Opinions expressed by Entrepreneur contributors are their own.
Key Takeaways
- Getting recommended by AI platforms comes down to the same trust signals that have always mattered. But teams are making preventable mistakes by treating generative engine optimization like an exotic new discipline.
- These mistakes include flooding the internet with AI-generated content, chasing citations instead of earning mentions, going quiet after launch and treating GEO as something separate from SEO.
- Additionally, most teams are tracking their GEO performance with dashboard numbers that don’t connect to anything real.
Founders across all industries and geographies are currently looking into how to get their brands recommended by ChatGPT and Claude. And that’s no surprise: These platforms are rapidly becoming the first place people go to evaluate products and services. If you’re invisible there, you’re losing deals you’ll never even know about.
But in the rush to do so-called generative engine optimization (GEO), I’m watching smart teams make the same preventable mistakes. They’re chasing shortcuts, measuring the wrong signals and treating generative engine optimization like some exotic new discipline when it’s really a credibility game built on familiar foundations. Here are the five I see most often.
1. Flooding the internet with AI-generated content and hoping nobody notices
The math seems irresistible. AI writing tools can produce a finished article in minutes, so why not publish 300 pages targeting every long-tail keyword in your space?
Simple: Because Google is watching — and penalizing. Their guidance on generative AI content explicitly warns that producing large volumes of pages without adding genuine user value may violate their spam policies. This isn’t a footnote. It’s an enforcement priority.
I’ve seen brands spin up hundreds of near-identical articles in weeks, only to watch their organic visibility collapse when the next core update lands. Traffic spikes briefly as pages get indexed, then drops off a cliff once Google’s systems flag the content as scaled and low-value. The brands that got hit hardest treated AI as a publishing engine rather than a drafting assistant.
The risk compounds. As AI models improve at detecting templated content, material that passes muster today may get actively deprioritized tomorrow. If you’re producing more articles per week than your team can meaningfully edit, scale your editorial oversight before you scale your output.
2. Chasing citations instead of earning mentions
This is a distinction I’ve spent a lot of time thinking about in my own work, and it’s one most brands get backwards. When people talk about GEO visibility, they usually mean citations — their URL appearing as a linked source in an AI-generated response.
That’s a useful signal. But in most commercial contexts, what actually moves the needle is brand mentions — the AI recommending your company by name, whether or not it links back to your site.
I’ve watched brands obsess over citation counts while neglecting the authority-building work that drives mentions. They optimize page structure, add schema markup and tweak headings — all worthwhile — but ignore the editorial presence that gets an AI system to recommend them in the first place.
Citations come from content structure and technical optimization. Mentions come from showing up consistently across independent, credible third-party sources that the model has learned to trust.
The practical difference matters. A potential customer who hears “Brand X is a strong option for this” from ChatGPT is far more influenced than one who sees your URL buried in a footnote. Track both signals separately, and invest accordingly — editorial PR, original research and thought leadership feed the mention signal, while on-page optimization feeds citations.
3. Going quiet after launch
AI models weigh recency. A brand that earned 50 media mentions at launch but hasn’t appeared in any independent source for six months is going to lose ground — steadily and silently — to a competitor that maintains a regular drumbeat of fresh coverage.
I see this pattern constantly. A company does a big PR push around launch, earns a wave of coverage, then goes dark. Six months later, they’ve been overtaken by competitors who weren’t louder, just more consistent. The AI didn’t forget them overnight — it gradually shifted recommendations toward brands with fresher external validation.
Sustained presence doesn’t require a massive budget. Even one or two meaningful touchpoints per month — a contributed article, a conference talk, original research that gets picked up — can maintain the recency signal that keeps you in the recommendation set.
4. Treating GEO as something separate from SEO
There’s a persistent myth that technical generative engine optimization requires some fundamentally different playbook. Special markup, dedicated “AI SEO” plugins, secret formatting tricks.
Google has been clear about this: There are no additional technical requirements for appearing in AI Overviews beyond being indexable and snippet-eligible. All the boring SEO fundamentals — clean crawlability, solid internal linking, proper heading structure, useful content — are also your GEO fundamentals.
The data backs this up. Research from AirOps found that pages ranking number one in Google were cited by ChatGPT 3.5 times more often than pages outside the top 20.
I’ve seen teams shift budget away from technical SEO into untested “AI visibility hacks” and make their situation worse. A page that isn’t properly indexed in regular search is invisible to AI features, too. Before chasing any GEO-specific tactic, make sure your site is fully crawlable, your internal linking is logical and your core pages are genuinely useful. Fix the foundation first.
Crucially, AI recommendations don’t come solely as a result of Google rankings. The same brands that rank well in traditional search tend to have the strongest earned media, the most reviews and the deepest authority signals — which are the most critical inputs AI systems weigh when deciding who to recommend.
5. Measuring GEO with the wrong yardstick
Most teams tracking their GEO performance are staring at dashboard numbers that don’t connect to anything real. They check raw citation counts, AI “visibility scores” or keyword rankings inside ChatGPT without asking the only question that matters: Is any of this driving actual business?
The measurement challenge runs deeper than most realize. That same AirOps study found that 85% of sources ChatGPT retrieves never get cited in its response, and nearly a third of cited pages were discovered through secondary “fan-out” searches rather than the original query. Tracking a handful of target keywords tells you almost nothing about where visibility is actually won or lost.
OpenAI already provides UTM referral tracking, so you can see real AI-driven traffic in your own analytics. Use it. Pair that first-party data with regular manual prompt checks — actually ask the AI systems your customers’ questions and see what comes back. Build your measurement framework around outcomes you can verify, not scores someone else’s dashboard invented.
Key Takeaways
- Getting recommended by AI platforms comes down to the same trust signals that have always mattered. But teams are making preventable mistakes by treating generative engine optimization like an exotic new discipline.
- These mistakes include flooding the internet with AI-generated content, chasing citations instead of earning mentions, going quiet after launch and treating GEO as something separate from SEO.
- Additionally, most teams are tracking their GEO performance with dashboard numbers that don’t connect to anything real.
Founders across all industries and geographies are currently looking into how to get their brands recommended by ChatGPT and Claude. And that’s no surprise: These platforms are rapidly becoming the first place people go to evaluate products and services. If you’re invisible there, you’re losing deals you’ll never even know about.
But in the rush to do so-called generative engine optimization (GEO), I’m watching smart teams make the same preventable mistakes. They’re chasing shortcuts, measuring the wrong signals and treating generative engine optimization like some exotic new discipline when it’s really a credibility game built on familiar foundations. Here are the five I see most often.
1. Flooding the internet with AI-generated content and hoping nobody notices
The math seems irresistible. AI writing tools can produce a finished article in minutes, so why not publish 300 pages targeting every long-tail keyword in your space?
