The future of work is changing rapidly, with knowledge increasingly embedded in conversations rather than documents. In this keynote, I will explore how large language models (LLMs) can boost people’s productivity and creativity by generating natural language suggestions and feedback that align with their context and intent. To do this effectively, LLMs need to be able to leverage relevant content from various sources to ground their responses. People also need to learn new conversational patterns that elicit the full value of LLMs, as the ones that work well among people may not be optimal for LLMs. I will discuss the importance of prompt engineering in productivity contexts, and highlight the value in being able to identify and recommend conversational templates. By leaning into these research topics, there is an opportunity for the Recommender System community to create a new – and better – future of work.