The integration of large language models (LLMs) into search engines has driven the emergence of AI Overviews, artificial intelligence (AI)-generated summaries that provide immediate answers within search engine results pages (SERPs). These new features, spearheaded by companies such as Google and Microsoft, represent a paradigm shift from traditional organic search (SEO). Rather than prioritizing links, search systems synthesize information from multiple sources, redefining visibility metrics and altering use behavior. Within this context, the concept of Generative Engine Optimization (GEO) has emerged, referring to the strategies developed by marketing professionals to optimize content so that it is selected and presented by AI systems. In contrast to conventional SEO, GEO places greater emphasis on semantic relevance, content quality, and adherence to E-E-A-T principles (experience, expertise, authoritativeness, and trustworthiness), as well as on providing a clear structure that facilitates information extraction by AI. Furthermore, GEO introduces new practices such as using structured data, optimization for conversational queries, and continuous content updating. This approach has significant implications for visibility, web traffic, competitive research, and user experience. While challenges remain regarding reliability, economic impact, and the consolidation of dominant players, GEO constitutes a key strategic evolution. Understanding and applying its principles will be essential for maintaining the relevance of brands and content in a digital ecosystem increasingly shaped by AI.