How insurers can build AI-ready data foundations that de-risk legacy modernization and unlock digital transformation.
Digital transformation programmes in insurance often stumble on a simple question: can we actually trust the data feeding our new journeys, analytics, and AI models? For many carriers, the honest answer is “not consistently.” Policy and claims data is scattered across legacy cores, regional platforms, spreadsheets, and vendor systems; definitions drift by line and geography; and every new initiative spawns another bespoke integration. In that environment, even the best-designed cloud migration or AI strategy will under-deliver. A robust, AI-ready data foundation is therefore not a “nice to have” adjunct to modernization—it is the precondition. Without consistent, governed data, it is impossible to stand up SageSure-style claims copilots, underwriting workbenches, or customer experience analytics at scale. At the same time, the industry is under pressure to modernize faster. EPAM’s 2025 research on digital modernization in insurance, based on a survey of 200 European insurance executives, found that legacy technology is the biggest barrier to digital tools and new ways of working, with 45% of respondents citing it as a major obstacle (Digital Modernization in the Insurance Industry). BriteCore’s 2025 P&C core systems report, which surveyed 60 North American carriers, similarly concludes that modernization is now a competitive mandate, with insurers doubling down on integration, analytics, and AI to drive growth and efficiency (2025 P&C Core Systems Report). For CTOs, CDOs, and operations leaders in SageSure’s ICPs, the takeaway is that modernization and data must be planned together. Instead of bolting a data lake or warehouse onto an unchanged core, insurers need to define how claims, policy, billing, and party data will move—via APIs and events—into a governed platform that AI and analytics can safely consume. That platform becomes the connective tissue between legacy systems and modern applications: underwriting workbenches, claims automation, broker portals, and regulatory reporting. This article lays out a pragmatic blueprint for those data foundations. First, it reframes data as a modernization product: a set of curated, ACORD-aligned data domains that front-line teams can actually use. Second, it sketches a reference architecture that bridges legacy cores and modern cloud data platforms with events and APIs. Third, it shows how to run and govern this foundation so that AI initiatives can move faster without putting compliance or trust at risk.
Designing that data platform starts with acknowledging constraints: mainframes and monolithic PAS still hold the book of record; regional regulations shape where data can live; and business users need trustworthy, near-real-time views of policies, claims, and exposures without waiting for a five-year core replacement. The data foundation has to sit between those realities and the ambitions of AI-assisted claims, underwriting workbenches, and digital CX. Technical and market guidance increasingly points to a three-layer pattern. At the bottom, operational systems emit business events—policy.bound, fnol.received, claim.triaged, payment.initiated—via an event backbone rather than one-off batch jobs. In the middle, a governed data platform (often a lakehouse) stores curated, ACORD-aligned datasets for policy, claims, billing, party, and exposure, enriched with external data. At the top, consumption layers expose that data to analytics, AI services, and downstream apps via well-documented APIs. Reports on digital modernization in insurance make it clear that legacy technology is the biggest barrier to this vision. EPAM’s 2025 “Digital Modernization in the Insurance Industry" survey of 200 European insurance executives found that nearly half (45%) cited legacy tech as the single greatest hindrance to adopting digital tools and new ways of working; the report emphasises that modernization and data strategy must go hand-in-hand (Digital Modernization in the Insurance Industry). A 2025 P&C core systems report from BriteCore, based on 60 North American carriers, similarly highlights growing investment in API integrations and analytics as insurers seek to balance operational stability with innovation (2025 P&C Core Systems Report). From a design perspective, that means doing three things well. First, define canonical data contracts that decouple consumers from legacy schemas—aligned where practical to ACORD standards—so that AI teams and product squads can build against stable structures even as you swap out sources. Second, embed metadata and lineage from the start: every dataset should carry information about origin systems, transformation steps, and data quality scores. Third, separate storage from compute so you can scale AI-heavy workloads (for example, claims document embedding or risk scoring) without choking operational systems. Case studies and strategy pieces from consultancies like BCG now also stress that agentic AI can help accelerate discovery and documentation of legacy rules for modernization, but those techniques still depend on having a coherent target data model and platform to land in (How Agentic AI Can Power Core Insurance IT Modernization).
Treating data foundations as a product—rather than a one-off project—changes how modernization feels to the business. Instead of a long pipeline of IT deliverables, you get a catalogue of governed data products (for example, “Claims 360,” “Underwriting Submissions,” “Broker Performance”) with owners, SLAs, and clear interfaces. That product mindset is what allows SageSure-style AI initiatives to reuse data assets rather than rebuilding pipelines for every new use case. For insurance executives, the first step is to define KPIs that link the data platform to business outcomes. Track time-to-data for key domains (how long it takes to onboard a new source into curated layers), data freshness SLAs for high-value use cases (such as near-real-time claims MI and underwriting dashboards), and self-service adoption (how many personas—claims leaders, underwriters, brokers—are using shared analytics rather than ad hoc spreadsheets). Layer on value metrics: reduction in manual reconciliation time, faster month-end close, and the impact of better data on claims and underwriting decisions. Externally, modernization research offers useful benchmarks. EPAM’s 2025 report finds that insurers investing in digital modernization and data capabilities are significantly more confident about meeting growth and efficiency targets than peers, but also notes that many are still at early stages of data maturity (Digital Modernization in the Insurance Industry). BriteCore’s 2025 P&C core systems survey shows that two-thirds of respondents now view modernization as a competitive mandate, with a surge in API adoption and analytics-driven decision-making as they prepare for more AI-infused operations (BriteCore 2025 Core Systems Report). Governance is the final piece. A data foundation that feeds AI must satisfy evolving expectations on privacy, fairness, and explainability. That requires clear data domain ownership; robust access controls and masking; and policies for how data products support AI training and inference. Event-level logging—where each significant lifecycle change in a claim or policy is captured with trace IDs and source details—makes it possible to reconstruct decisions and support AI explainability. For multi-region carriers, the platform also needs to encode regional data residency and retention rules so that datasets used for EU-facing AI, for instance, remain GDPR-compliant while Indian or US books align with IRDAI or NAIC-aligned guidance. When you can show that your modernization story starts with clean, governed data—not just shiny tools—you reinforce a “trust-first” AI narrative and create a foundation that every SageSure-style campaign can build on.