How to Become a Brand AI Recommends
Updated: Aug 29
Summary
|
Not long ago, a buyer's online journey almost always started with Google – for both B2B and B2C businesses. Today, more buyers turn to LLMs like ChatGPT, Claude, or Gemini to learn about products, compare tools, and narrow their shortlist before they ever visit a website.
That shift has marketers asking a new question: How do we make sure AI recommends us?
We spoke with Michelle Fayssoux, who leads SEO at Fingerprint, and Steve Toth, founder of ainotebook.com, about what actually matters in AI search. Their conclusion: the companies that win won't chase AI hacks. They'll build stronger signals of authority wherever buyers, and AI, look for answers.
What are AEO and GEO – and Are They Really Different from SEO?
Answer Engine Optimization (AEO) is the practice of structuring content so search and AI systems can easily understand it and use it to answer specific questions. Generative Engine Optimization (GEO) is the broader practice of increasing a brand’s visibility and authority in generative AI, so it’s more likely to be mentioned, cited, or recommended across AI-generated responses. Put simply, AEO focuses on answering the question well; GEO focuses on making the brand more likely to show up in the AI’s answer.The two increasingly overlap – for this article, we'll focus on AEO and how marketers can improve their visibility in AI-powered search.
And, when it comes to SEO? It's all an evolution of SEO, Michelle explains. Fast websites, clear site architecture, structured content, and technical SEO still matter.
“The foundation hasn't changed but the outcome has,” she says. “Instead of optimizing for clicks alone, marketers are optimizing for more mentions in AI searches.”
The key difference is this: traditional SEO was largely about optimizing your own website to rank in search results. AI learns about your brand from far more channels and sources than your website alone, which means improving your visibility is no longer just a marketing challenge but now is one that requires action from multiple functions.
“More teams across companies influence AEO," Steve says. "Everything from brand teams to PR to product to customer support. Even your HR department and how they describe the company in job postings. This can all go into what a model ends up thinking about you."
How Do AI Models Decide Which Companies to Recommend?
LLMs don't recommend companies based on a single webpage. They build confidence by looking for consistent signals across what it already knows and what it can retrieve in real time.
Steve explains that recommendations come from two places: a model's training data and live search results. "If you're ubiquitous in the training data and your brand has strong ranking, that's going to lead to being recommended and mentioned more often."
Michelle describes this as creating "irrefutable proof" that your company belongs in a category by showing up consistently across authoritative sources. Rather than relying on a single blog post, she recommends building a presence across multiple channels. "If there's a category you care about and you don’t know where to start," she says, “make a checklist: create a blog post, record a YouTube video, write a LinkedIn post from the CEO, get your customers to talk about it on G2. It’s surround-sound.”
"The more confidence the model has in your brand and the more often it encounters you in the places it looks for information, the more likely you are to be surfaced,” says Steve.
The companies that establish these signals first will have an advantage. As buyers increasingly rely on AI to research categories, the brands investing in authority today are shaping how they're represented tomorrow, while everyone else risks letting competitors define the conversation.
How Do You Build Your Authority Across the Web?
Building authority means showing up across channels, but it also means building topical relevance. Steve explains that AI models learn by repeatedly encountering your brand alongside the topics, competitors, and customer questions that define your category. The more consistently those connections appear across the web, the more confidence the LLMs have in recommending your company.
Michelle argues this isn't an entirely new strategy, it's an evolution of one that's always mattered. Earning reviews, analyst coverage, PR mentions, and community discussion has long helped establish brand credibility. The difference is that AI now relies on those signals much more heavily when deciding which companies to mention.
Which signals matter most depends on the question being asked, explains Steve. AI doesn't rely on the same sources for every prompt. Questions about product comparisons often draw from Reddit, reviews, and analyst sites because they're looking for independent perspectives. But questions about integrations, security, or compliance are more likely to rely on first-party documentation because your site is the authoritative source. LLMs can then use these inputs to create side-by-side comparisons and inform buying decisions in ways that traditional Google searches historically could not.
The takeaway isn't that you need to be everywhere. It's that you need to build authority on the topics your buyers care about, in the places they naturally turn for answers.
What Types of Content Are AI Models Most Likely to Cite?
Not all content carries the same weight with AI. Michelle recommends creating differentiated content, especially proprietary research, customer insights, benchmarks, and expert analysis. "Using your own proprietary data is one way to do that because competitors can't just copy and paste,” she says. One of Fingerprint’s most shared content pieces spotlighted a privacy vulnerability discovered by their expert research team.
Steve suggests focusing less on publishing volume and more on maintaining high-value content. At FreshBooks, his team published hundreds of articles per month. Today, he says that approach would be a "huge red flag." Instead, he recommends maintaining 20-50 high-value cornerstone pages, like comparison pages, pricing, compliance, and industry guides, and keeping them up to date. Structure matters, too. Clear summaries, descriptive headings, and FAQs help AI understand your content.
Importantly, your website isn't the only source AI uses to understand your brand. Reviews across third-party platforms can also shape how your company is represented in AI-generated answers. Unlike marketing content, those signals reflect the broader customer experience, making reputation and customer satisfaction part of the visibility equation too.
The 621 AEO Playbook: Where to Start
What's the biggest takeaway? AEO doesn't require an entirely new marketing playbook. It requires a broader way of thinking about authority and the companies that build it now will have a head start as LLMs become a bigger part of how buyers discover and vet companies.
For marketing teams, that means focusing on a few high-impact priorities:
Build a strong SEO foundation. Technical SEO, site structure, and search visibility still matter because AI frequently retrieves information from search engines.
Create differentiated content. Invest in proprietary research, customer insights, benchmarks, and expert perspectives that competitors can't easily replicate.
Build authority beyond your website. Reviews, PR, LinkedIn, YouTube, podcasts, and community discussions all contribute to how AI understands your brand.
Prioritize your most valuable content. Instead of producing endless AI-generated articles, maintain and refresh the core pages buyers rely on to make decisions.
Audit your AI visibility regularly. Ask ChatGPT, Gemini, Claude, and Perplexity how they describe your company and your category. Are they recommending you? Is your positioning accurate? Are they surfacing outdated information? As AI models continue to evolve, this should become a regular marketing health check.
Measure visibility differently. Traditional SEO metrics don't tell the whole story anymore. Tools like Profound, Peec AI, and AirOps can help track AI mentions and prompt visibility, but Steve cautions that they're "directional at best." Pair them with broader business signals like branded search growth, direct traffic, AI referral traffic, and customer feedback. One simple change: instead of asking customers, "How did you hear about us?" ask, "How did you learn about us?" to better capture AI-driven discovery.
The goal isn't simply to rank in AI search. It's to become a trusted authority AI recommends. That requires investing in expertise, original insights, and the agility to adapt as AI continues to evolve.
At 621 Consulting, we help companies build the authority that drives visibility, trust, and growth in the AI era. Ready to build a brand AI recommends? Let's talk.
