Build a system where new data from meetings or intel is automatically appended to existing project or person-specific files. This creates "living files" that compound in value, giving the AI richer, ever-improving context over time, unlike stateless chatbots.

Elevate your AI's interpersonal context with an automated workflow. Connect a meeting transcriber to your AI's 'operating system.' A skill can then parse transcripts, extract key points mentioned by colleagues, and automatically update their individual profiles in your '/people' folder for future reference.

Despite having a large staff, Gary found crucial context was lost from meetings. He now uses an AI tool as a "capture all" CRM, sending it photos and notes via text. The AI builds a relationship graph that he then uses to automate follow-ups and maintain connections, essentially scaling his personal memory.

Upload call recordings or transcripts from tools like Gong or Fathom into an AI model. Ask specific questions like, 'Where was the most friction?' to identify disconnects you missed in the moment. Use this insight to craft hyper-relevant follow-ups that address the core misunderstanding.

CEO Brad Jacobs uses AI to automatically take notes and generate summaries from important meetings across his company. This technology provides him with near-instantaneous, unfiltered insights into operations and challenges that previously would have taken months to surface through the corporate hierarchy.

The high-volume feedback during a mastermind "hot seat" can be overwhelming. A simple solution is to record the audio, run it through an AI transcription service, and generate a structured document. This creates an actionable summary, ensuring valuable insights are captured and not lost after the event.

Tools like Granola.ai offer a key advantage by recording locally without joining calls. This privacy, combined with the ability to search across all meeting transcripts for specific topics, turns meeting notes into a queryable knowledge base for the user, rather than just a simple record.

Hoffman argues companies should immediately start recording all meetings and applying AI for summaries and action items. He sees this as a low-hanging fruit for productivity and predicts that within years, not having an AI in your meeting will be considered strange and inefficient.

Use AI on your own process to accelerate client work. Record discovery calls, generate transcripts, and feed them into an LLM. Ask it to identify the highest-value automation opportunities and map out the step-by-step workflow based on the client's own words.