We are seeking a Senior Director, AI Product & Strategy to define and drive the company’s enterprise AI agenda. This leader will operate at the intersection of business strategy, product management, and applied AI, translating a rapidly evolving technology landscape into clear, actionable business outcomes. This is a highly visible, externally aware role that partners closely with enterprise business leaders and business-facing teams to identify, shape, and prioritize AI-driven opportunities. A core responsibility of this role is to bridge the gap between business intent and technical execution—ensuring that AI initiatives are well-defined, feasible, and positioned to deliver measurable impact. In addition, this role will play a key part in building AI product management capabilities in the organization, elevating how business needs are translated into scalable AI solutions. Key Responsibilities 1. Enterprise AI Strategy & External Point of View - Partner with the VP, Data, Analytics & Enterprise AI to define and evolve the enterprise AI strategy aligned with company priorities - Develop a forward-looking point of view on AI, including generative AI, agentic systems, automation, and advanced analytics - Stay current on rapidly emerging technologies, tools, and market trends, and translate them into clear strategic direction for the company - Provide thought leadership to executive stakeholders, synthesizing complex developments into actionable insights 2. Business Engagement & Use Case Identification - Serve as a strategic partner to enterprise business leaders across Enterprise Business Functions. - Lead structured conversations to: o Identify manual, inefficient, and high-friction workflows o Reimagine them using AI, automation, and advanced analytics - Collaborate with business-facing teams in Data & Analytics and Business Applications to: o Articulate the art of the possible o Bring credibility to conversations, having had the experience building and deploying Ai solutions to production. o Identify and define high-impact AI use cases - Build and maintain a pipeline of prioritized AI opportunities aligned to measurable business value 3. AI Product Translation & Requirements Definition - Lead the translation of business-level problem statements into clear AI product and solution definitions - Work closely with business-facing teams to:o Convert use cases into structured requirements, user journeys, and success metrics o Define data needs, model expectations, and operational workflows - Partner with AI Engineering and Data Science teams to ensure: o Requirements are feasible, well-scoped, and aligned with technical realities o Solutions are designed for scalability, maintainability, and enterprise integration - Act as the bridge between business stakeholders / business facing portfolio teams and technical teams, ensuring clarity, alignment, and shared ownership 4. AI Product Strategy & Portfolio Development - Define and guide the enterprise AI product and use case portfolio, ensuring alignment with strategic priorities - Establish frameworks for use case prioritization, balancing business impact, feasibility, and scalability - Drive clarity on: o What should be built o Why it matters o How success will be measured - Ensure AI initiatives are delivered as products (not one-off projects) with clear ownership, lifecycle management, and value tracking 5. Executive Communication & Influence - Communicate AI strategy, priorities, and progress to senior leadership and the executive committee with clarity and conviction - Build alignment by articulating: o Business value and ROI o Trade-offs, risks, and dependencies - Distinguish between high-impact opportunities and low-value or overhyped initiatives - Serve as a trusted advisor to leadership on AI investments and direction 6. Technical Credibility & Solution Shaping - Bring depth in AI/ ML to: o Evaluate solution approaches and architectures o Challenge assumptions and guide teams toward practical, high-impact implementations - Ensure alignment with enterprise data strategy, architecture, and governance - Help define guardrails for responsible, ethical, and secure use of AI 7. AI Product Capability Building - Build and develop AI product management capabilities within business-facing teams. - Establish best practices for: o Translating business needs into AI requirements o Structuring use cases and defining success metrics o Managing AI initiatives as products - Coach and mentor teams to improve their ability to: o Engage effectively with Data Science and Engineering o Drive clarity and discipline in AI solution definition Qualifications. Experience - 12 years of experience in product management, strategy, or enterprise technology leadership, with a strong focus on data and AI - Bachelor's degree in a relevant field (Computer Science, Information Systems, Business, Law, Data Management, or equivalent). Advanced degree preferred - Hands-on experience with building and deploying AI products to production. - Proven experience working with enterprise business functions to drive transformation through technology - Demonstrated success in translating business problems into scalable AI product/ technology solutions - Experience influencing executive stakeholders and shaping enterprise strategy AI / Technical Acumen - Strong understanding of: o Machine learning and advanced analytics o Generative AI (LL - Ms, copilots, agent-based systems) o Enterprise data platforms and AI architectures - Ability to: o Translate business needs into clear AI solution requirements o Distinguish between feasible, scalable solutions and experimental or low- impact ideas - Comfortable engaging deeply with Data Science and Engineering teams without being hands-on. Leadership & Communication - Exceptional ability to translate complex AI concepts into clear, business-relevant language - Strong executive presence with the ability to influence and align senior stakeholders - Demonstrated ability to operate in ambiguous, fast-moving environments and drive clarity - Proven ability to lead cross-functional initiatives and build consensus across diverse teams