From Noise to Narrative: Building Reputation in AI Dominated Search and Discovery Environments

This review article examines the profound transformation of digital reputation in an era that is increasingly dominated by Large Language Models (LLMs) and generative search engines. It describes the technological shift from a user-driven, keyword-based search to the AI-mediated, synthesized narratives, analyzing the architectural and philosophical break this shift represents. The article reviews literature from the disciplines of computer science, communication studies, psychology, and marketing to explore how core principles of online authority, such as discoverability, brand control, and trust, are being redefined. We found that traditional reputation signals, such as those encapsulated in Google's E-E-A-T framework, are being repurposed in order to establish algorithmic trust, rather than direct user trust. This creates a new layer of abstraction where the AI agent becomes the primary judge of credibility. Through an analysis of emerging strategies of Generative Engine Optimization (GEO), the evolving role of digital PR, and the significant risks of algorithmic bias and AI hallucination, this paper argues that digital reputation is transitioning from a human-centric social construct to a complex socio-algorithmic one. It is no longer shaped solely by direct engagement with content, but instead it is actively curated, deconstructed, and reassembled by autonomous AI intermediaries, fundamentally modifying the strategies required to build and protect brand identity online.