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 with statistics fundamentals, distributions, and sampling sufficient to ask the right questions of data science and ML teams. Demonstrated experience building and owning end-to-end enterprise AI roadmaps, including knowledge management, conversational AI, and document intelligence platforms at scale. Track record of quantifying and communicating AI product value to executive stakeholders, including ROI framing, AUM growth attribution, and operational efficiency metrics. This position will remain posted for a minimum of three business days from the date posted or until a sufficient/appropriate candidate slate has been identified Note: Employment-based non-immigrant visa sponsorship and/or assistance is not offered for this specific job opportunity. Compensation and Benefits Base salary range and benefits information for this position are being included in accordance with requirements of various state/local pay transparency legislation. Please note that base salaries may vary for different individuals in the same role based on several factors, including but not limited to location of the role, individual competencies, education/professional certifications, qualifications/experience, performance in the role and potential for revenue generation. Compensation The base salary compensation range being offered for this role is $225,000-$300,000 USD per year. This role is also eligible for an annual short-term incentive bonus Company Benefits WTW provides a competitive benefit package which includes the following (eligibility requirements apply): Health and Welfare Benefits: Medical (including prescription coverage), Dental, Vision, Health Savings Account, Commuter Account, Health Care and Dependent Care Flexible Spending Accounts, Group Accident, Group Critical Illness, Life Insurance, AD&D, Group Legal, Identify Theft Protection, Wellbeing Program and Work/Life Resources (including Employee Assistance Program) Leave Benefits: Paid Holidays, Annual Paid Time Off (includes paid state/local paid leave where required), Short-Term Disability, Long-Term Disability, Other Leaves (e.g., Bereavement, FMLA, ADA, Jury Duty, Military Leave, and Parental and Adoption Leave), Paid Time Off ( Retirement Benefits: Contributory Pension Plan and Savings Plan (401k). All Level 38 and more senior roles may also be eligible for non-qualified Deferred Compensation and Deferred Savings Plans. Pursuant to the San Francisco Fair Chance Ordinance and Los Angeles County Fair Chance Ordinance for Employers, we will consider for employment qualified applicants with arrest and conviction records. EOE, including disability/vets #J-18808-Ljbffr WTW