AI Insights for Better Product Development

I've spent 12 months ruthlessly testing AI tools for market research at Impact Theory. What did we learn? We identified market opportunities 6 months before competitors. We 10X'd our research capabilities. We turned market analysis from guesswork into science. But most people get AI market research completely wrong. They're passive. They wait for the perfect prompt. They expect AI to do the work. Those who are killing it with AI take a different approach. I use what I call the "Market Intelligence System": Step 1: Problem Verification Use this prompt: "List the top 5 urgent and painful problems faced by [your target market] with supporting evidence from Reddit, Amazon, Facebook, or other real sources." Step 2: Competitive Gap Analysis "Identify primary competitors and evaluate their strengths, weaknesses. Highlight clear opportunities to meaningfully differentiate my product." Step 3: Market Demand Assessment "Assess current market size and potential for growth. Evaluate key trends indicating increasing or declining demand with evidence from search volumes, surveys, industry data." Step 4: Pricing Intelligence "Suggest realistic pricing strategies and benchmarks. Analyze customer willingness to pay based on real data." Step 5: Validation Framework "Recommend actionable validation experiments to verify all base assumptions. List early warning signs of potential product-market misfit." The nuclear question: "What do people who disagree with these trends say? What are their best arguments?" This process takes me from zero market knowledge to expert-level intelligence in hours, not months. In a world where everyone has access to data, the advantage goes to those who know exactly how to extract insights from it. Most are drowning in information. Be the one who turns data into decisions. I built a free GPT that walks you through the whole process in 30 minutes. It will give you a step-by-step roadmap to launch your business. Try it out here: https://buff.ly/WQHxGFU

AI isn't just a tool; it's becoming a teammate. A major field experiment with 776 professionals at Procter & Gamble, led by researchers from Harvard, Wharton, and Warwick, revealed something remarkable: Generative AI can replicate and even outperform human teamwork. Read the recently published paper here: In a real-world new product development challenge, professionals were assigned to one of four conditions: 1. Control Individuals without AI 2. Human Team R&D + Commercial without AI (+0.24 SD) 3. Individual + AI Working alone with GPT-4 (+0.37 SD) 4. AI-Augmented Team Human team + GPT-4 (+0.39 SD) Key findings: ⭐ Individuals with AI matched the output quality of traditional teams, with 16% less time spent. ⭐ AI helped non-experts perform like seasoned product developers. ⭐ It flattened functional silos: R&D and Commercial employees produced more balanced, cross-functional solutions. ⭐ It made work feel better: AI users reported higher excitement and energy and lower anxiety, even more so than many working in human-only teams. What does this mean for organizations? 💡 Rethink team structures. One AI-empowered individual can do the work of two and do it faster. 💡 Democratize expertise. AI is a boundary-spanning engine that reduces reliance on deep specialization. 💡 Invest in AI fluency. Prompting and AI collaboration skills are the new competitive edge. 💡 Double down on innovation. AI + team = highest chance of top-tier breakthrough ideas. This is not just productivity software. This is a redefinition of how work happens. AI is no longer the intern or the assistant. It’s showing up as a cybernetic teammate, enhancing performance, dissolving silos, and lifting morale. The future of work isn’t human vs. AI. The next step is human + AI + new ways of collaborating. Are you ready?

Are you using AI to work faster, or to think smarter? Most product people I talk to are stuck in efficiency mode. They're using AI to write PRDs quicker, generate user stories faster, or automate basic tasks. That's useful, but it's just scratching the surface. In this week's Product Thinking Podcast, Lucie Buisson, CPO at Contentsquare, flipped this conversation on its head. She's not interested in AI as a productivity hack. She sees it as a strategic intelligence layer for your team and your customers. The real opportunity? AI can decode customer intent at scale in ways we've never been able to before. Instead of guessing what users want based on clicks and page views, you can understand the "why" behind their behavior across every touchpoint. Think about it: conversational interfaces that adapt in real-time, personalized experiences that predict needs before customers even realize they have them, and insights that connect quantitative patterns with qualitative feedback. This isn't about doing the same work faster. It's about doing entirely different work. How are you leveraging AI in your product strategy? Are you optimizing for speed, or for deeper customer understanding?