AI Product Manager: Strategy-to-Delivery Leader

The Role The AI Product Manager is a pivotal connector between business strategy and intelligent product delivery—translating complex organisational needs into clear, prioritised requirements and driving coordinated execution across Product Owners and cross-functional teams. This role sits at the intersection of business analysis and AI enablement, accountable for requirements gathering, stakeholder alignment, and ensuring that every product initiative is well‑defined, technically feasible, and tied to measurable outcomes. Responsibilities • Business Requirements & Discovery • Leads requirements discovery across stakeholders through workshops, interviews, and process reviews. • Elicits, documents, and validates business needs, user needs, pain points, and desired outcomes; translates into clear problem statements and requirements. • Develops artifacts such as business requirement documents (BRDs), epics/features, use cases, user journeys, acceptance criteria, and process flows. • Ensures requirements reflect regulatory, legal, privacy, security, and operational considerations; engages the right SMEs early. • Analysis, Prioritization Support & Decision Enablement • Analyzes qualitative and quantitative inputs (client feedback, operational metrics, adoption/usage data, defect trends) to refine requirements and recommendations. • Supports the Product Leader with data‑backed insights, business cases, and trade‑off options (scope, timeline, cost, risk). • Helps assess value, impact, dependencies, and feasibility; proposes sequencing and release groupings for roadmap planning. • Coordination with Product Owner & Delivery Teams • Partners with Product Owners to convert business requirements into well‑groomed backlog items and sprint‑ready work. • Maintains continuous alignment between stakeholders and the delivery team; manages requirement clarifications, changes, and approvals. • Participates in agile ceremonies as needed (backlog refinement, sprint planning, demos, retros) to ensure intent and acceptance criteria are understood. • Coordinates UAT readiness and execution with business stakeholders; confirms delivered functionality meets defined requirements. • Stakeholder Management & Communication • Serves as a primary point of contact for product leaders and other relevant stakeholders on in‑flight requirements and upcoming deliverables. • Creates and maintains clear communication materials (requirements traceability, release notes inputs, decision logs, status updates). • Proactively surfaces risks, gaps, and cross‑team dependencies; drives timely resolution. • Quality, Adoption & Continuous Improvement • Defines and tracks requirement‑level success measures (e.g., process efficiency gains, reduced call drivers, improved completion rates, error reduction). • Gathers post‑release feedback, triages issues/enhancements, and feeds learnings back into the backlog. • Champions usability, data quality, and operational fit—ensuring solutions are intuitive, trusted, and supportable. • AI & Data Product Management • Leads feasibility framing for AI‑enabled features: assesses data availability, model complexity, and ROI before requirements are finalised. • Translates business problems into clear data and modelling needs; defines what "good" looks like for model outputs in terms of accuracy, fairness, and explainability. • Defines AI‑specific success metrics alongside business metrics—including model performance indicators (precision/recall, lift, false‑positive rates, latency) and outcome metrics tied to revenue or retention. • Works closely with data scientists, ML engineers, and designers to align on experimentation approaches. • Oversees post‑launch monitoring requirements: defines thresholds for model drift, bias, and performance decay; ensures feedback loops are built into the product. • Applies AI ethics and governance principles and ensures privacy and compliance obligations are embedded into requirements—particularly in regulated HWC contexts. • Communicates AI trade‑offs clearly to non‑technical stakeholders; bridges the gap between technical teams and business decision‑makers. • Leads enterprise‑scale GenAI roadmap planning, including prioritisation of knowledge management, conversational AI, document intelligence, and analytics use cases in alignment with organisational strategy and executive stakeholders. • Embeds responsible AI lifecycle management into product requirements, including governance frameworks, bias and fairness reviews, and iterative oversight mechanisms throughout model deployment. • Scopes and drives R&D initiatives for emerging AI patterns such as Retrieval‑Augmented Generation (RAG) and autonomous AI Agents, translating innovation lab findings into scalable product capabilities. • Success Metrics • Requirements quality: completeness, clarity, testability; reduced rework and churn in delivery. • On‑time readiness: backlog items "definition of ready" met