IT Sr. Analyst, Product Manager

Patrick Industries•Elkhart, IN

About The Position

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.

Requirements

Bachelor's degree in Business, Information Technology, Computer Science, Engineering, or related field; equivalent experience may be considered
4-7 years of experience in product management, business analysis, software delivery, AI solutions, or related disciplines
Demonstrated experience owning product roadmaps, portfolios, or prioritization decisions
Strong written and verbal communication skills with the ability to influence technical and business stakeholders
Experience building business cases, defining requirements, and managing stakeholder relationships
Hands-on experience with AI, machine learning, automation, agent-based solutions, or AI prototyping
Experience with Agile methodologies including Scrum, Kanban, backlog management, and sprint planning
Understanding of software development lifecycle, DevOps, testing, and release management practices
Working knowledge of system architecture, AI solution design, and technical feasibility assessment
Experience with AI coding assistants, prompt engineering, and AI solution prototyping
Familiarity with Python, SQL, and JavaScript/TypeScript concepts
Familiarity with Microsoft Azure, Microsoft Fabric, Power BI, Azure DevOps, Microsoft Foundry, and Dynamics 365 environments
Ability to query and analyze data to validate assumptions and measure outcomes

Nice To Haves

Experience in manufacturing, operations, supply chain, finance, HR, or other enterprise business functions preferred
Experience in decentralized, multi-site, or acquisition-driven environments preferred
Experience supporting governance committees, portfolio reviews, and executive reporting preferred
Exposure to C#, Rust, or KQL is a plus

Responsibilities

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 DevOps, 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, operational excellence, and digital transformation objectives
Support AI capabilities across customer experience, operations, supply chain, aftermarket growth, and back-office optimization
Identify opportunities to expand AI adoption across business units and functional areas
Partner with AI & Data leadership to advance enterprise AI maturity and capabilities
Promote responsible AI adoption through governance, communication, and stakeholder engagement