Senior Manager, Operations Analytics & AI Enablement

Prudential’s purpose is to be partners for every life and protectors for every future. Our purpose encourages everything we do by creating a culture in which diversity is celebrated and inclusion assured, for our people, customers, and partners. We provide a platform for our people to do their best work and make an impact to the business, and we support our people’s career ambitions. We pledge to make Prudential a place where you can Connect, Grow, and Succeed.

This role is responsible for driving data-driven transformation in Policy Servicing (managing and updating insurance policies) and Customer Servicing (customer support and inquiries) operations. The role integrates advanced analytics and artificial intelligence (AI) into everyday insurance operations to optimize efficiency, enhance customer experience, and enable scalable automation. This manager will leverage tools like Databricks for big data processing, Python and SQL for analysis, and AI technologies (including developing co-pilot style bots and data analytics assistants) to empower teams and streamline policy and customer service workflows. By combining deep insurance domain knowledge with expertise in data analytics and AI enablement, the Senior Manager ensures that the organization’s servicing processes become smarter, faster, and more proactive in meeting customer needs.

Job Descriptions:

Data & AI Strategy

Define and execute a data analytics and AI strategy for policy and customer servicing operations. Develop a transformation roadmap that aligns AI initiatives with business goals to improve service quality and efficiency. This involves identifying high-impact use cases (e.g. automating policy updates, AI-assisted customer inquiries) and rewiring processes to embed analytics and AI solutions end-to-end rather than as bolt-on tools. Ensure the strategy delivers measurable business value in terms of speed, cost, and customer experience improvements

AI Solution Development & Automation

Lead the development and implementation of AI-driven solutions to automate and enhance policy servicing and customer servicing tasks. This includes building and deploying predictive models and intelligent automation such as chatbots or RPA bots for routine service requests. A core responsibility is to create a custom “co-pilot” AI agent – an intuitive analytics bot – that allows team members (even those without coding skills) to automate their work and retrieve insights easily via natural language. By leveraging technologies like machine learning and natural language processing, the manager enables straight-through processing of service transactions (e.g. automatic policy data updates, smart triage of customer queries) and reduces manual effort and errors.

Data Analysis & Insights

Build-from-scratch advanced analytics to turn operational data into actionable insights for process improvement. Use tools like Databricks for big data processing and SQL/Python for analysis to identify trends, bottlenecks, and opportunities within servicing workflows. Provide regular reporting and dashboards on key metrics (e.g. turnaround times, volumes, error rates) and conduct deep-dive analyses (such as root-cause analysis of service failures or policy persistency trends). These insights will inform decisions such as where to streamline processes or how to personalize customer engagement, thereby continuously improving operations.

Team Enablement & Training

Empower non-technical team members to leverage data and AI tools in their daily work. This involves designing user-friendly interfaces for the custom AI bot and self-service analytics platforms, and conducting training sessions/workshops so that operations staff can confidently use these tools to automate tasks and make data-driven decisions. The manager drives a culture shift towards data literacy and innovation, mentoring team members in analytical thinking. By focusing on change management and stakeholder buy-in, ensure that new processes and technologies are adopted smoothly – addressing any resistance through communication and hands-on support. Ultimately, success is seen when frontline servicing teams routinely use AI insights or bots to work smarter, not harder.

Cross-Functional Collaboration

Collaborate closely with IT, Data Engineering, and Operations teams to ensure analytics and AI solutions are effectively integrated into the existing tech ecosystem and workflows. Work with IT on data infrastructure (ensuring access to quality data from policy admin systems, CRM, etc.) and with Customer Service managers to align AI enhancements with real-world needs. Partner with underwriting, claims, and product teams to share insights and ensure consistency across the insurance value chain. Additionally, communicate project progress, wins, and learnings to senior leadership, championing the value of AI enablement in operations and securing ongoing support and resources for these initiatives

Performance Monitoring & Governance

Establish governance and continuously monitor outcomes of analytics and AI initiatives. Define clear KPIs (as outlined below) for each project and regularly track performance against baselines to quantify impact (e.g. reduction in processing time or error rate). Ensure robust data governance, security, and regulatory compliance for all solutions – especially when dealing with sensitive customer policy data. This includes working with risk and compliance teams to review AI models for fairness and accuracy, and implementing controls to prevent errors. Use feedback loops to refine models and processes (for example, capture user feedback on the AI co-pilot’s suggestions and improve its algorithms over time). By proactively managing both the technical and change management aspects, ensure sustainable, long-term success of AI enablement programs in operations.

Job Requirements:

• Bachelor’s degree in Analytics, Data Science, Computer Science, Business Administration, or a related field is required. (An advanced degree in data analytics, MBA, or related discipline is advantageous but not mandatory.)
• A minimum of 8-10 years of experience in data analytics, business intelligence, or operations strategy, with at least 3-5 years in a leadership role managing analytics or process improvement teams. Experience in the insurance or financial services industry, specifically in operations or servicing functions, is required. The ideal candidate has a proven track record of executing analytics projects and implementing AI/automation solutions in an operational context.
• Deep familiarity with insurance policy servicing and customer service processes is essential.
• Professional certifications that demonstrate expertise in insurance operations or analytics are strongly desired. Relevant technical certifications (e.g. Microsoft Certified: Azure Data Engineer, or certifications in data science/AI) are a plus if they support the technical skillset. (Note: Lean Six Sigma or process improvement certifications, while not mandatory, would further indicate capability in driving operational excellence.)

 

Prudential is an equal opportunity employer. We provide equality of opportunity of benefits for all who apply and who perform work for our organisation irrespective of sex, race, age, ethnic origin, educational, social and cultural background, marital status, pregnancy and maternity, religion or belief, disability or part-time / fixed-term work, or any other status protected by applicable law. We encourage the same standards from our recruitment and third-party suppliers taking into account the context of grade, job and location. We also allow for reasonable adjustments to support people with individual physical or mental health requirements.