Imagine stepping into a high-stakes earnings call, where every word matters. You're facing a barrage of questions from analysts eager to dissect your company's performance. How do you ensure your answers are not just accurate but also persuasive and insightful? This is the challenge addressed by innovative research that explores how AI can help company managers rehearse for these critical Q&A sessions. The core idea is to use causal knowledge graphs, essentially maps of cause-and-effect relationships between financial concepts, to guide large language models (LLMs). These graphs help LLMs connect the dots between questions and the underlying financial realities they represent, enabling them to generate more informed and concrete responses. The researchers experimented with three types of causal knowledge graphs: one based on a company's annual reports, one derived from earnings call transcripts, and a unique graph built from analysts' reports. This last graph proved particularly interesting, as it allowed LLMs to take on the perspective of the analysts, essentially anticipating the kind of detailed answers they would be looking for. This perspective-taking approach led to significant improvements in the quality and persuasiveness of the AI-generated answers. The results are promising. LLMs, when guided by these knowledge graphs, can generate answers significantly richer in information than typical human responses. However, there's room for improvement in how these models incorporate hard numbers and empirical data to make their answers truly concrete and compelling. Future research aims to refine this approach, enabling AI to not only provide informative responses but also support them with the detailed evidence that analysts crave. This could transform earnings calls from potentially stressful interrogations into showcases of insightful communication, driven by the power of AI.