This systematic literature review analyzes how Generative AI (GenAI), spe-cifically Large Language Models (LLMs), is impacting Product Management (PM). Based on recent peer-reviewed studies, our analysis reveals that cur-rent research is in a nascent, experimental phase, focusing on task-level ap-plications like requirements engineering and user persona generation, which show potential for efficiency gains. However, the field suffers from critical limitations, including a lack of methodological standardization, an over-reliance on a narrow range of LLMs like ChatGPT, and a focus on superfi-cial efficiency metrics over measures of product success. We conclude that while LLMs show promise for discrete PM tasks, their true transformative potential lies in integrated, workflow-centric systems. This paper provides a baseline of the current research and calls for a more rigorous, impact-focused agenda for future studies.