This paper explores how insurers can build the data foundation required to engineer autonomy, enabling the progression from AI-assisted and AI-augmented processes to more autonomous operations. It outlines a practical approach for transforming data into a trusted, reusable and business-ready asset that supports AI, analytics and operational decision-making at scale.

A strong data foundation goes beyond data integration. It requires shared business definitions, visible data quality, reusable data products, clear ownership and end-to-end traceability. By establishing these capabilities, insurers can reduce complexity, accelerate AI adoption and create a reliable foundation for future autonomous workflows.

Key highlights:

• A roadmap from AI assistance to autonomy
  Understand how data requirements evolve as insurers move from AI-assisted and AI-augmented capabilities to autonomous operations and why increasing levels of autonomy require greater data self-sufficiency.
• Building a modern insurance data foundation
  Explore the four core capabilities needed to support AI at scale: making data easy to find and access, creating consistent business meaning, enabling reuse across applications and establishing accountability and traceability.
• Data products as enablers of AI maturity
  Learn how reusable, business-focused data products help eliminate duplication, accelerate delivery and provide trusted information for underwriting, claims, servicing and other insurance workflows.
• A practical implementation journey
  Discover a four-stage approach comprising Discover, Connect and Clarify, Productize and Reuse and Operate, helping insurers align data investments with business priorities and AI outcomes.

Download the paper to learn how insurers can create a trusted data foundation that makes information easier to find, understand, trust and reuse, enabling the journey toward scalable AI adoption and autonomous insurance operations.