The subject of the work is the analysis of innovations in information search with a special focus on the role of generative artificial intelligence. The aim is to investigate how generative artificial intelligence affects traditional search systems and to identify the key advantages and challenges it brings. The motivation for studying this subject lies in the growing complexity and amount of information available in the digital age, which creates a need for more efficient and accurate search systems. Generative artificial intelligence offers new possibilities for personalized search, content summarization and solving the problem of information overload, making it a key technology in the information age. The methodology of the work is based on the analysis and presentation of relevant literature and comparisons between traditional search systems and new models that use generative artificial intelligence. Case studies and technological tools were used to gain insights into contemporary trends. The most important results show that generative artificial intelligence significantly improves the user experience through advanced methods of personalization, automation and data analysis, while challenges remain in the ethical segment, security and algorithm bias. The main takeaway is that generative artificial intelligence not only improves information search, but also redefines the way users access information, providing deeper, more accurate results. This opens up space for further innovation, but requires responsible use and development to minimize potential risks.