Senior AI Product Manager

Location Remote, United States Category Technology Job Type Full Time Day Job Job  

• Owns a major area of the BSWH AI use-case roadmap and is accountable for translating customer jobs-to-be-done into AI-powered experiences that improve customer outcomes, operational efficiency, and business value.
• Leads prioritization, roadmap development, requirements definition, launch planning, and success measurement for AI-enabled capabilities across one or more customer journey areas.
• Works closely with Digital, Journey, Clinical, Analytics, Data, Ontology, Architecture, Engineering, and Operations teams to define, deliver, and continuously improve AI-powered customer experiences.
• Ensures AI capabilities solve meaningful customer problems, achieve measurable outcomes, and are designed for safe, scalable, and reusable implementation across BSWH use cases.
• Drives alignment across stakeholders while balancing customer needs, technical feasibility, operational constraints, regulatory requirements, and enterprise priorities.
• Partners with AI platform and architecture teams to leverage reusable AI capabilities while maintaining accountability for customer and business outcomes within the assigned product area.

• Own the product strategy, roadmap, and backlog for one or more priority AI use-case areas.
• Translate customer, operational, and clinical problems into clear jobs-to-be-done, product requirements, delivery priorities, and measurable outcomes.
• Define success metrics and key performance indicators that measure business impact, customer value, adoption, engagement, satisfaction, and operational improvement.
• Identify, evaluate, and prioritize opportunities where AI can create differentiated customer experiences or operational advantages.
• Develop business cases and prioritization frameworks that balance strategic value, implementation complexity, risk, dependency management, and expected return.

• Lead discovery efforts to understand customer needs, stakeholder requirements, operational workflows, and pain points that can be addressed through AI-enabled capabilities.
• Frame AI use cases as concrete customer jobs-to-be-done and ensure each use case has a clear problem statement, target outcome, measurable success criteria, and responsible stakeholders.
• Partner with customer journey leaders, clinical and operational subject matter experts, and analytics teams to validate that proposed capabilities address the right problems.
• Ensure product decisions stay grounded in customer value, clinical/operational integrity, and measurable business impact rather than technology novelty alone.

• Define AI-enabled product capabilities, workflows, agent behaviors, escalation paths, guardrails, and customer experiences for assigned AI use cases.
• Translate strategy into clear user stories, acceptance criteria, success metrics, and delivery priorities that engineering and AI teams can execute against.
• Collaborate with architecture and engineering teams to determine when solutions should use orchestration, retrieval, deterministic rules, personalization, automation, or human review.
• Partner with design, research, and customer experience teams to ensure AI-powered interactions are understandable, helpful, trustworthy, and aligned with BSWH experience standards.

• Lead cross-functional collaboration across Product, Engineering, Architecture, Journey, Clinical, Analytics, Data, Ontology, Security, Privacy, Compliance, and Operations teams.
• Manage roadmap tradeoffs across customer experience, speed of delivery, technical complexity, operational readiness, safety, compliance, and long-term scalability.
• Communicate product vision, roadmap priorities, delivery progress, risks, launch readiness, and outcomes to leadership and stakeholders.
• Support launch planning, adoption, training, stakeholder readiness, and operational handoffs for AI-enabled capabilities.

• Define expected AI behaviors, quality expectations, guardrails, and acceptance criteria in partnership with clinical, operational, technical, and AI quality stakeholders.
• Partner with engineering, analytics, and AI quality teams to establish evaluation approaches, testing criteria, performance expectations, and feedback loops.
• Monitor AI capability performance, adoption, customer outcomes, operational outcomes, and quality metrics to…