Using Mind Mapping for Project Planning

Google just dropped Mind Maps for NotebookLM, and it’s a must-have AI tool for every PM. Here’s how it works and why you’ll love it: As a PM, we’re constantly juggling with: - Research: Customer interviews, competitor analysis, market trends. - Ideation: Brainstorming features, roadmaps, and strategies. - Documentation: PRDs, meeting notes, and stakeholder updates. What if you could turn all of this chaos into clear, actionable visual diagrams in seconds? 𝗧𝗿𝘆 𝗚𝗼𝗼𝗴𝗹𝗲’𝘀 𝗡𝗼𝘁𝗲𝗯𝗼𝗼𝗸𝗟𝗠 - 1. Go to NotebookLM (Google’s AI-powered note-taking tool). 2. Add Your Sources:   - PDFs, text, markdown, audio.   - Paste copied text.   - Website & YouTube links.   - Google Docs & Slides from Drive. 3. Click “Mind Map” when ready. 4. Your Mind Map is ready. Instantly visualize connections, themes, and insights. Here are 5 use cases, you can use it to save hours and work smarter: 👇 1. 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵:   - Upload interview transcripts or survey data.   - Instantly map pain points, themes, and opportunities.   - Example: “Visualize recurring customer frustrations from 20 interview transcripts.” 2. 𝗖𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀:   - Add competitor product docs, reviews, or website content.   - Create a mind map of their strengths, weaknesses, and gaps.   - Example: “Map out Competitor X’s feature set vs. ours to identify differentiation opportunities.” 3. 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗜𝗱𝗲𝗮𝘁𝗶𝗼𝗻:   - Brainstorm new features or improvements.   - Turn messy ideas into a structured roadmap.   - Example: “Create a mind map of potential features for our next sprint.” 4. 𝗠𝗲𝗲𝘁𝗶𝗻𝗴 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝗲𝘀:   - Upload meeting notes or recordings.   - Visualize key decisions, action items, and dependencies.   - Example: “Turn a 1-hour stakeholder meeting into a clear, actionable mind map.” 5. 𝗣𝗥𝗗𝘀 & 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻:   - Organize complex requirements into a visual flow.   - Example: “Map out user flows and acceptance criteria for a new feature.” P.S. How would you use Mind Maps for NotebookLM in your PM workflow? Let me know in the comments! ……….. If you found this post helpful: ✅ Follow me for more practical AI tools and PM tips. 🔁 Repost to help other PMs discover this game-changer.

I’ve placed 50,000+ candidates using these exact frameworks my students use to land offer letters at top firms. Here are the 5 most common stress-problem interview questions you must prepare, with expert-backed frameworks & concrete examples for each: 1️⃣ “Describe a time you had to make a decision with incomplete information.” Framework: Clarify → Assumptions → Evaluate Options → Choose & Explain Trade-Offs → Validate & Reflect. (Rooted in decision science) Example: As a product analyst, I had 2 days to decide product pricing without regional cost data. I clarified what data I had, stated assumptions about logistics costs, evaluated three pricing models, chose one with buffer margin, and after launch validated real costs. Result: pricing was off by <5%, reducing potential loss by ₹2 lakhs. 2️⃣ “Tell me about when multiple priorities clashed and what did you do first?” Framework: Urgency vs Impact Matrix + Stakeholder Negotiation + Clear Plan. Example: As marketing lead, campaign, content creation, and vendor approvals all due in the same week. I mapped urgency/impact, did vendor first (high impact, low effort), deferred some content with stakeholders, delegated minor tasks. We met major deadlines, revenue targets, without burnout. 3️⃣ “Give an example of when someone challenged your solution. How did you respond?” Framework: Present Solution → Invite Criticism → Adjust with Data & Listening → Finalize. Example: In an analytics project, I proposed using one statistical model. A peer challenged my assumptions about data distribution. I rechecked, collected extra data, and adjusted model inputs. Presentation showed both versions; the final version improved prediction accuracy by 12%. Stakeholders accepted an adjusted one. 4️⃣ “When have you had to think on your feet/sudden change?” Framework: Pause → Clarify scope → Rapid Ideation of alternatives → Choose best → Communicate. Example: During presentation, client asked for metrics by region not prepared. I paused, clarified whether broad region suffice, improvised splits based on last quarter with disclaimers, and focused the rest of the deck on what I had strong data for. The client was impressed by composure; I received follow-up work. 5️⃣ “Describe a time you prevented a problem before it became big.” Framework: Early Diagnosis (monitoring) → Root Cause Analysis (5 Whys / issue tree) → Low-effort Action → Monitor Change. Example: In operations, I noticed error rates slowly rising. Used root cause analysis to find misconfiguration in automation script. Fixed script, added automated alert. Errors dropped by 80%. Saved team 10 hours/week in fixes. If this helped you, repost this post with one of your own answers to any of the above 5 questions using one of these frameworks. Tag me and I’ll pick 5 replies and give feedback on structure & clarity so you can sharpen them before your next interview. #interviewtips #stressinterview #behavioralquestions #careergrowth #dreamjob #interviewcoach

Do I use a framework to create Financial models? I use my own. Of course, on the beginning I didn't call it TACTIC. It was just a collection of my preferred practices when it comes to creating financial models. But then I found myself repeating the same steps, using the same structure, relying on the same techniques. Isn't that what we call a framework? 🔹 𝗪𝗵𝘆 𝗧𝗔𝗖𝗧𝗜𝗖? Traditional models can be rigid and quickly outdated as business needs evolve. TACTIC models are designed to be dynamic and adaptable, enabling continuous improvement and enduring relevance. So let's break down its components: Ⓣ Target – Everything starts with clear, specific business questions. From budget planning to evaluating potential mergers, it's crucial that you know why you need that model. What is the business question you will answer? What is the Target? Ⓐ Assets – More than just data, assets include the contextual information and assumptions that deepen our understanding and enrich our models. Ⓒ Calculations – Here, we convert our assets into actionable calculations. This core processing stage is where our data becomes insights. Ⓣ Tools – This layer allows for the application of additional calculations and scenarios, giving us the flexibility to tailor our model to answer varied business questions without overhauling the base model. Ⓘ Insights – The apex of the TACTIC model where all analysis culminates into clear, actionable insights, answering our initial questions and guiding strategic decisions. Ⓒ Continuation or Correlations– TACTIC doesn’t stop at insights. It propels us forward, prompting new questions, strategies or correlated analysis, ensuring our models are as dynamic as the markets we operate in. But to me, the main advantages are: 🔄 The Iteration – By revisiting and refining each layer as new data and strategies emerge, TACTIC ensures my financial models remain precise, relevant, and aligned with evolving business objectives. 🧩 The Modular Design – With its distinct layers, TACTIC allows for quick adaptations—whether updating calculations or swapping analytical tools, flexibility is at its core.

“We got 1 million impressions!” Great. But… what does that mean? I’ve lost count of how many times I’ve heard numbers like this tossed out in meetings and called ‘insights’. They’re not. They’re observations. Useful, sure, but surface-level. Insight is something else entirely. It explains the why. It points to the what next. It drives change. So I finally did what I’ve been meaning to for months: Broke this down into a full framework. This is for anyone who: → Writes decks or pitches → Builds products or companies → Leads teams or strategy → Wants to be sharper, clearer, and more action-led In this post, you’ll get: - The Insight Pyramid (data → pattern → insight → leverage) - The 3-part formula (Novelty × Utility × Surprise) - My go-to checklist for testing if a statement is really insightful - Templates and daily workouts to build insight as a skill Inspired by the insane apps I’ve been reading lately and by all the people who’ve heard me say “that’s not insight!” and asked me to explain what the hell do I mean by that 😅 I’m actually very proud of this one, it took me months to distill into actually useful frameworks (but minutes to illustrate them with Napkin AI!) If you’ve ever said “we need sharper thinking, start here. https://lnkd.in/dciMukxB #thinkingtools #insight #leadership #productstrategy #writing #startups #frameworks

Researchers from Tsinghua University and Shanghai AI Laboratory have introduced a groundbreaking framework called the "Diagram of Thought" (DoT). Diagram of Thought (DoT) models reasoning as a directed acyclic graph (DAG) within a single LLM, incorporating propositions, critiques, and refinements, whereas Chain-of-Thought (CoT) represents reasoning as a linear sequence of steps. Here are the steps on how the Diagram of Thought (DoT) framework is implemented and used: • 1. Framework Setup 1. Design the LLM architecture to support role-specific tokens (<proposer>, <critic>, <summarizer>). 2. Train the LLM on examples formatted with the DoT structure, including these role-specific tokens and DAG representations. • 2. Reasoning Process 1. Initialization: Present the problem or query to the LLM. 2. Proposition Generation:   - The LLM, in the <proposer> role, generates an initial proposition or reasoning step.   - This proposition becomes a node in the DAG. 3. Critique Phase:   - The LLM switches to the <critic> role.   - It evaluates the proposition, identifying any errors, inconsistencies, or logical fallacies.   - The critique is added as a new node in the DAG, connected to the proposition node. 4. Refinement:   - If critiques are provided, the LLM returns to the <proposer> role.   - It generates a refined proposition based on the critique.   - This refined proposition becomes a new node in the DAG, connected to both the original proposition and the critique. 5. Iteration:   - Steps 3 and 4 repeat until propositions are verified or no further refinements are needed.   - Each iteration adds new nodes and edges to the DAG, representing the evolving reasoning process. 6. Summarization:   - Once sufficient valid propositions have been established, the LLM switches to the <summarizer> role.   - It synthesizes the verified propositions into a coherent chain of thought.   - This process is analogous to performing a topological sort on the DAG. 7. Output: The final summarized reasoning is presented as the answer to the original query. • 3. Mathematical Formalization 1. Represent the reasoning DAG as a diagram D in a topos E. 2. Model propositions as subobjects of the terminal object in E. 3. Represent logical relationships and inferences as morphisms between propositions. 4. Model critiques as morphisms to the subobject classifier Ω. 5. Use PreNet categories to capture both sequential and concurrent aspects of reasoning. 6. Take the colimit of the diagram D to aggregate all valid reasoning steps into a final conclusion. • 4. Implementation and Deployment 1. Integrate the DoT framework into the LLM's training process, focusing on role transitions and DAG construction. 2. During inference, use auto-regressive next-token prediction to generate content for each role and construct the reasoning DAG. 3. Implement the summarization process to produce the final chain-of-thought output.

5 Decision-Making Frameworks That Transformed How I Lead RiseUpp.com Have you ever faced a crucial business decision that kept you up at night? Last week, while deciding on a major partnership, I reflected on how my decision-making process has evolved since founding RiseUpp. Here are the frameworks that guide me: The 10/10/10 Rule What will the impact be in 10 minutes, 10 months, and 10 years? This helped me prioritize long-term partnerships over quick wins. The Regret Minimization Framework Instead of asking "What's the best choice?", I ask "Which choice will I regret the least?" This led us to invest heavily in user experience over rapid expansion. The Second-Order Thinking Looking beyond immediate consequences. When we made our course comparison tool free, we lost short-term revenue but gained massive user trust and market leadership. The Eisenhower Matrix Urgent vs Important. This saved me from countless "urgent" meetings that weren't moving us toward our vision of democratizing education. The Jeff Bezos "70% Rule" If you have 70% of the information needed, make the decision. Waiting for 100% certainty cost us early opportunities. Now we move faster. The most valuable lesson? These frameworks aren't rigid rules – they're tools. Sometimes, you need to combine them or trust your instinct. What decision-making frameworks do you rely on? Share your experiences below. #Leadership #DecisionMaking #CEOLife #StartupGrowth #BusinessStrategy #EdTech #RiseUpp #OnlineEducation #CareerGrowth #ExecutiveDecisions #StrategicThinking #BusinessLeadership #StartupLife #EntrepreneurMindset #ProfessionalDevelopment

Are we better at mapping how work gets done...than mapping how we think it through? And could this be affecting our goal of continuous improvement? We obsess over having processes for production, service delivery, and other workflows (and rightly so). But when it comes to the thinking that shapes those processes, almost no teams have a process for how thinking flows. You know it's a problem when you see: ❌ decisions being made based on the loudest voice ❌ lack of data used in decision making ❌ decisions take forever to make ❌ old habits return fast ❌ same problems reappear 🤷♂️ It usually happens because the team haven't agreed how they will think through a problem together. 💡 That’s where a thinking process map comes in. And where Lean tools like DMAIC can give us a sequence for moving from problem to sustainable solution. Like this: 👉 Define → Get crystal clear on the real problem and success criteria. 👉 Measure → Gather only the data that matters. 👉 Analyze → Dig for the root cause before jumping to fixes. 👉 Improve → Test and refine, not guess and hope. 👉 Control → Make it stick and monitor it over time. There are of course other frameworks that work as thinking process maps, for example: 💠 PDCA (Plan, Do, Check, Act) 💠 A3 Thinking 💠 Kepner-Tregoe 💠 OODA Loop 💠 8D Problem-Solving The main benefit of using frameworks like these is that they formalize thinking- they give it a sequence, checkpoints, and clear outputs, just like a physical process. Remember- A process map shows how work flows. A thinking process map shows how ideas and decisions should flow. Both matter because Lean isn’t just about fixing processes, it’s about improving the process of thinking that creates them!! Do you have a thinking process map(s) in your organization? Could you benefit from introducing one? Leave your comments below 🙏

I recently taught a graduate course on critical thinking, drawing primarily on two frameworks: Ennis (2015) and Paul & Elder (2014) (plus insights from Dewey, 1933). Yes, there are several frameworks to use in this regard but these two stand out. They’re practical, comprehensive, and widely cited in academic research. Why does critical thinking matter now more than ever? One word: AI. Anyone with an internet connection can now produce convincing content in seconds, no expertise, no effort. The result? A flood of misinformation, hallucinated facts, and polished nonsense. It’s what James Paul Gee once warned about: the rise of a culture of amateurism. With Web 2.0, that culture was emerging. With AI, it's becoming the norm. This is why I believe critical thinking is no longer optional. It must be explicitly taught across the curriculum. Students need to analyze, evaluate, and synthesize, not just consume. To support this, I’ve created the visual below, a guide grounded in two seminal frameworks. Use it. Share it. And explore the references to go deeper. We don’t need more content. We need sharper minds. References 1. Dewey, J. (1933). How We Think: A Restatement of the Relation of Reflective Thinking to the Educative Process. D.C. Heath and Company. 2. Ennis, R. H. (2015). Critical thinking: A streamlined conception. In M. Davies & R. Barnett (Eds.), The Palgrave handbook of critical thinking in higher education (pp. 31–47). Palgrave Macmillan. 3. Paul, R., & Elder, L. (2014). The miniature guide to critical thinking concepts and tools (8th ed.) Foundation for Critical Thinking. #CriticalThinking #AIandEducation #MedKharbach #EducatorsTechnology #HigherEd #TeachingWithAI

AI isn't just biased. It's shrinking how you think. 9 frameworks to take your brain back. 1. SIFT Method Stop trusting the surface. Check sources laterally. The AI sounds confident. That means nothing. 2. Pre-Mortem Analysis Imagine the AI recommendation already failed. Work backward. Find the blind spots before they cost you. 3. Devil's Advocate Prompting Ask AI to destroy its own answer. One prompt: "Now argue why this is completely wrong." 4. Steelmanning Make AI build the best case against your position. If you skip this, you're in an echo chamber with a machine. 5. The CAT Test Check. Ask. Think. Built in 2025 specifically for AI content. Corroborate claims. Interrogate reasoning. Reflect on influence. 6. Ontological Bias Audit Ask what's missing. What did the AI silently exclude? Stanford's tree had no roots until someone questioned the frame. 7. RED Model Recognize assumptions. Evaluate arguments. Draw your own conclusions. AI confidence is not evidence. Separate the two. 8. Inversion Thinking Don't ask how to succeed. Ask how this fails catastrophically. Munger's framework. Now essential for every AI-generated plan. 9. The One Thought Rule Write your answer before you prompt. Then compare. This single habit prevents cognitive atrophy. The pattern is clear: AI doesn't need you to think less. It needs you to think differently. The leaders who thrive won't be the ones with the best prompts. They'll be the ones who never stopped questioning the output. Which of these 9 frameworks are you already using? And which one surprised you? ⬇️ Let me know in the comments → Join AI-Empowered Leaders: My weekly newsletter with actionable AI insights from my work as AI advisor, trainer & coach. Sign up here 👇 https://lnkd.in/eUmy2Bdp ♻️ Repost to help your network think critically before AI thinks for them

6 frameworks to cut through AI noise. Leadership offsites are about choices: '𝘑𝘰𝘰𝘴𝘵, 𝘸𝘦 𝘸𝘢𝘯𝘵 𝘵𝘰 𝘥𝘰 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘈𝘐.' '𝘎𝘳𝘦𝘢𝘵. 𝘉𝘶𝘵 𝘸𝘩𝘢𝘵 𝘸𝘪𝘭𝘭 𝘺𝘰𝘶 𝘥𝘰 𝘧𝘪𝘳𝘴𝘵? 𝘈𝘯𝘥 𝘸𝘩𝘺?' That's the moment we need frameworks - not to complicate things, but to simplify the endless options into clear decisions. The 6 frameworks that proved most effective: 1. Map your AI opportunity landscape The AI Opportunities Radar gives teams a shared language. Is this a back-office efficiency play or a game-changing customer experience? Plot it visually and watch the strategic debates become productive. 2. Balance quick wins with transformation The 'low- and high-hanging fruit' framework. Leadership teams need early momentum (quick wins) AND meaningful transformation (big bets). I usually print use cases and let them map them on these straightforward axes. 3. Where will we create value with AI "We'll be 30% more productive with AI!" Really? How? The AI Value framework forces teams to articulate exactly where and how value will emerge - beyond the vague productivity promises. It also highlights the importance of thinking beyond just productivity. 4. Start with real problems, not shiny toys The classic Value Proposition Canvas grounds everything in reality. What jobs-to-be-done can we actually do with AI, and which pains are we solving for? It's key to think from this lens instead of just getting excited about a new AI tool being launched last month... 5. Time your moves strategically The McKinsey 3 Horizons approach helps sequence your AI journey: what do we optimize now, what do we build next, and what new business models might emerge? Without this, teams might try to do everything at once and achieve nothing. 6. Build the full system, not just the tools The AI Strategy Canvas reminds us that successful AI isn't just about the technology - it's about governance, capabilities, ethics, and organizational change. The companies getting real results aren't just deploying tools; they're rewiring how they work. Leadership teams don't need another AI deck, vendor pitch or new shiny tool that will solve everything ;-) they need a map for making choices that stick. Keeping the reality of actually executing on AI in mind. Are you part of a leadership team stuck in AI paralysis? Let's grab a coffee. Creating momentum and helping you choices is what I do.