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
Description & 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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