Abstract: The recent breakthroughs in large language models (LLMs) have unlocked the ability for Recommender Systems (RS) not only to be interfaced with via natural language interfaces, but also to be more dynamic and interactive. These advancements mean that RS can now cater to multifaceted user intents, transitioning between recommendations based on the evolving context of a conversation. However, integrating LLMs with RS introduces unique challenges. Achieving a balance between computational demands, prompt response times, and ensuring data privacy, all while maintaining recommendation relevance, remains a pivotal issue. In this talk, we examine its potential, challenges, and envisage a roadmap for the future. Through this exploration, we hope to provide insights and directions for future research in this space.