Advances in AI, especially Gen AI and Agentic AI, are creating significant opportunities but also sharpening a critical question for insurers: just because a process can be automated, should it be?
Executive Summary
Manuel Rodriguez Vera of Capgemini's WNS unit offers practical approaches for insurers to embed control, privacy and compliance into AI systems while enabling collaboration and innovation across jurisdictions. Approaches discussed include restricting data and AI workloads to specific geographies and an emerging approach to machine learning known as federated learning, among others.High-impact AI use cases offer the potential to reduce costs and risk; improve productivity and efficiency; and drive revenue across sales, underwriting, claims and servicing. Yet, as insurers accelerate AI adoption, they confront growing risks that go beyond effectiveness to critical areas like cybersecurity, privacy and explainability. In 2025 alone, data breaches affected nearly 300 million individuals, underscoring how quickly trust can erode. At the same time, customer expectations and regulatory scrutiny around data usage and model transparency are intensifying, with increasingly direct questions around how data is used and how models operate.
