In this section I review one AI-powered application and demonstrate how it can be used to create value. I include concrete examples and lessons learned from actual use, and only recommend tools that I have used extensively myself.
When it comes to "talking to your data" Google NotebookLM is one of the most powerful tools to consider. It's a research and note-taking tool that lets you discover or upload documents (PDFs, Google Docs, web pages, YouTube videos, audio files) and then interact with that content through natural language. You can ask questions, request summaries, find connections across sources, and generate insights - all grounded in the specific materials you've provided.
What makes NotebookLM particularly valuable is that it works only with your uploaded sources. Unlike asking ChatGPT or Claude a general question, where you might get hallucinated information, NotebookLM restricts itself to the documents you've given it and cites its sources. This makes it extremely valuable for professional work where accuracy matters.
I use NotebookLM regularly for several types of work, and I've recommended it to numerous clients facing similar challenges.
When preparing for client engagements or writing newsletter content, I often need to understand a topic deeply by reviewing multiple articles, reports, and case studies. Instead of reading each document linearly and taking notes, I upload them all to NotebookLM and ask questions like: "What are the common themes across these sources about AI implementation challenges?" or "How do the recommendations in these three reports differ?"
NotebookLM identifies patterns, highlights contradictions, and surfaces insights that would take me much longer to find manually. It cites which document each point comes from, so I can verify or dive deeper when needed.
Several clients have used NotebookLM to analyze customer interview transcripts or internal meeting notes. Upload 15-20 customer interviews, then ask: "What are the top three pain points mentioned across these conversations?" or "Which customers mentioned pricing as a concern, and what specifically did they say?"
This is particularly powerful for product teams doing customer research. Instead of manually tagging and categorizing feedback, they can quickly explore patterns and drill into specific themes. The time saved is significant, but more importantly, it makes thorough analysis feasible where before teams might have only skimmed the surface.
When understanding a new market or competitive landscape, I collect relevant reports, competitor websites, industry analyses, and news articles. NotebookLM lets me ask comparative questions: "How do these three competitors position their AI offerings?" or "What trends are mentioned across these industry reports?"
The tool excels at this kind of synthesis work - finding common threads, identifying outliers, and helping you understand the landscape without reading every source word-for-word.
One of NotebookLM's standout features is its ability to generate an "Audio Overview" - a podcast-style conversation between two AI hosts discussing your sources. I've used this when I need to absorb material but don't have time to read. Upload your documents, generate the audio, and listen while commuting or exercising.
The quality is remarkably good. The AI hosts discuss key points, make connections, and present the material in an engaging format. It's not a replacement for deep reading when that's needed, but it's excellent for getting oriented to new material or refreshing your memory on sources you've reviewed before. With the new and fun interactive mode, you can even join the conversation and ask follow-up questions while listening.
Strategy and consulting: Synthesize client documents, industry reports, and competitive analyses to prepare recommendations or identify key insights.
Product management: Analyze customer interview transcripts, support tickets, and user feedback to identify feature priorities and pain points.
Sales and business development: Research prospects by gathering their public materials, earnings reports, press releases, and industry context into one notebook.
Legal and compliance: Review multiple policy documents, regulations, or contracts to find specific clauses or understand differences across versions.
Learning and development: Collect training materials, research papers, or course content and use NotebookLM as a study companion that can answer questions and explain concepts.
NotebookLM is excellent for research and synthesis, but it has boundaries. It doesn't connect to live databases or real-time data sources - you upload static documents. It's not designed for data analysis in the structured sense (use other tools for SQL-style queries on databases). And while it cites sources, you should still verify important claims, especially for high-stakes decisions.
The tool works best when you have clear questions and specific sources. It's less useful for open-ended exploration where you're not sure what you're looking for yet.
For a complete walkthrough of NotebookLM's October 2025 updates, including the new customization features, learning guide mode, and all the multimedia capabilities, I recommend this episode from the Everyday AI podcast: NotebookLM October 2025 Complete Walkthrough. Host Jordan Wilson does an excellent job demonstrating the new conversational customization options, the interactive learning guide, and shows how all the features work together - from mind maps and quizzes to the audio and video overviews. He also covers what's coming next for the platform, including confirmed features like Nano Banana integration for infographics and the upcoming API. It's a practical, hands-on guide that goes beyond the basics.