You've built a website. You've invested in SEO. You show up reasonably well on Google. But ask ChatGPT or Perplexity about your industry, your services, or even your business name, and you're nowhere to be found. Why?
AI answer engines don't work like Google. They have different requirements, and most business websites — even well-optimised ones — fail to meet them. Here are the six most common reasons.
1. No schema markup
Schema markup is structured data that tells search engines and AI systems exactly what your content is about. Without it, an LLM has to infer your business type, services, and authority from unstructured text — and it often gets it wrong or doesn't bother.
Most business websites have zero schema markup. Even websites with decent SEO often lack the specific types that AI engines prioritise: FAQPage, HowTo, Article, Organization, Service, and LocalBusiness. Adding these schemas is one of the fastest and highest-impact GEO improvements you can make.
2. Weak entity signals
AI engines are more likely to cite sources they "know" — brands that are clearly established as named entities with consistent information across the web. If your business name, description, and details are inconsistent across your website, social profiles, Google Business Profile, and directory listings, the LLM can't reliably establish who you are.
Entity establishment means ensuring your brand is described consistently everywhere it appears online, your Organization schema links to your social profiles, and there are external references to your business from credible sources.
3. Thin or unstructured content
AI engines cite content that directly and clearly answers questions. If your pages are short, vague, or structured as marketing copy rather than informational content, they're unlikely to be cited in AI responses.
Pages that perform well in AI search typically have clear question-based headings (H2s that read like questions), direct answers at the start of each section, specific details rather than generalities, and a logical structure that an LLM can parse cleanly.
4. No E-E-A-T signals
Experience, Expertise, Authoritativeness, and Trust — the four signals that both Google and AI engines use to assess citation worthiness. Most business websites demonstrate almost none of these signals explicitly.
E-E-A-T signals include named authors with verifiable credentials, case studies with specific outcomes, client testimonials from real people, references to industry experience, methodology descriptions, and external sources that reference your expertise. Without these, your site looks like every other generic business website to an LLM.
5. No answer-ready content
AI engines are answer machines. They look for content that directly answers the questions their users are asking. If your website doesn't contain content structured as answers to real questions in your industry, you're missing the primary mechanism by which AI engines select citations.
Adding an FAQ section to key pages, creating "what is" and "how to" content in your niche, and restructuring existing pages to lead with direct answers are all high-impact improvements that most businesses can implement quickly.
6. Poor content freshness
AI engines, particularly those with live web access, favour content that is current. If your key pages haven't been updated in years, or your site has no recent content at all, you'll be deprioritised in favour of more recently updated sources.
A blog with regularly published, substantive content is one of the most effective ways to signal freshness to AI engines. It also gives you ongoing opportunities to create the kind of answer-ready, definitional content that drives citations.
The fix
The good news is that all six of these issues are fixable. A GEO audit will identify exactly which signals you're missing and prioritise them by impact. Many of the highest-impact fixes — schema markup, FAQ content, entity signals — can be implemented within days. The longer-term work of building content depth and authority compounds over months and years.