Define and execute a clear, outcome-driven vision for AI-powered capabilities (copilots, recommendations, summarization, search/chat, workflow automation) Translate business goals into measurable product outcomes and prioritized roadmaps Identify high-impact use cases through deep customer and stakeholder discovery Translate complex AI/ML capabilities into simple, intuitive, and trustworthy user experiences Partner with engineering and data science to design and deliver solutions leveraging LLMs, RAG, and agentic architectures Define AI system behavior, including inputs/outputs, confidence handling, fallback logic, and UX for uncertainty Drive end-to-end execution with strong ownership and urgency Make high-quality product and technical tradeoffs (MVP vs scale, speed vs quality) Manage cross-functional dependencies across engineering, design, data science, and business teams Own evaluation frameworks (offline and online), including quality metrics, human review, and experimentation Ensure systems meet standards for accuracy, latency, reliability, and user trust Continuously improve performance through iteration and data-driven insights Own adoption as a primary success metric, not just delivery Design onboarding, enablement, and feedback loops that drive behavior change Track and optimize engagement, usage, and business impact metrics Navigate complex enterprise environments with multiple stakeholders, legacy systems, and constraints Influence without authority across product, engineering, sales, operations, and leadership teams Communicate clearly with executives, translating technical concepts into business impact