The high workloads involved in clinical documentation represent one of the major factors contributing to the significant escalation of clinician burnout. The emergence of artificial intelligence (AI) has provided new avenues for relieving this burden by automating certain tasks like clinical documentation through the generation of clinical notes from a transcript of a clinical encounter. The advances in large language models (LLMs) have led to the emergence of such startups, but they come with their own set of challenges, predominantly surrounding the concerns of documentation accuracy, completeness, and data security. These can be addressed with a multi-faceted approach which could include fine-tuning the currently available models; using domain-specific models and in-house AI systems to ensure data security; and involving smaller LLMs and clinicians in the development and implementation of such systems. We can imagine a future where these systems are deeply incorporated into electronic health records, providing not only automated clinical documentation but also improving Clinical Decision Support systems, research, and patient communication.