Senior Product Owner - AI-Enabled Enterprise Solutions

Responsibilities 

1. Product Ownership & Enterprise Application Delivery  

• Own and manage the product vision, roadmap, and backlog for assigned enterprise IT initiatives across business domains.  

• Elicit and refine business requirements through workshops, interviews, and process reviews.  

• Translate business needs into epics, features, user stories, and acceptance criteria using enterprise tools (e.g., Jira, Confluence).  

• Document business processes, system flows, and functional logic using clear diagrams and narratives (e.g., Lucid).  

• Assess enterprise systems end‑to‑end, from user experience through integrations, data flows, and downstream impacts.  

• Balance business value, technical feasibility, regulatory obligations, and delivery risk.  

• Support Agile, waterfall, and hybrid delivery models, including formal governance and design documentation where required.  

• Write and execute functional test cases and support user and business acceptance testing.  

2. AI & Intelligent Automation Opportunity Identification  

• Analyze business processes to identify where AI, Copilot‑style tools, automation, or agent‑based solutions can reduce manual effort and improve outcomes.  

• Identify repetitive, rules‑based, or information‑heavy activities suitable for AI augmentation or automation.  

• Partner with stakeholders to frame AI opportunities in measurable business terms (e.g., productivity improvement, cycle time reduction, decision support).  

• Leverage AI tools (e.g., Copilot, prompt‑based assistants) to support requirements analysis, documentation, ideation, and early solution exploration.  

• Identify decision points where AI can act as decision support or a semi‑autonomous “digital teammate”, with clear human oversight.  

3. AI Requirements & Delivery Enablement  

• Elicit and define requirements for AI‑enabled initiatives, including:  

• Business objectives and success criteria  

• User interaction models (human‑in‑the‑loop vs. automation)  

• Inputs, outputs, decision boundaries, and escalation rules  

• Translate business needs into AI‑ready user stories, acceptance criteria, and prompt or logic descriptions suitable for AI or agent‑based implementations.  

• Identify and document data requirements for AI use cases (source systems, data quality, metadata, access constraints) in collaboration with SMEs and data teams.  

• Work iteratively with technical and AI teams to validate feasibility, refine scope, and adjust requirements based on risk and complexity.  

• Support testing and validation of AI outputs, including quality, accuracy, explainability, and exception handling.  

4. AI Governance, Risk & Responsible Use  

• Ensure AI‑enabled features are appropriately governed, transparent, and aligned with enterprise AI, data, and security policies.  

• Capture and maintain AI use‑case documentation (purpose, scope, data inputs, user impacts, controls) to support governance and audit needs.  

• Identify and factor in ethical, legal, data privacy, and compliance considerations when defining AI requirements.  

• Collaborate with governance, legal, risk, and data teams to support AI approval, lifecycle management, and traceability.  

5. Stakeholder Management & Change Enablement  

• Serve as the primary point of contact between business, IT, vendors, and AI/data teams.  

• Facilitate alignment on scope, priorities, risks, dependencies, and outcomes.  

• Translate complex technical and AI concepts into clear, business‑focused language.  

• Support change management and adoption by clearly defining how AI‑enabled capabilities fit into daily workflows and decision‑making.  

• Contribute to improving overall AI literacy through documentation, walkthroughs, and practical examples. 

Qualifications 

•  4–6+ years of experience in Business Systems Analysis, Product Ownership, or IT delivery in an enterprise environment. 

• Strong experience delivering enterprise applications using Agile, waterfall, or hybrid methodologies. 

• Demonstrated experience writing clear requirements, user stories, process flows, and acceptance criteria. 

• Practical, working knowledge of AI, automation, analytics, and Copilot‑style tools (no model development required). 

• Strong stakeholder management, communication, and facilitation skills. 

• Hands‑on experience with Jira, Confluence, and Lucid (r equired). 

Preferred / Nice to Have 

• Experience supporting AI‑enabled, automation, or intelligent workflow initiatives. 

• Familiarity with agent‑based automation or advanced workflow orchestration concepts. 

• Experience in domains such as automotive, finance, sales, contact center, or technical operations. 

• Exposure to Kepner‑Tregoe, Lean, Six Sigma, or process re‑engineering methodologies. 

• Relevant certifications (CSPO, SAFe POPM, PMP, or equivalent). 

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