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.

Instead of relying on notes, record all conference sessions and key conversations. Upload the transcripts into a large language model to create a personal, queryable knowledge base. This 'agent' allows you to instantly recall specific insights and advice long after the event ends.

Always have an AI call recorder like Fireflies active during meetings. The AI can capture and flag critical details that humans might miss in the moment, such as a supplier mentioning an upcoming 8% price hike. These flagged items can be added directly to your dashboard for immediate action.

Most users re-explain their role and situation in every new AI conversation. A more advanced approach is to build a dedicated professional context document and a system for capturing prompts and notes. This turns AI from a stateless tool into a stateful partner that understands your specific needs.

Go beyond using AI for research by codifying your North Star, OKRs, and strategic goals into a personalized AI agent. Before important meetings, use this agent as a 'thought partner' to pressure-test your ideas, check for alignment with your goals, and identify blind spots. This 10-minute exercise dramatically improves meeting focus and outcomes.

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.

Instead of manual note-taking, use AI tools to transcribe and summarize all meetings. This creates a unique, searchable knowledge base from your conversations, which can be leveraged to improve preparation, follow-ups, and decision-making over time.