Beyond Banking: Generative AI Transformation in Insurance and Wealth Management

As generative AI (Gen AI) reshapes the financial services landscape, its transformative potential is extending well beyond banking—reaching deep into the insurance and wealth management sectors. These industries face a unique set of challenges: complex risk modeling, intricate claims processes, stringent regulatory requirements, and the growing demand for hyper-personalized client engagement. To unlock the full value of Gen AI, insurers and wealth managers must address not only technological hurdles but also organizational, cultural, and operational barriers. Publicis Sapient’s five debts framework—technology, data, process, skills, and culture—offers a proven path to sustainable Gen AI adoption tailored to these verticals.

The Five Debts Framework: A Blueprint for Gen AI Success

1. Technology Debt

Legacy systems and fragmented architectures are especially prevalent in insurance and wealth management, where decades of incremental IT investments have resulted in tangled infrastructures. This slows innovation and complicates the integration of Gen AI solutions. Modernizing core systems—migrating to cloud-native, modular platforms and leveraging accelerators like Sapient Slingshot—enables real-time data access and seamless AI integration. For example, a global asset manager partnered with Publicis Sapient to unify data access and streamline operational processes, accelerating decision-making cycles while ensuring compliance and traceability.

2. Data Debt

Both sectors are data-rich but often data-siloed. Insurers must aggregate and analyze vast amounts of structured and unstructured data for underwriting, claims, and risk modeling, while wealth managers require unified client views for personalized advice. Poor data quality and lack of governance hinder Gen AI’s effectiveness and regulatory compliance. Moving to cloud-based, unified data architectures and implementing rigorous data governance protocols are essential. In one case, a UK-based retail bank (with wealth management operations) accelerated time to insights for data scientists, enhancing productivity and enabling more responsive client service.

3. Process Debt

Manual, paper-based, or inconsistent processes are common in insurance claims and wealth management onboarding. These not only limit scalability but also introduce risk and inefficiency. Gen AI and robotic process automation (RPA) can automate routine tasks—such as document processing, compliance checks, and claims triage—freeing up human talent for higher-value activities. For instance, AI-powered document imaging and automation at a multinational investment bank streamlined unstructured data handling, saving tens of millions of dollars and driving significant process efficiencies.

4. Skills Debt

The shortage of Gen AI talent—spanning data science, compliance, and business strategy—is a critical barrier. Upskilling and reskilling are vital to building a self-sufficient AI operating model. Establishing AI centers of excellence, investing in targeted training, and partnering with experienced providers can accelerate capability building. Publicis Sapient’s work with Deutsche Bank, for example, included building an AI/ML catalog and fostering continuous learning to drive sustainable transformation.

5. Cultural Debt

A risk-averse, siloed, or change-resistant culture can undermine even the most promising Gen AI initiatives. Success requires a shift from isolated experimentation to enterprise-wide innovation. This means fostering an AI mindset, empowering cross-functional teams, and promoting agile ways of working. Responsible AI usage, transparency, and continuous learning are essential to align innovation with regulatory expectations and client trust.

Unique Industry Challenges and Gen AI Use Cases

Insurance: Risk Modeling, Claims Automation, and Compliance

Wealth Management: Hyper-Personalization and Advisor Enablement

Lessons Learned from Publicis Sapient’s Client Work

Recommendations for Sustainable Gen AI Adoption

  1. Assess Readiness: Evaluate current technology, culture, skills, processes, and data maturity. Identify gaps and prioritize high-value use cases.
  2. Build the Foundation: Modernize core systems and data platforms. Establish governance frameworks and AI centers of excellence.
  3. Pilot and Scale: Launch targeted Gen AI pilots with clear success metrics. Use agile, cross-functional teams to iterate and refine solutions.
  4. Embed and Sustain: Integrate Gen AI into business-as-usual operations. Continuously upskill teams, monitor outcomes, and adapt to evolving regulations and market needs.
  5. Partner for Success: Choose providers with proven AI expertise and a track record of delivering transformation at scale.

The Path Forward

The future of insurance and wealth management belongs to organizations that can break free from legacy constraints and harness Gen AI as a driver of innovation, efficiency, and client value. By systematically addressing technology, data, process, skills, and cultural debts, these sectors can move from incremental change to enterprise-scale transformation. Publicis Sapient stands as a trusted partner for this journey—combining deep industry expertise, proven frameworks, and a relentless focus on outcomes. With Gen AI as the catalyst, insurers and wealth managers can redefine what’s possible for their clients and their business.

Ready to accelerate your Gen AI journey? Connect with Publicis Sapient’s experts to unlock the full potential of generative AI in insurance and wealth management.