Strategic Competitive Intelligence

This is the most underrated way to use Claude: (and it has nothing to do with writing or coding) It's competitive intelligence. Using data that's free, public, and updated every single week. Here's my extract step by step guide: Step 1. Go to claude .ai. Step 2. Select the new Claude "Opus 4.6." Step 3. Turn on "Extended Thinking." Step 4. Pick a competitor. Go to their careers page. Step 5. Copy every open job listing into one doc. (Title. Team name. Location. Full description) Step 6. Save it as one .txt or .docx file. Step 7. Search the company at EDGAR (sec .gov) Step 8. Download its recent 10-K or 10-Q filing. (Official strategy, risks, and financials - all public.) Step 9. Upload both files to Claude Opus 4.6. Step 10. Paste this exact prompt: "You are a competitive intelligence analyst at a rival company. I've uploaded [Company]'s complete current job listings and their most recent SEC filing. Perform a strategic intelligence analysis: → Cluster these roles by what they suggest is being built. Don't use the team names they've listed. Infer the actual product initiatives from the skills, tools, and responsibilities described. → Identify capabilities or teams that appear entirely new — not mentioned anywhere in the SEC filing. These are unreleased bets. → Find roles where seniority is disproportionately high for a new team. This signals executive-level priority. → Cross-reference the SEC filing's Risk Factors and Strategy sections with hiring patterns. Where are they investing against a stated risk? Where did they flag a risk but have zero hiring to address it? → Predict 3 product launches or strategic moves this company will make in the next 6-12 months. State your confidence level and cite specific job titles and filing sections as evidence. Format this as a 1-page competitive intelligence briefing for a CMO." What you'll find: → Products that don't exist yet but will in 6 months. → Priorities that contradict what the CEO said. → Risks they told the SEC but aren't addressing. This is what consulting firms charge $200K for. It took me 10 minutes. I used the new Claude 'Opus 4.6' for a reason: ✦ It read 60 job listing & a 200-page filing together.  ✦ And connects dots across both. ✦ It is superior in thinking and context retrieval. That's why I didn't use ChatGPT for this.

One of the most important applications of GenAI is in foresight. A new report from Paulo Carvalho at IF Insight & Foresight on "How Generative AI Will Transform Strategic Foresight" provides wide-ranging perspectives on the possibilities. Here are some of the most interesting action-oriented frames I found in the report. 🔍 Real-Time Environmental Scanning: Use GenAI to conduct continuous scanning of emerging trends, weak signals, and disruptions across diverse sources. This real-time, dynamic approach allows organizations to stay agile, proactively adjusting strategies as new insights unfold. 🌐 Immersive Scenario Simulations: Utilize GenAI to create interactive VR/AR scenarios that bring potential futures to life. These simulations engage stakeholders deeply, helping them visualize and emotionally connect with complex strategic choices, fostering stronger alignment with future goals. 🔄 Adaptive Scenario Planning: Move from static to adaptive planning by integrating live data into foresight models. Continuous updates based on geopolitical, economic, and technological shifts ensure that scenarios remain relevant and actionable over time. 💬 Enhanced Strategic Conversations: Use GenAI-powered virtual agents to facilitate dynamic "what-if" conversations, helping stakeholders explore a range of possible outcomes. This deepens strategic insights and encourages a proactive approach to complex decision-making. ⚙️ Modeling Complexity and Emergent Behaviors: Use GenAI to simulate complex systems and emergent behaviors, enabling organizations to anticipate interconnected, cascading effects. This prepares them for resilience in the face of unpredictable challenges and non-linear changes. 📊 Multimodal Data Integration for Richer Insights: Leverage GenAI’s capacity to analyze diverse data types (e.g., text, images, audio, video) to gain a nuanced, comprehensive view of trends and risks. This multimodal approach captures intricate patterns that single-source analysis might miss. 🌍 Embrace Multiple Perspectives and Plurality: Design foresight processes that incorporate a wide array of perspectives, blending cross-disciplinary and cultural insights. This inclusive approach creates more robust, innovative scenarios that account for diverse worldviews and challenges assumptions. 🤝 Facilitate Participatory and Co-Creative Approaches: Use GenAI to build interactive platforms that invite diverse stakeholders to co-create and refine scenarios. Real-time collaboration enhances the relevance and inclusivity of strategic models, making them more reflective of shared goals and values. I'll be sharing some of my thoughts on this very important topic in the next little while.

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

Today’s must-read for anyone working with foresight and futures thinking comes straight from the European Parliament: a timely and sharp briefing on Augmented Foresight. This new briefing explores the transformative potential of generative AI in strengthening foresight analysis and strategic decision-making. As recent advancements in large language models (LLMs) reshape how we approach policy research, their integration into foresight practice is accelerating. Generative AI is already enhancing the work of foresight — from identifying trends and weak signals, to crafting rich, immersive scenario narratives that help bring alternative futures to life. As generative agents powered by LLMs become increasingly capable of mimicking human behavior, they offer new possibilities for exploring complexity and accelerating insight at scale. Yet, alongside these opportunities lie important challenges. Effectively embedding LLMs into foresight work demands careful scrutiny of their limitations and inherent biases. Human oversight remains essential — not just for validating outputs, but for upholding principles of transparency, accountability, and ethical integrity. Crucially, generative AI should be seen as a powerful augmentation tool — not a replacement for human judgment. By combining computational power with human expertise, foresight practitioners can unlock new ways to enrich strategic planning and anticipate long-term uncertainties. A proactive and critical approach to adopting generative AI will be key to developing more informed, resilient, and adaptive strategies in the face of complex and contested futures. Love it. Kudos to Lucia Vesnic-Alujevic and Salvatore d'Ambrosio for helping push this important conversation forward. #foresight #strategicintelligence #AI #LLM #futures #policy #europeanparliament #augmentedforesight

"Many militaries are expanding the scope and speed of incorporating more complex data-driven techniques into the processes of determining courses of action, including when it comes to the use of force. These developments raise questions about the changing roles played by humans and machines, or human-machine interaction, in warfare. "This report contributes to ongoing debates on AI DSS by reviewing main developments and discussions surrounding these systems and their reported uses. It takes stock of what is known about AI DSS in military decision-making on the use of force, including in ongoing war zones around the globe. Section 2 provides a brief overview of the roles that AI DSS can play in use-of-force decision-making. Section 3 reviews main developments that we treat as indicative of trends in AI DSS in the military domain." "It focuses on three concrete empirical cases, namely the United States (US)’ Project Maven initiative, as well as systems reportedly used in the Russia-Ukraine war (2022-) and the Israel-Hamas war (2023-). Section 4 discusses opportunities and challenges associated with these developments, drawing inspiration from ongoing debates in the media and expert communities. The report concludes with some recommendations on potential ways forward to address the challenges discussed and with some questions raised by AI DSS that deserve further attention in the global debate on AI in the military domain." From Anna Nadibaidze Dr Ingvild Bode Qiaochu Zhang Center for War Studies, University of Southern Denmark

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data. ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose key tasks and see how successful users are. It won’t be right at first, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/eUBScaHp 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush: https://lnkd.in/dUgWtwnu 👍 UKO: https://lnkd.in/eNFv2p_a 👍 Wireframing Kit: https://lnkd.in/esqRdDyi 👍 [continues in comments ↓]

Real-time data analytics is transforming businesses across industries. From predicting equipment failures in manufacturing to detecting fraud in financial transactions, the ability to analyze data as it's generated is opening new frontiers of efficiency and innovation. But how exactly does a real-time analytics system work? Let's break down a typical architecture: 1. Data Sources: Everything starts with data. This could be from sensors, user interactions on websites, financial transactions, or any other real-time source. 2. Streaming: As data flows in, it's immediately captured by streaming platforms like Apache Kafka or Amazon Kinesis. Think of these as high-speed conveyor belts for data. 3. Processing: The streaming data is then analyzed on-the-fly by real-time processing engines such as Apache Flink or Spark Streaming. These can detect patterns, anomalies, or trigger alerts within milliseconds. 4. Storage: While some data is processed immediately, it's also stored for later analysis. Data lakes (like Hadoop) store raw data, while data warehouses (like Snowflake) store processed, queryable data. 5. Analytics & ML: Here's where the magic happens. Advanced analytics tools and machine learning models extract insights and make predictions based on both real-time and historical data. 6. Visualization: Finally, the insights are presented in real-time dashboards (using tools like Grafana or Tableau), allowing decision-makers to see what's happening right now. This architecture balances real-time processing capabilities with batch processing functionalities, enabling both immediate operational intelligence and strategic analytical insights. The design accommodates scalability, fault-tolerance, and low-latency processing - crucial factors in today's data-intensive environments. I'm interested in hearing about your experiences with similar architectures. What challenges have you encountered in implementing real-time analytics at scale?

This is a pivotal time for business leaders to apply strategic foresight and systems thinking. Go beyond tariffs and stock market trends and consider the broader, longer-term impacts: 1. How might a trend toward AI deregulation in product safety affect the AI products my business relies on? 2. In what ways could shifts in immigration policy influence my workforce strategy for maintaining a competitive edge with emerging technologies? How could these policies reshape PhD talent pipelines? 3. How will evolving U.S. geopolitical relationships impact my third-party suppliers and global partnerships? 4. With the increasing influence of techno-politics, what new considerations emerge for my business strategy? Scenario planning is key in moments of change and uncertainty.

In Equity research, the best way to study a business is to see what the business's input is, output is ,and how it earns money - This will help you understand the business on a basic level For instance - Marico is an FMCG company. Let's break it down - 1. Input (Raw Materials, Resources, Capabilities): Agricultural commodities: copra (for Parachute oil), safflower, rice bran oil, almonds, oats, etc. Packaging materials: bottles, caps, labels. Marketing & distribution spend. Brand equity/Goodwill Strong supply chain and vendor ecosystem. 2. Output (Products & Services): Parachute Coconut Oil Saffola Edible Oils Hair & skincare products (Livon, Nihar, Hair & Care) Healthy foods: Saffola oats, masala oats, honey International products in Bangladesh, MENA, South Africa 3. How it Earns Money (Revenue Model): Sells FMCG goods via retail, wholesale, modern trade, and e-commerce. Relies on strong brand recall and repeat consumption. High-margin segments: premium skincare, value-added foods. International business adds diversification (e.g., Bangladesh is a major profit contributor). Why this Input-Output-Business Model Method Works - - You reduce a large idea into basic understanding - Helps compare companies: For example, Emami vs Marico vs Godrej consumer – what inputs differ? Who has better pricing power? - Identifies risks: If copra prices spike (input cost), Marico’s margins may shrink. - Gives business model clarity: Is this a volume-driven business, premiumisation story, or expansion play? This framework may sound simple — but it forces clarity of thought and reveals: Business model strengths Cost structures Competitive edges Scalability potential Once you master this foundation, you can go deeper into: Qualitative Analysis (Management, etc) Competitive analysis (Porter’s 5 Forces) Financials (ROCE, Gross/EBITDA Margins) Moats (Brand, Distribution, Patents) All the best for Equity research, don't wait for a job to come by, start doing Research on your own today!