AI CoE Delivery Enablement is seeking an experienced Product Owner to lead discovery and execution across two AI mini-pods delivering two scalable production-ready AI use cases within a 3 4 month timeframe.
The Product Owner will ensure solutions:
Leverage established enterprise AI technology standardsAre architected for scalability reuse and enterprise adoption
Align with business-aligned AppDev teams
Establish repeatable AI delivery patterns
Enable pod and AppDev team self-sufficiency in ongoing agile execution
This role extends beyond feature delivery and is responsible for positioning each use case for long-term enterprise adoption and sustainable ownership.
Scope of EngagementThe Product Owner will operate across two concurrent AI mini-pods and support use cases delivered under varying engagement models:
AI CoE led build Mini-pod leads discovery and execution end-to-end
Joint delivery (co-build) Shared ownership with business AppDev team
Advisory enablement AppDev team leads build with AI CoE providing standards guidance and oversight
The Product Owner must flex across these models while maintaining delivery discipline and alignment with enterprise AI standards.
Key Responsibilities 1. AI Discovery LeadershipLead structured discovery workshops with business stakeholders
Validate problem statements and define measurable business outcomes and KPIs
Translate business objectives into scalable AI solution scope
Identify data dependencies technical constraints and integration points
Define a clearly bounded production-ready scope aligned to a 3 4 month delivery window with explicit in-scope and out-of-scope capabilities
Define and maintain feature backlog including:
Business requirements
AI capabilities
Non-functional requirements (security performance explainability compliance)
Clear acceptance criteria tied to business outcomes
Prioritize backlog based on value feasibility and dependency alignment
Continuously refine and groom backlog across both mini-pods
Structure work to accommodate experimentation (e.g. technical spikes model evaluation cycles)
Lead agile ceremonies:
Sprint planning
Backlog refinement
Sprint reviews
Retrospectives
Act initially as de-facto Scrum Master
Track sprint goals delivery progress and impediments
Coordinate cross-team and cross-functional dependencies
Ensure sprint outcomes align with defined business value
Ensure alignment with enterprise AI architecture governance and risk standards
Partner with architecture data governance and security stakeholders
Identify and mitigate risks related to:
Data quality
Model performance
Bias and explainability
Operationalization and monitoring
Establish strong working relationships with business-aligned AppDev teams
Align backlog technical design and delivery approach with downstream system requirements
Define and execute structured handoff and transition plans
Ensure documentation and knowledge transfer support long-term maintainability
Enable AppDev teams to adopt AI CoE patterns and standards
Establish lightweight outcome-driven agile practices suitable for short AI initiatives (5 7 sprints)
Develop reusable templates and playbooks including:
Discovery frameworks
Backlog structures
Sprint reporting standards
AI use case delivery playbooks
Coach pod team members to rotate into Scrum Master role
Drive progressive ownership transfer by final sprint
Engagement Duration: 6 9 months
Target Start Date: End of February / Early March
Phase 1 Mini-Pod 1 (Active):
Lead discovery delivery backlog development sprint execution stakeholder alignment and governance coordination.
Phase 2 Mini-Pod 2 (Late Q2 / Early Q3 Activation):
Expand responsibilities to support concurrent or staggered delivery across both pods including discovery execution oversight and cross-pod coordination.
Engagement may be extended based on delivery outcomes or enterprise scaling needs.
Additional DetailsLocation Requirement: Hybrid St. Paul MN
Position Type: W2 / C2C
Work Authorization: As required