"Over the past six months, I've worked with 18 product teams on customer discovery, idea generation, and testing. Each of these teams used various artificial intelligence (AI) tools and large language models (LLMs), but three of them massively outperformed the other 15. The difference wasn't access to better tools. The top teams used LLMs to sharpen their thinking and move faster on decisions they already understood. The other teams leaned on AI-generated answers without applying their own judgment."
"LLMs are statistical pattern matchers, not thinkers. They don't have lived experience, emotions, or the ability to develop product sense. They've never felt a user's frustration with a broken workflow or navigated the constraints of your specific market. LLMs excel at synthesizing patterns from what already exists and producing an average of past solutions: That's rarely what you want when you're searching for differentiated solutions."
"AI can accelerate innovation, but it doesn't replace product leadership. Strong outcomes still depend on a PM who can frame problems, evaluate tradeoffs, and decide what good looks like. When teams outsource ideation to AI, they weaken their product judgment over time. Generating ideas yourself, including the bad ones, builds pattern recognition for what works in your specific context and makes future decisions stronger."
Three teams outperformed others by using LLMs to sharpen already-formed thinking rather than to replace it. Teams that relied on AI for ideation produced more output but less impact, iterating on weak assumptions without a clear bar for quality. LLMs are statistical pattern matchers lacking lived experience and product sense, so they tend to produce average past solutions rather than differentiated ones. Effective teams generate ideas solo first, then use AI as a sparring partner, create environments that let LLMs work well, and maintain clear mental models for evaluating quality and tradeoffs.
Read at LogRocket Blog
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