7 common GEO mistakes (and how to avoid them)

Most GEO mistakes do not come from laziness, but from wrong assumptions: teams treat Generative Engine Optimization as a new version of SEO, measure the wrong things, or optimize for one AI assistant and hope the rest will follow. GEO is the discipline that makes sure AI assistants like ChatGPT, Perplexity and Google AI cite your brand in their answers. In this article I walk through the seven pitfalls I encounter most often, each with a concrete way to avoid it.

Want the full framework first? Then read the complete GEO guide for 2026. This article deliberately zooms in on what goes wrong, so you do not have to experience it yourself.

Mistake 1: treating GEO as SEO 2.0

The most fundamental mistake is thinking GEO is just SEO with an AI flavor. It looks similar, but the goal differs. Classic SEO aims for a high position in a list of blue links, which the user then clicks on themselves. An AI assistant does not give a list, but a composed answer, and in doing so chooses which sources it cites. So you are no longer optimizing for a ranking, but for citability.

The consequence of this mistake is that teams stay stuck in keyword density and link building, while AI models mainly look for clear, factual and well-structured passages they can take over directly. SEO and GEO do share a foundation, because a page that is technically findable and well written helps both. Avoid the pitfall by seeing GEO and SEO as complementary disciplines, not as a replacement. You can read the difference in detail in GEO vs SEO.

Mistake 2: not doing a baseline measurement

The second mistake is starting to optimize without knowing where you are starting from. Many companies have no idea whether an AI assistant ever mentions them, for which questions, or which competitor appears in their place. Without that baseline measurement, every intervention is blind guessing: afterward you cannot prove whether anything worked.

The solution is to establish a baseline before you change anything. Compile your most important questions, submit them to each major AI platform, and note each time whether you are cited and who else appears. Repeat each question a few times, because AI answers vary from one time to the next. Record that baseline measurement somewhere with a date, so that in three months you can demonstrate real progress instead of a feeling. How to approach this systematically I describe in the complete GEO audit.

Mistake 3: not making content extractable

The third mistake lies in the structure of your pages. A lot of B2B content starts with a long run-up, an anecdote or a marketing sentence, and only in paragraph four does the actual answer arrive. An AI model that is quickly looking for a citable passage then drops off. It simply cannot find your answer or extract it neatly from your text.

Avoid this by writing answer-first: place under every heading a complete, self-contained sentence that answers the question immediately, and only then build out. Use headings phrased as the question your reader (and the AI) asks. Work with short paragraphs, lists and clear definitions. You can read more about that structure in content architecture for AI extraction.

Mistake 4: inconsistent brand and entity data

The fourth mistake is presenting your brand just slightly differently on each channel. One source calls you a “marketing agency”, another a “data agency”, your company name appears somewhere with and elsewhere without its legal form, and your location details do not line up. AI models build an image of who you are based on all those signals combined. When they contradict each other, trust drops and you are cited less quickly as a reliable source.

The solution is to enforce consistency: use the same name everywhere, the same description of what you do, and the same core data. The stronger and more unambiguously recognizable you are as an entity, the easier it is for a model to place you correctly. I work out this principle in entity consistency and AI visibility.

Mistake 5: chasing backlinks instead of brand mentions

The fifth mistake is carrying over the old SEO reflex: collecting as many backlinks as possible and thinking that determines your AI visibility. Links remain useful, but for AI models brand mentions often weigh more heavily. What matters is how often and in what context your brand is mentioned in reliable texts, even without a clickable link.

Avoid the pitfall by shifting your attention to presence in relevant conversations: specialized media, comparison articles, independent reviews and communities where your target audience is. A brand that pops up everywhere in the right context is recognized as an authority by AI. I explain the difference in brand mentions over backlinks.

Mistake 6: optimizing for one platform

The sixth mistake is assuming that all AI assistants work the same way. They do not. ChatGPT, Perplexity and Google AI draw from different sources, cite differently and place different demands on your content. Whoever optimizes only for their favorite assistant builds in a blind spot for the places where their target audience might just be searching.

Avoid this by examining your visibility platform by platform and tuning your approach to it. Start with the assistant where your buyers are most often, but do not ignore the others. A good starting point is getting found in ChatGPT, and from there you expand to the other platforms.

Mistake 7: steering on vanity metrics

The seventh mistake is celebrating the wrong things. A rise in the number of mentions feels good, but if those mentions produce no leads or revenue, you are measuring busyness instead of impact. I see teams proud of “we are being mentioned now”, without knowing whether a single qualified inquiry ever came out of it.

The solution is to link your AI visibility to business results. Look at the traffic that comes from AI sources, at the quality of the inquiries, and at whether the people who find you via an AI answer actually match what you sell. Are you cited for questions that matter commercially, or only for broad informational use? That is the distinction between a nice report and real growth. This is exactly why we always steer clients on leads and revenue, not on vanity metrics.

The short summary

The seven GEO mistakes revolve around one common thread: do not treat GEO as an extension of old SEO reflexes. Concretely that means: see GEO as a discipline of its own, first record a baseline measurement, write answer-first and extractable, keep your brand data consistent, invest in brand mentions, optimize platform by platform, and measure on leads and revenue instead of on mentions. Whoever avoids these pitfalls builds AI visibility that actually delivers something.

Want to know which of these mistakes your company makes today? See how we help you get found in ChatGPT, or schedule your free intake.