AI search optimization is quickly becoming a priority for marketers, but the biggest mistake is treating every AI answer engine as if it works like Google—or like every other AI platform. A useful breakdown from Rita Cidre at Semrush makes the case that visibility in ChatGPT, Google AI Overviews, Google AI Mode, and Perplexity is far more fragmented than most SEO playbooks assume.
The practical takeaway is not that SEO is dead or that every team needs a separate “GEO” department. It is that search visibility now has two layers: making your site technically accessible and authoritative, then earning citations and recommendations across a wider web of sources.
AI search optimization is platform-specific
Cidre’s video highlights an eye-opening finding from Semrush testing: when the same SaaS-related queries were run across several AI platforms, most cited URLs appeared on just one platform. In the example used in the video, ChatGPT and Perplexity cited dozens of sources for a payroll-software query but shared only one URL.
That result should change how brands interpret AI visibility reports. A citation in ChatGPT is a positive signal, but it is not proof that a company will appear in Google AI Mode, AI Overviews, or Perplexity. Each product can use different retrieval systems, ranking signals, interfaces, and source-selection behavior.
The underlying mechanics also support that conclusion. OpenAI says ChatGPT Search can rewrite a user’s request into one or more targeted searches and may partner with third-party search providers. Google’s AI features are rooted in Google Search’s ranking and quality systems. Perplexity, meanwhile, operates its own crawler infrastructure and offers real-time web search capabilities. Those differences do not guarantee entirely separate source pools for every query, but they make a one-platform-fits-all strategy unrealistic.
For marketers, this means measurement needs to become comparative. Do not ask only, “Do we rank?” Ask:
- Is our brand mentioned in each relevant AI platform?
- Is our own site cited, or are third-party pages doing all the talking?
- Is the description accurate, current, and differentiated from competitors?
- Which publishers, communities, review sites, and forums repeatedly appear alongside our category?
- Which competitors show up in AI answers despite weaker traditional rankings?
Why page-one rankings do not guarantee AI citations
Traditional Google rankings still matter, but they are no longer a complete proxy for discovery. Semrush’s cited research found limited overlap between Google’s top 10 organic results and pages cited in AI-generated answers, with the degree of overlap varying widely by platform.
The important concept here is query fan-out. An AI system can interpret a prompt such as “best payroll software for startups” as a bundle of needs rather than a single keyword. It may retrieve material about compliance, onboarding speed, pricing models, integrations, user reviews, data security, international payments, or industry-specific use cases before composing an answer.
That creates an opportunity for sites that are not the obvious winner for the head term. A company might rank outside the top three for “best payroll software,” yet still earn a citation because it has the clearest guide to payroll compliance for small businesses or the most useful comparison of setup requirements.
Google itself advises site owners to focus on unique, valuable content that meets users’ needs, noting that people use AI experiences for longer, more specific, and follow-up questions. In other words, the content model is shifting from one landing page targeting one keyword toward a connected body of evidence that answers the surrounding decision journey.
A stronger content plan should therefore map each commercial topic into sub-questions. For a B2B SaaS company, that could include implementation, integrations, security, pricing, migration, customer support, ROI, alternatives, and use cases by company size. The goal is not to publish thin articles for every variation. It is to create genuinely useful pages that provide clear answers AI systems can retrieve and cite.
Technical SEO remains the foundation of AI visibility
One of the most valuable points in Cidre’s explanation is also the least flashy: AI search does not eliminate technical SEO. If crawlers cannot find, render, and index your content, your site has a much smaller chance of being selected as a live web source.
Google’s documentation is explicit that its AI features do not require special AI-only markup or a separate technical standard. The same foundational practices still apply: Google must be able to crawl and index the page, the page must be eligible to appear in Search, and structured data must match visible page content.
That should be reassuring. Before chasing new acronyms, fix the basics:
- Confirm crawl access. Review
robots.txt, meta robots directives, authentication walls, and CDN or firewall rules that may unintentionally block legitimate crawlers. - Make essential content render reliably. Do not hide product facts, comparisons, pricing context, or answers behind JavaScript experiences that fail to load consistently for bots or users.
- Improve internal linking. Important pages should be easy to reach through logical site architecture, not buried several clicks deep or orphaned from the rest of the site.
- Keep pages fast and useful. Performance alone will not earn a citation, but slow, unstable pages create friction for crawling and for people who do click through.
- Use accurate structured data where it fits. Product, organization, article, FAQ-related content where eligible, review, and discussion markup can help search engines understand a page—but it must reflect what users can actually see.
Perplexity’s crawler guidance also shows why a broad technical audit matters. Website owners need to understand which crawlers access their domains and how their infrastructure handles them, rather than assuming Googlebot access covers every AI-related retrieval path.
Your brand’s AI footprint extends beyond your website
AI search optimization is partly an on-site content challenge, but it is also a reputation and distribution challenge. AI tools often surface review platforms, editorial coverage, Reddit threads, forums, directories, comparison sites, documentation, and other third-party sources alongside—or instead of—a brand’s own pages.
This is especially important for product recommendations. A polished vendor page can explain what a product claims to do. Independent reviews and real customer discussions can help an AI system corroborate whether the product is trusted, who it is best for, and where its limitations are.
That does not mean teams should spam Reddit, manufacture reviews, or chase low-quality mentions. Those tactics create reputational risk and rarely build durable authority. Instead, invest in sources that deserve to rank and be cited:
- Keep product listings, documentation, pricing, and company profiles current.
- Encourage authentic customer reviews on platforms relevant to your category.
- Give subject-matter experts opportunities to contribute useful, attributable insights to reputable industry publications.
- Participate in communities by solving real problems rather than dropping promotional links.
- Publish clear comparison and alternatives content that acknowledges trade-offs honestly.
The strategic shift is simple: your website is no longer the only page you are optimizing. Your brand is the entity users and AI systems encounter across the web.
Build an AI search optimization workflow, not a guessing game
Because AI outputs can change by prompt, location, personalization, and product updates, one-off searches are not enough. Build a recurring visibility review around the categories and questions that influence revenue.
Start with a small, repeatable set of prompts: category queries, “best for” queries, alternatives, comparisons, implementation questions, and problem-led questions. Run them across the AI platforms that matter to your audience. Record whether the brand is mentioned, whether it is cited, which URL is used, what competitors appear, and which external sources influence the response.
Then connect those observations to actions. Missing citations may point to a technical issue, weak topical coverage, stale product information, poor third-party validation, or simply a platform-specific preference that requires more testing. Avoid overreacting to a single answer; look for patterns across repeated prompts and over time.
The future of AI search optimization is broader SEO
The lesson from Semrush’s analysis is not that Google rankings have stopped mattering. It is that rankings are now one input into a broader visibility system. AI platforms retrieve, compare, synthesize, and cite information differently, so brands that rely on a single keyword position or a single channel will have blind spots.
The most resilient approach combines technical SEO, deep topic coverage, accurate structured information, and credible third-party brand presence. Treat AI search optimization as an extension of modern SEO: make your information accessible, make it genuinely useful, and make sure the wider web has compelling evidence that your brand belongs in the answer.