There are more than four billion voice assistants in use worldwide today. Human to human conversations are complex and challenging for AI applications due to the integration of audio streams, scripted vs. unscripted conversations, and various cues such as intonation, body language, and jargon. To overcome these challenges, AI can capture conversations to augment human capabilities, leveraging speed and efficiency in tasks like fact-checking, medical diagnosis, and indexing recorded conversations. However, securing sufficient quality audio and addressing technical issues like speaker diarization remain crucial for accurate transcription and understanding of human-to-human conversations.