cover rather than becoming another excuse to manufacture pages around every possible variation of a keyword.
And because AI responses can vary by model, prompt and user, Peterson treats this kind of research as directional. Repeated testing can reveal patterns and opportunities; one AI response shouldn't be treated as ground truth.
Once you've identified the right questions, structure matters.
“A lot of times when I’m writing blogs now, one of my H2s might simply be the search question, and the first sentence under it is the answer.”
Then expand.
Use descriptive headings. Break complicated information into lists or tables when those formats make it easier to understand. And if you're including statistics or factual claims, provide the evidence behind them.
This isn't about writing robotic content for machines.
“At its core, it’s still good, helpful, useful content that my audience actually cares about and that is relevant to them,” Peterson says.
This is where AI search and PR become especially intertwined.
A company can publish detailed service pages, insightful blog posts, original research and strong case studies. But those are all sources the company controls.
Customers have always looked elsewhere for validation. They read reviews. They look for media coverage. They consider recommendations from experts and industry publications. They pay attention to awards and recognition.
That broader information ecosystem matters for search visibility, too.
“Now it’s less about the actual link and more about how your brand is being talked about.”
That doesn't mean backlinks no longer matter. High-quality, relevant links remain useful. The larger point is that authority extends beyond accumulating links to include earned media, brand mentions, reviews, expert positioning and other forms of third-party proof.
As Peterson puts it:
“It is that third-party proof that makes a difference.”
This is also where the questions uncovered during content research become valuable beyond the company website.
First ask: What questions is our audience asking?
Then: Which of those questions can we answer credibly with our own expertise?
And finally: Which independent voices could credibly discuss that expertise?
“That query fan out also gives us an idea of where we want to be featured for PR opportunities.”
Peterson takes the sources surfaced around those related questions and uses them as another input for identifying potential earned-media opportunities.
“If you look at the sources that they’re using, that gives me a good idea of: Here are some of the sources we should go earn and get placed in.”
That creates a useful connection between search strategy and PR.
The questions informing an owned guide can also reveal publications, experts and third-party sources that already have credibility around the same subject.
The objective isn't to chase every source that appears in an AI answer. It's to understand where credible conversations about your area of expertise are already happening — and where your brand could legitimately contribute to them.
There isn't one PR tactic for AI visibility.
There are several ways brands can build relevant third-party authority: media coverage, industry publications, original research and data, expert features, and reviews and recognition.
That could mean earning an expert quote in a news story. Contributing expertise to a respected trade publication. Publishing proprietary research that others have a reason to reference. Putting a subject-matter expert forward for podcasts, interviews, webinars or speaking opportunities. Or building a strong presence on the review sites and industry directories customers actually use.
The common denominator is relevance.
A mention on a credible source connected to your expertise can do more for your brand story than collecting placements simply for the sake of having another link.
The other important piece is consistency.
“You focus on what you own — your website, your blog, your case studies. And then you also focus on what you can earn.”
A brand controls its website, product and service pages, blog and case studies. It doesn't control what independent publications or customers say about it.
But it can make sure its own positioning is clear and work to earn credible coverage in the areas where it genuinely has expertise.
“The more consistent the story is across the board — the same products, the same services, the same way people talk about you — the more control you have over that output.”
That relationship introduces a simple idea: what you own, combined with what you earn, gives people and search systems more consistent information to evaluate.
That's why GEO isn't exclusively an SEO challenge.
It's also a content, PR and brand consistency challenge.
Visibility isn't the end goal.
A first-page ranking is useful. An AI citation is useful. An increase in impressions is useful.
But none of those numbers alone tells a marketing leader whether the work is contributing to the business.
“It’s now about: What am I getting out of my SEO investment? Am I getting leads? Are people buying things? Am I in front of the right people? Is there quality traffic?”
That's the final pillar: be chosen.
Modern search measurement requires multiple sources because different metrics answer different questions.
Google Search Console can tell you whether people are finding you through Google. GA4 can help explain what visitors do after arriving. AI visibility tools can monitor selected prompts for brand mentions, citations and share of voice. CRM data can show whether leads move through the sales process and eventually contribute to revenue.
No single tool tells the whole story.
That's especially important with AI visibility tracking. These platforms can only monitor the prompts they're given, so their results should not be treated as a complete census of everywhere a brand appears.
“AI tools can only track what you tell them to,” Peterson says.
Instead, she thinks about measurement as a chain:
Investment → leading indicators → behavior → outcomes → business impact.
Leading indicators include things like rankings, impressions, local visibility, AI mentions and traffic. They're signals that the strategy may be moving in the right direction, but they're not business outcomes by themselves.
“I may not be able to tie them directly to leads, but it tells me: Are we on the right track?”
Then comes behavior. Are qualified visitors reaching the site? Are they engaging with the content? Are they starting forms or moving toward a conversion?
Then outcomes.
“Do they become actual leads? Am I getting calls? Are people booking meetings? And then if those go through, am I getting revenue out of those?”
The goal is not to claim that every ranking or AI citation produced a dollar of revenue. It's to build a chain of evidence that shows how marketing activity contributes to visibility, behavior, leads and, ultimately, business impact.
The final step is interpretation.
“The biggest thing about reporting is that you don’t just want to throw numbers at people. You want to tell a story.”
An increase in impressions isn't the story. Why did impressions increase?
More AI citations aren't the story. Where did the brand gain visibility, around which topics and from which sources?
More traffic isn't necessarily a success. Did the right audience arrive, and could they easily take the next step?
More leads aren't the end of the analysis either. What contributed to those actions, and what should the organization invest in next?
It’s what Peterson calls “metrics with meaning”: moving beyond reporting the number to understanding what caused it and what the team should do as a result.
“If you understand why your numbers changed, that’s going to make you better at your job.”
AI search hasn't created four entirely new jobs for marketers.
It has made four existing jobs more important.
Be found. Show up where your audience actually searches, while maintaining the technical SEO foundation that makes your information accessible.
Be understood. Build useful content around subjects you genuinely know and the questions your customers actually ask.
Be trusted. Reinforce what you say about yourself with credible third-party proof, and connect search insights with PR opportunities that make sense for your expertise.
Be chosen. Measure whether that visibility reaches the right audience, influences meaningful action and ultimately contributes to the business.
That's why the rise of GEO shouldn't send marketers searching for a single tactic that guarantees inclusion in an AI answer.
The bigger opportunity is to connect work that has too often lived in separate silos: SEO that makes expertise discoverable, content that makes it understandable, PR that helps validate it and measurement that shows whether any of it is making a difference.
As Peterson summarizes it:
“It all comes back to the four big pillars for me: being found, being understood, being trusted, being chosen.”