• Build working product solutions using AI as the primary execution medium • Rapidly prototype, iterate, and refine solutions to validate ideas early • Use AI agents for requirements analysis, design exploration, implementation, and testing • Move from problem understanding to working solution without reliance on legacy SDLC rituals • Engage directly with customers to understand workflows, pain points, and unmet needs • Use live demos and working prototypes to drive customer conversations • Validate solutions through real usage, not assumptions or opinions • Build trust by clearly explaining AI behavior, limitations, and tradeoffs • Frame problems clearly and precisely before execution • Identify new AI-powered solution opportunities and revenue potential • Evaluate ideas based on impact, feasibility, and learning velocity • Use experimentation to discard weak ideas quickly • Define success in terms of adoption, retention, efficiency, and revenue impact • Prioritize initiatives based on outcomes, not feature volume • Continuously assess whether solutions are creating real value • Own the full lifecycle from idea to impact • Work closely with AI Product Engineers and AI Developers within AI Pods • Align engineering, design, and data efforts around outcomes • Communicate clearly with internal stakeholders • Represent the product externally with credibility and clarity