Product Manager / Product Owner – AI - SC2026

End-to-End AI Product Owner – Line of Business or Platform Aligned

Role Overview
The Product Manager / Product Owner – AI is responsible for end-to-end ownership of AI initiatives for a specific Line of Business or claims domain. This role partners directly with business stakeholders to define AI-enabled improvements to claims operations, cost control, and client service performance, and translates those objectives into structured, sprint-ready execution plans. This role combines strategic initiative shaping with hands-on backlog ownership and daily collaboration with AI engineers and data scientists. It ensures that AI development is grounded in real operational pain points and that delivered solutions are practical, compliant, and measurable in impact.

Key Responsibilities

• 6–10 years of experience in Product Management or Business-Technology roles within insurance, claims, or managed services
• Strong understanding of claims operations, adjuster workflows, or insurance servicing models
• Experience working in Agile delivery environments
• Ability to translate operational pain points into structured technical requirements
• Strong stakeholder communication skills across business and technical audiences
  
  

• The Product Manager/Owner works directly with claims leadership, operational managers, and Business Transformation Leads to understand workflow bottlenecks, leakage drivers, customer friction points, regulatory risks, and client performance commitments. They translate these insights into clearly defined AI use cases with measurable operational objectives such as reduced claim cycle time, improved reserving accuracy, enhanced fraud detection rates, or improved document processing speed.
• They decompose initiatives into epics and features that reflect real-world operational workflows, ensuring requirements account for system integrations, adjuster behavior, client reporting requirements, and compliance obligations. They collaborate with AI engineers and data scientists to validate feasibility, data availability, model explainability considerations, and downstream operational impact.
• They own and continuously refine the product backlog, ensuring it remains prioritized according to business value and aligned with enterprise AI portfolio constraints. They prepare stories with detailed acceptance criteria that reflect actual claims scenarios and operational realities, not abstract technical requirements.
• During sprint execution, they remain closely engaged with the delivery team, clarifying nuances of claims processes, regulatory rules, and client-specific variations. They validate that developed models, automation features, or decision-support tools function appropriately within the claims workflow and align with user expectations.
• They communicate progress, risks, and trade-offs to LOB stakeholders transparently, ensuring expectations are managed around sequencing and delivery timelines. Following deployment, they monitor adoption, operational metrics, and user feedback, adjusting backlog priorities based on real-world performance and measurable impact.
• This role requires balancing operational urgency with disciplined backlog governance, particularly in environments where client escalations or regulatory deadlines may introduce pressure for rapid change.