Director Product Management - AI (Jackson)

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Director Product Management - AI

ARE YOU AN IDEAL CANDIDATE?

We are looking for a strategic, customer-focused leader who embodies Sedgwick’s values of empathy, accountability, collaboration, innovation, growth, and inclusion. The ideal candidate is passionate about transforming enterprise data into measurable value through modern data products, AI-powered experiences, and secure, governed platforms that enable better decisions at scale.

PRIMARY PURPOSE :

The Director, Product Management – AI Enabled Enterprise Data Products is responsible for defining and executing the product strategy, roadmap, and business outcomes for Sedgwick’s enterprise AI-enabled data product portfolio.

This role owns a portfolio of strategic data products including:

• Enterprise AI Analyst and conversational analytics platforms that allow users to ask natural language questions and receive trusted insights, visualizations, recommendations, and analysis.

• Enterprise data sharing, data exchange, marketplace, semantic layer, data catalog, and self-service analytics capabilities.

• AI-enabled operational decision support products and analytical applications.

The Director serves as the bridge between business strategy, data strategy, AI innovation, technology execution, governance, and client value realization. This role leads Product Managers and Product Owners while partnering with Data Engineering, Architecture, Security, Governance, AI Engineering, Analytics, and Business leaders to deliver secure, scalable, governed, and measurable enterprise capabilities.

ESSENTIAL FUNCTIONS AND RESPONSIBILITIES

Define Product Vision and Strategy

• Define and own the multi-year vision, strategy, roadmap, business model, and investment priorities for enterprise AI and data products.

• Establish a product portfolio that enables business users, clients, and operational teams to discover, access, share, and analyze trusted enterprise data.

• Drive AI-powered analytical experiences that allow users to interact with enterprise data through natural language and conversational interfaces.

• Partner with executive leadership to identify opportunities that improve decision‑making, efficiency, client experience, revenue growth, and cost optimization.

• Develop business cases, success measures, portfolio‑level OKRs, and executive communications on progress, risks, and architectural implications.

Oversee, coordinate & support development work

• Oversee delivery across multiple teams, ensuring alignment between product strategy, technical execution, architectural direction, and client outcomes.

• Set expectations for backlog quality, acceptance criteria, prioritization, capacity planning, dependency management, and delivery risk mitigation.

• Own product portfolio KPIs and contributions to Product Group and enterprise OKRs, including quality, efficiency, sustainability, and value delivery.

• Ensure solutions comply with enterprise security, privacy, regulatory, governance, and responsible AI requirements.

• Serve as the senior escalation point for product-related dependencies, risks, and prioritization conflicts.

Own Enterprise Data Product Portfolio

• Establish product management practices for enterprise data products, including data‑as‑a‑product principles.

• Define and manage reusable, governed data products supporting claims, finance, compliance, operations, client, and executive reporting needs.

• Drive adoption of semantic models, business glossaries, metadata, cataloging, and self‑service analytics capabilities.

• Lead product strategy for enterprise data‑sharing solutions for internal consumers, clients, partners, and third‑party integrations.

• Ensure data products are reliable, scalable, discoverable, secure, usable, and supported by lineage and governance.

Drive AI Analyst and Conversational Analytics Capabilities

• Own the product vision for AI‑powered analytics that provide trusted answers, insights, metrics, visualizations, and recommendations through natural language interactions.

• Partner with business teams to identify high‑value analytical use cases and decision‑support opportunities.

• Define requirements for semantic layers, knowledge models, retrieval experiences, governance controls, trust mechanisms, and user experiences.

• Measure AI product effectiveness through adoption, accuracy, user satisfaction, business impact, and operational efficiency.

• Ensure AI‑generated insights are explainable, governed, auditable, and aligned with enterprise policies.

Enable Secure Enterprise Data Sharing

• Define strategy and roadmap for enterprise‑scale data exchange and sharing capabilities.

• Partner with Security, Governance, Privacy, and Legal teams to establish secure and compliant data‑sharing frameworks.

• Drive capabilities for role‑based access, entitlements, auditing, lineage, masking, client access controls, and data‑sharing agreements.

• Enable governed data product distribution across business units, clients, partners, and external ecosystems while supporting growth, performance, compliance, and resilience.

Stakeholder Engagement and Value Realization

• Build strong relationships with business leaders, claims operations, finance, client services, technology leaders, and external stakeholders.

• Translate business objectives and operational challenges into actionable AI and data product strategies.

• Communicate roadmap progress, risks, dependencies, investment needs, and outcomes to executive leadership.

• Establish and monitor portfolio‑level KPIs, OKRs, and value realization metrics that trace strategy through implementation to business impact.

People Leadership

• Lead and develop Product Owners, Scrum Masters, and product delivery teams.

• Establish product management best practices across AI, data, analytics, and platform teams.

• Coach teams on customer‑centric product development, Agile practices, outcome‑based planning, and value measurement.

• Foster a culture of innovation, experimentation, accountability, continuous learning, and collaboration.

QUALIFICATIONS

Education & Licensing

• Bachelor's degree in Business, Computer Science, Information Systems, Engineering, Data Science, or related field required.

• Master's degree preferred.

Experience

• 10+ years of progressive experience in Product Management, Data Products, Analytics Platforms, AI Products, or Enterprise Technology.

• 5+ years leading Product Managers or Product Owners and managing product portfolios.

• Experience owning enterprise‑scale data, analytics, AI, platform, reporting, self‑service analytics, semantic modeling, AI/ML, or conversational AI products.

• Experience defining product strategy, roadmaps, business cases, and measurable outcomes for data‑driven products.

• Experience working in Agile or scaled Agile environments and leading cross‑functional teams across Architecture, Data Engineering, Analytics, Security, Governance, and Business stakeholders.

Skills & Knowledge

• Expert knowledge of Agile methodologies, product operating models, portfolio management, prioritization, roadmap development, and outcome measurement.

• Ability to translate business and portfolio strategy into product vision, execution standards, and measurable value.

• Deep understanding of Data as a Product principles, modern data platforms, data mesh, data fabric, governance, semantic modeling, metadata, lineage, and enterprise analytics architectures.

• Strong understanding of AI, generative AI, conversational analytics, augmented decision‑support platforms, and responsible AI practices.

• Experience with enterprise data sharing, security, privacy, governance, compliance frameworks, and risk management.

• Strong executive communication, stakeholder management, product adoption, organizational change, and people leadership capabilities.

• Ability to balance business value, technical complexity, investment, risk, and operational impact.

Success Measures

The Director will be evaluated on:

• Delivery of a clear enterprise AI and data product strategy, roadmap, and investment plan.

• Adoption and measurable business value from AI‑enabled analytics, data products, and data‑sharing capabilities.

• Improved data product quality, usability, discoverability, governance, security, and scalability.

• Effective stakeholder alignment, executive communication, prioritization, and value realization.

• Development of high‑performing product teams and]]>