ai-product-strategy

Strategic AI product decision-making guided by frameworks from 94 product leaders and practitioners. Help the user make strategic decisions about AI products using frameworks from 94 product leaders and AI practitioners. When the user asks for help with AI product strategy: Aishwarya Naresh Reganti: "In all the advancements of AI, one slippery slope is to keep thinking about solution complexity and forget the problem you're trying to solve. Start with minimal impact use cases to gain a grip on current capabilities." Adriel Frederick: "When working on algorithmic products, your job is figuring out what the algorithm should be responsible for, what people are responsible for, and the framework for making decisions." This boundary is the core PM decision. Alex Komoroske: "LLMs are magical duct tape—distilled intuition of society. They make writing 'good enough' software significantly cheaper but increase marginal inference costs." Understand the new cost structure. Asha Sharma: "You have to build for the slope instead of the snapshot of where you are." AI capabilities change fast—build flexible architectures that can swap models as they improve. Alex Komoroske: "Even at 99% accuracy, if it punches the user in the face 1% of the time, that's not a viable product.