Product Manager - AI Success

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*All the benefits listed above are subject to the terms of their individual Plans .

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General Summary:

The AI Product Success Product Manager is an Agile leader accountable for maximizing the ongoing value, adoption, and health of AI products in production. The Product Manager works across business, Product, Engineering, Data Science, Operations, Finance, and Risk to translate production insights, user feedback, performance trends, and evolving business needs into a prioritized roadmap and backlog.

• The Product Manager serves as the orchestrator for the post-launch AI product lifecycle, ensuring products successfully transition into production, remain operationally healthy, continuously improve, and deliver against intended business outcomes. The role drives prioritization across enhancements, model improvements, production issues, technical debt, adoption opportunities, and changing business needs, while mobilizing the appropriate teams to address them.
• This position works closely with internal constituents, external partners and customers to ensure product line strategy is aligned with distribution strategies, market needs, growth opportunities, and profitability and risk thresholds. This position drives strategic planning and must influence others in executing to plan. In doing so, the product manager formulates, prepares and oversees the implementation of product and marketing strategies for a product line that includes developing new products and enhancing existing products to meet sales growth, profitability and persistency objectives. The product manager is responsible for ensuring their product line is competitive and meets future marketplace demands and directs marketing strategies to increase product sales across distribution channels and market segments.

Specifications

• •Bachelor’s degree preferred, or equivalent combination of education and relevant experience.
• •Demonstrated experience in Product Ownership, Product Management, technology delivery, or digital product operations .
• •Insurance Industry experience preferred
• •Strong understanding of the complete product lifecycle, particularly launch, adoption, production operations, continuous improvement, and value realization .
• •Proven understanding of Agile product management practices, backlog management, prioritization, work decomposition, planning, and iterative delivery.
• •Strong technical acumen with the ability to collaborate effectively with Engineering, Architecture, Data Science, AI/ML, Data Engineering, DevOps/MLOps, and Operations.
• •Working knowledge of AI/ML product concepts such as model performance, monitoring, drift, data dependencies, explainability, and production monitoring .
• •Experience defining and using product, business, adoption, operational, and technical KPIs to inform prioritization and decision-making.
• •Strong verbal, written, and presentation skills, including the ability to translate technical and AI concepts for nontechnical audiences.
• •Proven ability to lead through influence and mobilize cross-functional teams without direct authority.
• •Strong problem-solving skills with demonstrated ability to make prioritization and trade-off decisions in ambiguous situations.
• •Strong interpersonal skills and ability to engage with business stakeholders, end users, technology teams, and senior leaders.
• •High degree of ownership and accountability with the ability to proactively identify product risks and opportunities.
• •Ability to champion change and partner with operational leaders to increase adoption of AI-enabled capabilities.
• •Experience working within governance, risk, compliance, or regulated environments is beneficial.
• •Demonstrated commitment to continuous improvement of products, processes, and ways of working.
• •Some travel may be required.

Principal Duties and Responsibilities

Product Success & Value Realization

• •Own the ongoing success of AI products in production , with focus on business value, adoption, user experience, operational health, and continuous improvement.
• •Establish product success measures and KPIs with business partners and translate them into actionable product, operational, and delivery metrics.
• •Track benefit realization and identify gaps between expected and realized business outcomes, incorporating findings into roadmap and backlog decisions.
• •Partner with Finance and business stakeholders to maintain visibility into product value and inform continued investment and prioritization decisions.
• •Maintain a holistic understanding of users, business processes, and customer needs to ensure production AI products continue to solve the intended problems.

Product Health & Continuous Improvement

• •Maintain an integrated view of product health across business value, adoption, operational performance, model performance, technology health, and risk .
• •Use production data, user feedback, operational insights, model performance, and business priorities to identify continuous improvement opportunities.
• •Partner with Data Science and AI teams to understand model performance, degradation, drift, and improvement opportunities and prioritize action based on business impact.
• •Ensure recurring production issues and user pain points are translated into sustainable product improvements rather than addressed solely as individual incidents.
• •Identify opportunities to improve adoption, usability, reliability, automation, and overall business outcomes.

Roadmap & Backlog Ownership

• •Build and maintain an updated, transparent, and prioritized AI Product Success backlog .
• •Prioritize work based on business value, product/model performance, user impact, operational risk, technical health, and available capacity.
• •Translate business needs, production insights, incidents, feedback, and improvement opportunities into clear backlog items and acceptance criteria.
• •Balance enhancements, model improvements, break-fix needs, technical debt, regulatory/risk needs, and platform changes within the product roadmap.
• •Partner with Engineering and Data Science to understand delivery constraints and make appropriate scope, sequencing, and priority trade-offs.
• •Use Agile backlog and delivery tooling to provide transparency into priorities, status, progress, dependencies, and outcomes.

Transition to Product Success

• •Represent AI Product Success during the transition of new AI products and major capabilities into production.
• •Ensure products meet established transition-to-production/Run readiness criteria before Product Success assumes ownership.
• •Coordinate readiness across documentation, model and application monitoring, operational processes, escalation paths, technical readiness, compliance requiremen