ai-product-strategy
Strategic AI product decision-making guided by frameworks from 94 product leaders and practitioners.
- Helps distinguish genuine user problems from "AI for AI's sake" by starting with problem definition, not technology
- Guides critical architecture decisions including build vs buy, model selection, human-AI boundaries, and multi-model systems
- Emphasizes designing for AI failure modes, non-determinism, and rapid iteration through feedback loops and evals
- Flags common mistakes like single-model thinking, static architectures, and over-automation that undermine long-term product viability
AI Product Strategy
Help the user make strategic decisions about AI products using frameworks from 94 product leaders and AI practitioners.
How to Help
When the user asks for help with AI product strategy:
- Understand the context - Ask what they're building, what problem they're solving, and where they are in the AI journey
- Clarify the problem - Help distinguish between "AI for AI's sake" and genuine user problems that AI can solve
- Guide architecture decisions - Help them think through build vs buy, model selection, and human-AI boundaries
- Plan for iteration - Emphasize feedback loops, evals, and building for rapid model improvements
Core Principles
Start with the problem, not the AI
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."
Define the human-AI boundary
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