Abstract: The proliferation of artificial intelligence across digital information systems has precipitated a fundamental restructuring of organic search paradigms. Traditional Search Engine Optimization (SEO), predicated on hyperlink authority and keyword indexing, has given way to increasingly sophisticated retrieval architectures capable of semantic reasoning and generative synthesis. This study systematically examines the structural and functional transformation of organic search through a tripartite comparative framework encompassing SEO, Answer Engine Optimization (AEO), and Generative Engine Optimization (GEO), with the aim of establishing a theoretically grounded model of AI-mediated digital discoverability. A mixed qualitative-comparative research design was employed, integrating systematic secondary data synthesis, multi-platform observational analysis, and comparative thematic evaluation. The analytical corpus comprised 162 units across traditional search engines, answer-based systems, generative AI platforms, SEO content samples, and AI-generated query outputs. Empirical observations reveal a statistically consistent improvement in visibility efficiency, semantic accuracy, and contextual retrieval performance as optimization paradigms evolve from SEO (visibility rate: 78%; semantic accuracy: 72/100) through AEO (84%; 84/100) to GEO (91%; 93/100). AI citation frequency analysis further demonstrates that entity-optimized content achieves citation rates up to 89%, compared with 41% for keyword-focused content. A pronounced behavioral transition from link-navigation (82% traditional) toward direct-answer consumption (87% AI systems) was also documented. The findings substantiate a paradigm shift from keyword-centric retrieval toward context-driven generative intelligence in digital search ecosystems. GEO emerges as the most performant and future-aligned optimization paradigm, while SEO and AEO persist as structurally necessary foundational and transitional layers. Practitioners and researchers must adopt unified AI Optimization (AIO) frameworks to ensure sustained visibility across converging search, answer, and generative AI environments.
Santhosh Kumar Iyappan (Sun,) studied this question.