Associate Director / Senior Manager, AI Product & Business Partner - Marketing

Associate Director / Senior Manager, AI Product & Business Partner - Marketing Position Responsibilities: 1. Domain Leadership & Strategy • Lead the Marketing Domain Squad and act as the primary AI partner to Marketing leadership. • Develop and maintain the Marketing AI and analytics roadmap aligned with business strategy, customer priorities, and growth objectives. • Identify and prioritize high-value AI and analytics use cases across customer acquisition, engagement, personalization, campaign management, content development, and marketing operations. • Partner with senior Marketing stakeholders to define opportunities, investment priorities, success metrics, and expected business outcomes. • Establish a culture of data-driven decision making across Marketing functions. • Align the Marketing AI roadmap with related initiatives across Distribution, Customer Experience, and other business domains. 2. Business Partnership • Build strong relationships with Marketing executives, customer strategy leaders, campaign teams, channel owners, and other relevant business stakeholders. • Translate marketing and customer challenges into clearly defined AI and analytics opportunities with measurable value realization plans. • Act as a trusted advisor to Marketing leaders on AI, advanced analytics, Generative AI, personalization, and emerging marketing capabilities. • Provide clear recommendations on how data and AI can improve customer targeting, engagement, conversion, and marketing effectiveness. • Lead executive-level discussions on AI priorities, investment requirements, delivery progress, risks, adoption, and expected business impact. 3. AI Product Ownership & Value Realization • Own the Marketing AI product portfolio and roadmap. • Lead end-to-end delivery of AI and analytics initiatives from opportunity identification and product definition through deployment, adoption, and ongoing improvement. • Work closely with Marketing and Technology stakeholders to define product vision, business requirements, customer needs, success metrics, and delivery priorities. • Maintain and prioritize the Marketing AI product backlog based on business value, customer impact, delivery feasibility, data readiness, and risk. • Ensure AI solutions are embedded into marketing processes, customer journeys, campaign workflows, and decision-making practices. • Define and track KPIs covering adoption, campaign performance, conversion, customer engagement, productivity, revenue contribution, and return on marketing investment. • Drive realization of tangible business benefits and communicate progress and outcomes to senior management. • Gather feedback from business users and use performance data to continuously improve AI products after deployment. 4. Advanced Analytics & AI Solutions • Apply advanced analytics, machine learning, and Generative AI to address Marketing business challenges, including: • Customer segmentation and audience identification • Propensity, response, and conversion modelling • Next-best-action and next-best-offer recommendations • Personalized customer engagement and communications • Campaign targeting and performance optimization • Lead generation, qualification, and prioritization • Customer lifecycle and behavioural analytics • Cross-sell and upsell opportunity identification • Customer retention and persistency improvement • Marketing attribution and effectiveness measurement • AI-assisted content development and localization • Customer and market insight generation • Marketing workflow and productivity enhancement • Ensure analytical approaches and AI outputs are appropriate for the business objective and can be translated into practical actions. • Assess emerging AI capabilities and identify opportunities to reuse and scale successful solutions across Marketing and other business domains. 5. Customer Data & Measurement • Partner with Data, Marketing, and Technology teams to identify the customer data required to support Marketing AI products and analytical use cases. • Support the development of consistent customer definitions, segmentation approaches, customer features, and measurement frameworks. • Define appropriate testing and measurement approaches, including controlled experiments, campaign test-and-learn frameworks, and performance tracking. • Ensure marketing recommendations and models are evaluated against clear business baselines and measurable outcomes. • Work with relevant stakeholders to address data quality, accessibility, consent, and usability issues that may affect delivery or adoption. 6. Governance & Risk Management • Ensure compliance with regulatory requirements, model governance standards, responsible AI principles, data privacy requirements, and enterprise AI controls. • Partner with Risk, Compliance, Legal, Data Privacy, and Technology teams to ensure appropriate oversight throughout the AI solution lifecycle. • Ensure appropriate controls are applied to customer targeting, personalization, AI-generated content, and customer communications. • Support model validation, performance monitoring, outcome tracking, and ongoing governance activities. • Identify and manage risks relating to customer data usage, consent, explainability, fairness, brand reputation, and the accuracy of AI-generated outputs. • Ensure AI solutions operate within approved business, regulatory, and ethical boundaries. 7. Squad Leadership • Lead a Marketing Domain Squad comprising data scientists and AI engineers, together with relevant cross-functional delivery partners, to deliver measurable business outcomes. • Set clear squad priorities and drive planning, resource allocation, execution, and performance management against the Marketing AI roadmap. • Provide business context and strategic direction to ensure technical solutions address genuine Marketing and customer needs. • Foster close collaboration between Marketing stakeholders, data scientists, AI engineers, Technology teams, a