AI Product Manager

WTW•New York, NY

About The Position

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.

Requirements

Business Requirements & Discovery
Analysis, Prioritization Support & Decision Enablement
Coordination with Product Owner & Delivery Teams
Stakeholder Management & Communication
Quality, Adoption & Continuous Improvement
AI & Data Product Management
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.

Responsibilities

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.
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.
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.
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.
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.
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.