AI Product Manager: Strategy to AI Delivery

Description 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. The AI Product Manager shapes how AI and data-driven capabilities move from concept to production. Where the portfolio includes machine learning models, generative AI features, or intelligent automation, this role is the critical bridge—translating real-world business problems into modelling requirements, assessing data readiness and technical feasibility, and defining success metrics that capture both model performance and business impact. The Requirements 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; translate 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; engaging 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; propose 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; manage 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; confirm 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 maintain clear communication materials (requirements traceability, release notes inputs, decision logs, status updates). • Proactively surface risks, gaps, and cross-team dependencies; drive timely resolution. Quality, Adoption & Continuous Improvement • Defines and track requirement-level success measures (e.g., process efficiency gains, reduced call drivers, improved completion rates, error reduction). • Gathers post-release feedback, triage issues/enhancements, and feed 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: assess data availability, model complexity, and ROI before requirements are finalised. • Translates business problems into clear data and modelling needs; define 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 (e.g. 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: define thresholds for model drift, bias, and performance decay; ensure 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; bridging 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 for planned sprints/releases. • Stakeholder satisfaction with requirement process and communication cadence. • UAT outcomes: reduced defect leakage; acceptance criteria met. • Post-release outcomes tied to the requirement intent (adoption, efficiency, reduced issues). • Adoption of AI-enabled features across the portfolio, with clear, measurable, evidence of client and operational impact (e.g. time saved, decision quality, process automation rates). • Proportion of the product roadmap incorporating AI/GenAI capabilities, with tracked progression from pilot to scaled deployment. • For AI features: model performance metrics (accuracy, fairness, latency) meet defined thresholds at launch; post-launch monitoring in place with documented drift and bias review cadence. • Measurable business value delivered from AI platform investments, evidenced by quantified ROI (e.g. hours saved, revenue influenced, cost reduction) tied to specific product capabilities. • Product governance compliance rate: AI use cases reviewed against responsible AI framework prior to deployment; lifecycle management checkpoints met on schedule. Qualifications The Qualifications • 7+ years of experience in product management, business analysis, or digital product delivery roles. • Demonstrated strength in requirements elicitation, documentation, and stakeholder facilitation (workshops, interviews, process mapping). • Strong analytical skills—able to synthesize data into insights, define measurable requirements, and support prioritization decisions. • Excellent written and verbal communication skills; able to translate technical AI trade-offs (cost, latency, accuracy) into clear terms for non-technical stakeholders. • Experience working in regulated environments and coordinating with compliance/legal/risk • Highly organized, detail-oriented, and comfortable managing multiple stakeholders and competing priorities. • Familiarity with product tools (e.g., Jira/Azure DevOps, Confluence, Miro) and requirements documentation practices. • Experience supporting data/analytics or AI-enabled product features, including: defining requirements for model outputs; understanding ML pipelines (training, validation, inference); working with model evaluation concepts such as precision/recall, ROC-AUC, and calibration; and establishing governance, monitoring, and iteration plans. • Understanding of AI ethics considerations: bias and fairness, transparency and explainability, privacy, and compliance—especially in regulated industries. • Data literacy: comfortable w