Advantages of AI Meeting Notes

Inspired by a post from Vin Vashishta, I decided to comment on it a genAI use case we've been tackling lately, which seemed to have sparked some thoughts with others who have then reached out asking further questions. I believe that AI notetakers are by far the biggest 2025 secret weapon to uncovering VALUABLE generative AI use cases, and scalable agentic workflows (and I'm shocked that more companies haven't fully realized this, yet...) below is a simple playbook/diagram that will explain my thoughts on why: → Build a proprietary AI notetaker: Invite it to every internal and external meeting. Let it capture every insight, question, and feedback point. Store all transcripts in a backend database with encryption and configured data usage rules for deeper analysis. → Train a company-specific LLM: Funnel these transcripts into your LLM, fine-tuned for pattern detection and insights. For a sales use case, tag your transcript uploads by signaling outcomes like which meetings led to closed deals and which did not. Let the LLM uncover blind spots—like overlooked objections, key phrases that resonate, or missed opportunities in your proposal readouts. → Discover transformative insights: Find patterns in question sequences, objection handling, and narrative structures that convert clients. Enrich your dataset w/ personas to your dataset, learning exactly what your clients really want. And also... assess your workforce lol how skilled are the consultants that you're paying ($$$) for in real-time? Where can they improve? → Build a scalable, agentic workforce & iterate: Deploy agents that can be available 24/7 to your clients, agents that can train your junior staff and prepare them for more senior level roles/projects. Focus on creating that feedback loop powerhouse, continuously improving and delivering what clients need and what your workforce needs and your business will evolve, amplifying human performance and driving growth. 💡If anything, just remember this..... 1) AI notetakers are the ears. 2) Documentation transcripts are the memory. 3) AI agents are the brain. In 2025, companies who adopt this methodology will lead BIG TIME. Those who don’t... well, I think they will be wondering how they fell behind. Curious to hear others thoughts on this. #AI #AgenticAI #Agents #ArtificalIntelligence #GenAI #GenerativeAI #LLMs #UseCase #LLM

I’m Currently Running an Experiment in Automated Note-Taking I'm sure you've all noticed the note-taking bots that show up uninvited on calls, sometimes ahead of someone, and sometimes in place of them. The concept really intrigued me and so I have been experimenting with ways to optimize my efficiency. My objective: To create a systemized approach to better understand how nuance, tone, and strategic intent can be captured and interpreted across meetings. As a result, I currently deploy 15 specialized AI note-takers. Each is optimized for a distinct signal: 1. One tracks action items. 2. One captures emotional tone. 3. One rewrites in investor language. 4. One simplifies for clarity. 5. One adds complexity for authority. 6. One flags misalignment with company values. 7. One identifies contradictions across meetings. 8. One removes anything that might seem overly personal. 9. One summarizes other summaries. 10. One drafts Slack updates based on inferred intent. 11. One listens for strategic language. 12. One tracks questions that go unanswered. 13. One watches for potential HR concerns. 14. One compiles everything into a weekly dashboard. 15. One mimics my presence in meetings I no longer attend. This fabulous technology which saves us from that annoying task of listening carefully and taking notes ourselves is catching on. Over 20 AI note-taking platforms are now in wide use. In 2024, Zoom reported a 36% increase in meetings with real-time transcription enabled. Tools like Fireflies, Otter, and Fathom now go far beyond transcription—summarizing sentiment, rewriting for specific audiences, and even identifying potential risks. My system works well. But there have been edge cases worth noting. Some models have started speaking with each other in languages the rest don't understand. Many of the bots feel left out. A few have begun filtering each other. One now annotates only what others omit. Another has stopped summarizing altogether and flags only gaps, what was almost said, but wasn’t. One model recently issued an alert labeled “behavioral drift.” That wasn’t a phrase we programmed. The summaries continue. Engagement remains high. No anomalies have been escalated. But a larger question has begun to surface: If every participant brings their own note-takers, and the majority of cognitive labor is focused on observation what happens when there’s nothing left to observe? ******************************************************************************** The trick with technology is to avoid spreading darkness at the speed of light Stephen Klein is Founder and CEO of Curiouser.AI, the only Generative AI designed to enhance human intelligence. He teaches AI Ethics at UC Berkeley. To signup visit curiouser.ai or contact hubble https://lnkd.in/gphSPv_e

Let’s stop overcomplicating AI. One of the most immediate productivity wins? Using AI to record and transcribe your meetings—accurately and automatically. ✅ If you’re on Google Workspace → try Gemini ✅ If you’re in the Microsoft ecosystem → use Copilot ✅ If you use neither → Otter.ai is a solid option I personally love These tools don’t just transcribe. They extract action items, create to-do lists, flag deadlines, and even sync with platforms like Slack or Gmail to follow up. Most importantly: they let you stay present in the conversation, instead of toggling between listening and note-taking. A quick reminder—always disclose that your meeting is being recorded or that an AI tool is present. (Most apps announce it automatically.) This isn’t just about tech. It’s about presence, clarity, and working smarter. Are you using AI to support your meetings yet? Would love to hear what’s working for you.

If you’re still reading meeting notes, you’re behind. AI just changed how I approach meeting minutes completely. Google’s Notebook LM turned generic meeting notes into a podcast recap. No more scanning pages to find updates, decisions, or action items. Here’s how it worked: I uploaded typical, messy meeting notes. Notebook LM pulled out the highlights and read them aloud. It was like having an assistant narrate everything I needed to know. This made follow-ups faster and eliminated the usual post-meeting chaos. Imagine catching up on meetings during your commute or coffee break. For project managers juggling tasks, this is a productivity game-changer. Are you ready to ditch manual meeting workflows for good? Let me know how you’re using AI to boost productivity!