OPTIMIZATION OF GENERATIVE ENGINE IN TRANSFORMING DIGITAL VISIBILITY IN AI SEARCH - Andhi Febisatria (ANALISIS, 2026)
This study employs a Systematic Literature Review (SLR) approach to identify and synthesize the development of the Generative Engine Optimization (GEO) concept based on literature published between 2020 and 2026, sourced from Google Scholar using a PRISMA-guided selection procedure. The findings indicate that GEO represents a shift from ranking-based optimization toward optimizing content representation within generative AI responses, emphasizing the likelihood of content being selected, summarized, and presented by AI-driven systems. Implementation has evolved from simple textual adjustments to semantic approaches, intent modeling, multimodal integration, and cross-query consistency. The application of GEO enhances product visibility in AI-generated answers and influences consumer perceptions, while also presenting risks related to bias, information manipulation, and ethical challenges in AI-based digital ecosystems.