Ai Product Owner

AI Product Owner

Role Purpose

The AI Product Owner defines, prioritizes, and delivers AI-enabled products that create measurable value fo business functions, You are the bridge between business needs, data science and engineering teams, and governance stakeholders. You own the product vision and backlog from idea through adoption and continuous improvement.

Key Responsibilities

Product vision and strategy

• Define and maintain the vision, roadmap, and value case for AI products and use cases within your Pharma Domain
• Translate business problems into well-framed AI opportunities, including whether AI is the right solution at all.
• Align the roadmap with the DT strategy, business priorities, and the AI ambition.

Backlog and delivery ownership

• Own and prioritize the product backlog (epics, user stories, acceptance criteria) using value, feasibility, risk, and effort.
• Manage and support build of AI products.
• Lead sprint planning, refinement, and reviews with data scientists, ML/data engineers, architects, and UX/design colleagues.
• Make timely scope and trade-off decisions to keep delivery on track.

Stakeholder and business partnership

• Act as the primary contact for business sponsors and end users; run discovery sessions, workshops, and feedback loops.
• Manage expectations around what AI can and cannot deliver, including data readiness and model limitations.
• Drive change management, training, and adoption so products are actually used.

Value realization

• Define KPIs and success metrics up front (e.g., time saved, cycle-time reduction, quality, cost avoidance, user adoption).
• Track and report benefits after launch; make go / pivot / stop decisions based on evidence.

Responsible AI, risk, and compliance

• Ensure AI products follow AI governance, data privacy, security, and quality policies.
• Partner with Legal, Privacy, Quality/Compliance, and Information Security on risk assessments, validation requirements (e.g., GxP, CSV/CSA where applicable), and regulatory expectations such as the EU AI Act and GDPR.
• Promote transparency, explainability, human oversight, and bias monitoring.

Lifecycle and operations

• Work with MLOps/LLMOps and platform teams on release management, monitoring, model performance, drift, and cost (FinOps).