IT Sr. Analyst, Product Manager

IT Sr. Analyst, AI Product Manager

Patrick Industries, a publicly traded company headquartered in Elkhart, Indiana, invites you to join a team of dedicated Team Members who are passionate about delivering high-quality products and exceptional customer service. As a leading solutions provider serving a diverse range of markets across the United States, our commitment to innovation, quality, and sustainability has positioned us as a high growth, diversified and empowered Team of more than 10,000!

Your adventure awaits!

The IT Sr. Analyst, AI Product Manager is responsible for owning the enterprise AI use case portfolio, roadmap, and prioritization process across Patrick Industries. This role serves as the primary connection between business stakeholders and the Agile AI Factory, ensuring AI initiatives are aligned to business objectives, governance standards, and measurable value outcomes. The AI Product Manager drives use case intake, business case development, stakeholder alignment, prioritization decisions, delivery oversight, and value realization while partnering closely with engineering teams to translate business needs into scalable AI-enabled solutions.

• Own the enterprise AI use case roadmap, including prioritization, sequencing, scope management, and investment decisions
• Define and maintain the AI use case portfolio aligned with business objectives and strategic priorities
• Evaluate, prioritize, defer, reshape, or discontinue use cases based on business value, feasibility, risk, and resource requirements
• Establish success criteria, business outcomes, and acceptance requirements for AI initiatives
• Maintain transparency around roadmap decisions and prioritization rationale with stakeholders and leadership
• Ensure AI initiatives support Patrick's long-term digital transformation strategy

• Lead intake and qualification of new AI use case requests across the organization
• Develop business cases, value assessments, sponsorship alignment, and prioritization recommendations
• Ensure AI initiatives follow established governance, approval, and funding processes
• Support AI Steering Committee activities through portfolio reviews, status reporting, and decision support
• Track portfolio health, delivery progress, and investment performance across active initiatives
• Maintain governance documentation, decision records, and value realization tracking

• Serve as the primary liaison between business stakeholders and AI delivery teams
• Partner with operations, supply chain, manufacturing, finance, HR, and corporate functions to identify AI opportunities
• Translate business challenges into clearly defined product requirements and delivery priorities
• Present roadmap updates, business cases, portfolio performance, and value realization metrics to leadership
• Facilitate stakeholder alignment, decision-making, and issue resolution throughout the delivery lifecycle
• Develop executive communications, requirements documents, business cases, and decision memorandums

• Partner with AI Platform Engineers and delivery teams to ensure requirements are clear, testable, and actionable
• Participate in sprint planning, backlog refinement, reviews, retrospectives, and other Agile ceremonies
• Define user stories, acceptance criteria, and product requirements that support successful delivery
• Coordinate release planning and deployment readiness with technical and business stakeholders
• Ensure product requirements align with enterprise Dev Ops, testing, and deployment practices
• Monitor delivery progress and proactively address risks, dependencies, and resource constraints

• Build proofs of concept, prototypes, and AI agents to validate business requirements and solution feasibility
• Assess technical trade-offs including cost, scalability, data readiness, reliability, and performance impacts
• Evaluate solution viability prior to engineering resource commitments
• Review data, system architecture, and technical artifacts to support prioritization decisions
• Identify dependencies, gaps, and foundational requirements necessary for successful implementation
  
  Partner with engineering teams to ensure proposed solutions align with enterprise architecture standards

• Define measurable business outcomes and success metrics for every AI initiative
• Track realized value against approved business cases and investment objectives
• Partner with engineering and business stakeholders to evaluate solution effectiveness and quality
• Monitor adoption, business impact, and operational outcomes following deployment
• Develop portfolio reporting and value realization dashboards for leadership review
• Capture lessons learned and continuously improve prioritization and delivery practices

• Drive execution of AI initiatives supporting enterprise growth,…