Insurance providers are under increasing pressure to deliver more growth and efficiency in complex operating environments. AI has the potential to help address that pressure, but to scale it effectively requires a shift in the underlying data foundation.
Insurance operates under strict regulatory conditions, and a complex data landscape, riddled with fragmented systems, inconsistent data definitions, and locally implemented rules. This creates significant risk and limitations for agentic AI, as inconsistent data produces inconsistent results and inappropriate controls lead to inappropriate data use.
To scale AI effectively across an enterprise or portfolio, insurers need a shared and consistent understanding of data, together with precise and consistent real-time enforcement of what data may be used, how, and under which conditions.
Fixing the data foundation
Insurance data is distributed across policy platforms, claims systems, billing environments, and service and partner applications. These systems often represent the same entities differently. Customer identifiers vary, policy structures differ, and relationship data is incomplete or inconsistent across channels.
While human teams can often compensate for gaps and inconsistencies, AI agents cannot. They operate on the data and logic available to them in the moment. When data is inconsistent, AI systems amplify risk. The same customer can receive different outcomes across channels, and sensitive data can be pulled into decisions that were never intended to use it.
For AI agents to operate effectively, they need a unified view of customers, policies, claims, billing relationships, agents, and partners, with rich context that allows them to retrieve and use data from a common and consistent reference point.
A unified data layer supports this by connecting systems of record, aligning how core entities are represented across environments, and preserving the context that gives that data meaning across workflows. This gives AI systems a consistent foundation for retrieval, interpretation, and action, and gives teams a stronger basis for trusting how AI reaches its outputs.
Governing data use in AI workflows
Insurance data is deeply contextual. Health information, financial indicators, underwriting attributes, beneficiary relationships, and claims histories all have legitimate uses. Their appropriateness depends on the task being performed and the purpose of the interaction.
As AI workflows assemble data in real time, data use has to be determined in real time as well. For each task, the system needs a clear and enforceable basis for what data it should use.
This is where governance becomes operational. It defines and enforces how data may be used inside AI-supported workflows so outputs remain aligned with business intent and regulatory expectations.
That governance has to hold across channels, applications, partner touchpoints, and AI systems. It should reflect the conditions around the interaction, including jurisdiction, consent status, customer relationship, delegated authority, product context, and contractual boundaries.
Granular, context-aware control makes this enforceable in real time. It allows constraints to be applied at the level of specific policies, claims, payouts, or attributes, so AI systems use data that is appropriate to the task at hand.
This means AI systems can operate with the speed and flexibility insurers want, while data use remains aligned with purpose, policy, and regulatory expectations. Decisions become more consistent, outcomes more defensible, and scale more achievable because the conditions for data use are enforced inside the workflow itself.
The unified data control layer for scalable insurance AI
AI scale in insurance depends on a shared and consistent understanding of data, and precise, consistent enforcement of how data may be used.
With a unified data layer, AI systems retrieve context from a stable reference point. With governed, context-aware control, they use data in ways that are appropriate to the workflow, purpose, and conditions of the interaction.
This is what allows AI to scale beyond - data remains traceable, usage remains aligned with purpose and outputs remain trustworthy.
