Job Description
This job is with WTW, an inclusive employer and a member of myGwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly. 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