Cluster Manager - Digital Platforms & AI

Job description

**Job Purpose** Bajaj Finservs Digital Platform consolidates all products and services into a single, advanced digital ecosystem. The vision is to build a highly efficient, intelligent Consumer AI platform that delivers seamless, personalized, and scalable access to all BFL offerings. As the Consumer AI platform Product Manager, you will lead the PMO and cross-functional delivery teams to design, build, and scale AI-enabled B2B products on a consumer-facing digital platform. You will collaborate closely with Business Centers of Excellence (COEs), Service teams, Operations, Marketing, and IT to ensure end-to-end product strategy, execution, and delivery. Key Responsibilities: - Lead the team responsible for conceptualizing, building, and delivering B2B products on a consumer AI-driven digital platform, ensuring business scalability and measurable outcomes. - Drive the integration of AI/ML capabilities (such as personalization, recommendations, intelligent workflows, automation, and insights) to enhance customer experience, operational efficiency, and business decision-making. - Design app and web journeys using a minimal-click, low-friction framework, leveraging AI to reduce customer effort and improve engagement. - Own and coordinate the creation of user stories, AI use cases, and functional documentation in collaboration with business units and stakeholders. - Refine and prioritize requirements to translate business needs into scalable, AI-powered digital solutions. - Partner with the Marketing and Design teams to deliver an intuitive, data-driven, and engaging UI/UX, informed by user behavior and AI insights. - Work closely with IT and engineering teams to ensure robust, secure, and efficient technology implementation, including AI model integration and platform performance. **Duties and Responsibilities** - Lead the team responsible for conceptualizing, building, and delivering B2B products on a consumer AI-driven digital platform, ensuring business scalability and measurable outcomes. - Drive the integration of AI/ML capabilities (such as personalization, recommendations, intelligent workflows, automation, and insights) to enhance customer experience, operational efficiency, and business decision-making. - Design app and web journeys using a minimal-click, low-friction framework, leveraging AI to reduce customer effort and improve engagement. - Own and coordinate the creation of user stories, AI use cases, and functional documentation in collaboration with business units and stakeholders. - Refine and prioritize requirements to translate business needs into scalable, AI-powered digital solutions. - Partner with the Marketing and Design teams to deliver an intuitive, data-driven, and engaging UI/UX, informed by user behavior and AI insights. - Work closely with IT and engineering teams to ensure robust, secure, and efficient technology implementation, including AI model integration and platform performance. - Oversee end-to-end delivery, ensuring timely execution, stakeholder alignment, and high-quality outcomes across multiple product streams. **Key Decisions / Dimensions** - AI use-case prioritization decisions: determining which AI capabilities (personalization, recommendations, automation, risk intelligence, insights) deliver maximum business and customer value. - Product roadmap and release prioritization decisions across B2B and B2C initiatives, balancing speed, impact, risk, and dependencies. - Platform architecture and integration approach decisions in partnership with IT, including AI model deployment, data pipelines, and system scalability. - Build vs. buy decisions for AI tools, platforms, and third-party integrations. - User experience and journey design decisions, including minimal-click flows, personalization logic, and AI-driven engagement models. - Data strategy decisions, including data sources, governance, quality standards, and usage for AI and analytics. - Delivery model decisions, including agile frameworks, sprint cadences, and release governance. - Resource allocation decisions across teams, vendors, and initiatives to ensure optimal delivery outcomes. - Risk, compliance, and security trade-off decisions related to AI usage, customer data, and platform functionality. - KPIs and success-metric decisions to measure platform performance, AI impact, and business outcomes. - Stakeholder dependency and escalation decisions to resolve conflicts and unblock delivery. - Continuous improvement and innovation decisions driven by customer insights, platform analytics, and emerging AI capabilities. **Major Challenges** - Driving AI adoption at scale while balancing regulatory, data privacy, security, and risk requirements inherent to financial services. - Aligning multiple stakeholders (Business COEs, Sales, Operations, Marketing, IT, Risk, Compliance) with competing priorities and timelines. - Translating complex business needs into AI-ready product solutions, including defining clear use cases, data requirements, and measurable outcomes. - Integrating legacy systems with modern AI-driven platforms without compromising performance, stability, or customer experience. - Ensuring high-quality data availability and governance to enable effective AI/ML models and personalization capabilities. - Managing end-to-end delivery across multiple product streams, vendors, and internal teams while maintaining speed and quality. - Balancing B2B and B2C requirements on a single consumer platform, ensuring both business scalability and seamless user experience. - Driving minimal-click, intuitive journeys across a diverse product portfolio without oversimplifying complex financial workflows. - Demonstrating tangible ROI from AI initiatives, including adoption, efficiency gains, and revenue impact. - Managing change and upskilling teams to adopt AI-first ways of working, agile delivery, and data-driven decision-making. - Maintaining