For over two decades, search engine optimization (SEO) has driven web marketing, but the rise of generative artificial intelligence (AI) systems now disrupts this by providing direct answers instead of link lists. Fortunately, much of what helps content rank well on search engines remains effective with generative AI, though marketers must adapt strategies to optimize visibility on platforms like ChatGPT and Gemini, says AI engine optimization expert Kevin Roy, CEO of GreenBanana SEO. Since May 2024, Google’s AI Overview summaries began appearing atop search results in about 30% of U. S. queries, causing significant declines in website click-through rates, according to Roy. TheCUBE Research predicts buyer preferences will shift heavily toward AI assistants over traditional search engines for research and vendor comparisons by next year, with AI expected to drive over 70% of B2B software research by 2030. Scott Hebner, principal AI analyst at theCUBE Research, explains that this decline stems from zero-click behavior, where AI systems generate concise answers without directing users to websites. As a result, traditional SEO loses visibility and control to AI engines that decide which brands to include in synthesized responses. Generative AI platforms excel as discovery tools, especially for research and professional queries, by saving users time with direct answers rather than links. While search engines measure relevance and authority through links, generative AI relies on structured data, citations, and entity relationships, Roy notes. Research shows the average domain age of ChatGPT’s referenced sources is 17 years, indicating a bias toward established, consistent entities over newer or disorganized sites. Companies focusing only on link-based SEO risk invisibility in AI-generated answers despite maintaining good search rankings. Though both search and AI engines seek similar qualities, they evaluate them differently. As Don Dodds, founder of M16 Marketing, writes, AI favors “entity recognition” — machine-readable identities, semantic connections across authoritative platforms, and consistent brand context — prioritizing entities such as people, places, and organizations over text keywords and backlinks. Visibility now hinges on how comprehensively an AI system understands a brand’s identity, purpose, and credibility.

Roy describes AI’s goal as holistic comprehension of a brand’s significance in a subject area. Entity Authority Engineering (AEO), Roy’s framework, involves two key components: content structure and authority. AI systems provide concise, synthesized answers, so content must be clearly organized—with short Q&A formats and comprehensive coverage—to enable quick, accurate data extraction. FAQs, long a SEO staple, are now essential. Authority extends beyond individual pages, requiring consistent recognition of entities across credible sources. Structured data, especially schema markup in standardized JSON format, helps AI interpret key elements like authors, organizations, products, and publication dates unambiguously. Roy emphasizes schema has shifted from optional to essential. Authorship also matters greatly; content should be linked to verified authors with dedicated profiles connecting to high-authority external sources such as professional networks, media mentions, and industry publications. This “entity stacking” confirms credibility and aids AI trust. Roy’s broader AEO approach trains AI systems to identify and trust a brand by maintaining consistent structured data online, mapping mentions across authoritative sources, and testing performance across multiple AI models rather than focusing on one. While AEO does not replace SEO fundamentals, it redefines tactics—from ranking in search results to becoming an entity AI deems worthy to cite. Roy concludes the divide is clear: those embracing structure, schema, and real authority will succeed, while shortcuts no longer work. In this evolving landscape, optimizing for AI recognition is critical to maintaining digital visibility and influence.