Job Family

Product Owner – AI‑Enabled Products

The Product Owner represents the business within the squad and acts as the primary interface for demand intake, including AI‑enabled, data‑driven, and automation use cases

The Product Owner reflects accepted demand on the squad backlog and prioritizes it according to business value, strategic priorities, regulatory constraints (banking license), and AI risk posture

Role Purpose

The Product Owner is the guardian of product fitness for purpose, ensuring that functional, non‑functional, and AI‑specific requirements are met for products of limited complexity, uncertainty, and dependencies (e.g. mature products, end‑of‑life systems, or products with a well‑defined operational scope).

This includes ensuring that AI components (models, data pipelines, decision logic, automation) are:

• Fit for business intent
• Compliant with regulatory and ethical standards
• Operationally robust and explainable

& Responsibilities

1. Product Ownership & Business Value (AI‑Aware)

• Act as end‑to‑end owner of the product, including:
  • Functional requirements
  • Non‑functional requirements (performance, security, resilience)
  • AI‑specific qualities such as explainability, data quality, bias awareness, and model lifecycle sustainability
• Link business value to the Product Backlog, explicitly identifying:
  • Where AI or automation contributes to efficiency, risk reduction, or customer value
  • Where non‑AI solutions are preferable, ensuring pragmatic and value‑driven decisions
• Represent the business intent behind AI usage, ensuring the squad understands:
  • Why AI is used
  • What decisions it supports or automates
  • What human oversight is required

2. Stakeholder & Customer Centricity (AI Context)

• Identify and manage stakeholders (business sponsors, operations, risk (EU AI Risks associated as well), compliance, legal, IT, data, architecture).
• Collect and federate stakeholder input on:
  • Business outcomes
  • Regulatory constraints
  • AI acceptability (risk appetite, explainability, auditability)
• Guide the squad towards customer‑centric and user‑centric AI solutions, ensuring:
  • Transparency of AI‑driven decisions
  • Clear communication of AI limitations and confidence levels

3. Backlog Management & Story Definition (AI‑Ready)

• Own and manage the Product Backlog, ensuring it is:
  • Complete, transparent, prioritized, and understood
  • Inclusive of AI lifecycle work, not just features
• Effectively write and slice stories that may include:
  • Data sourcing and preparation
  • Feature engineering (transforming raw data into model‑ready inputs)
  • Model inference integration (how predictions are consumed by systems)
  • Human‑in‑the‑loop controls (human validation or override of AI outputs)
• Ensure stories include AI‑relevant acceptance criteria, such as:
  • Accuracy or quality thresholds
  • Explainability requirements
  • Monitoring and logging expectations

4. Collaboration with Epic Owner & TPO (AI Alignment)

(TPO = Technical Product Owner, responsible for technical coherence)

• Work closely with the Epic Owner and TPO to:
  • Maximize business value from AI and data capabilities
  • Align AI initiatives with strategic priorities at epic and feature level
  • Co‑own business objectives, including AI‑enabled outcomes
• Refine Features into Product Backlog Items (PBIs) that reflect:
  • Business intent
  • Technical feasibility
  • AI risk and compliance constraints

5. Delivery Oversight & Risk Management (AI & Regulatory)

• Oversee delivery stages and ensure all risks are identified and mitigated, including:
  • Regulatory risks (e.g. CSDR – Central Securities Depositories Regulation)
  • Compliance and data protection (e.g. GDPR – General Data Protection Regulation)
  • Security and architecture risks
  • AI‑specific risks
    • Model bias
    • Lack of explainability
    • Data drift (changes in data patterns over time)
    • Model drift (degradation of model performance in production)
• Ensure AI solutions comply with:
  • Internal AI governance frameworks
  • Model risk management expectations
  • Audit and traceability requirements

6. Sprint Execution & Value Validation

• Define with the squad:
  • Sprint goals
  • Sprint content
  • Readiness of AI‑related work (data availability, environments, dependencies)
• Facilitate sprint reviews and demonstrations, ensuring:
  • AI outcomes are explained in business terms
  • Limitations and confidence levels are transparently communicated
• Validate and accept or reject delivered stories and features, including:
  • Verification that AI outputs meet agreed acceptance criteria
  • Confirmation that monitoring and controls are in place

7. Measurement, KPIs & Continuous Improvement (AI‑Informed)

• Define and pilot Product and Business KPIs, with support from senior colleagues, including:
  • Traditional KPIs (throughput, adoption, value delivered)
  • AI‑specific indicators, such as:
    • Prediction quality trends
    • Automation rates vs. manual intervention
    • Exception and override frequency
• Actively collect feedback from the squad and stakeholders and translate it into backlog improvements.
• Assess and demonstrate value delivered at squad level (e.g. squad health check boards), ensuring AI contributions are measurable and defensible

Role Scope & Support

• Operates on products of limited complexity, uncertainty, and dependencies, such as:
  • Mature or end‑of‑life products
  • Well‑defined operational scopes
  • AI components with controlled impact and clear governance
• Receives guidance from senior colleagues for:
  • Strategic decisions
  • Complex prioritization trade‑offs
  • AI‑related risk or compliance decisions

Key Competencies (AI‑Infused)

• Strong Product Ownership fundamentals (Agile, backlog management, value prioritization)
• AI and data literacy, including:
  • Understanding of the AI lifecycle (data → model → deployment → monitoring)
  • Ability to translate business needs into AI‑ready requirements
• Awareness of AI governance, compliance, and ethical considerations
• Ability to collaborate effectively with:
  • Data Scientists
  • Machine Learning Engineers
  • Architects and Risk/Compliance stakeholders

Final Note (Positioning)

This role does not require hands‑on model building, but it does require sufficient AI technology stack understanding to:

• Ask the right questions
• Prioritize the right work
• Ensure AI delivers real, compliant, and sustainable business value

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