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.

Use an AI assistant like Claude Code to create a persistent corporate memory. Instruct it to save valuable artifacts like customer quotes, analyses, and complex SQL queries into a dedicated Git repository. This makes critical, unstructured information easily searchable and reusable for future AI-driven tasks.

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.

The foundation of an AI-native company is a "brain"—a central context layer where all company information (SOPs, meeting notes, emails) is captured, curated, and structured. This makes the company's knowledge "readable" to AI agents, giving them the perfect vision to execute tasks.

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.

By the end of 2026, recording every meeting and applying AI agents to transcribe, summarize, assign action items, and align with strategy will be table stakes. Hoffman argues that companies not doing this will be making excuses, akin to sticking with horse-drawn carriages in the age of the car.

A hedge fund is recording nearly all internal meetings to create a "data lake" of unstructured information. This proactive data strategy aims to build a future-proof asset—a "collective"—that can be queried by AI to uncover insights, understand decision-making history, and predict future trends.

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.

A key practice at OpenLoop is making all meeting transcripts available to everyone in the organization. This radical transparency creates a massive, shared knowledge base that can be queried by AI systems, allowing employees to access information and context from meetings they didn't attend.