Insurance companies moving AI from pilot programs to enterprise-wide deployment must address governance, risk frameworks, and legal compliance, according to Chris Raimondo, EY's global and U.S. insurance consulting leader. The shift from experimentation to adoption demands clear strategies for building or buying technology, managing model risk, and ensuring regulatory transparency.
AI's expanding role in insurance
Insurers are using AI to speed up underwriting, claims, customer service, and fraud detection. Carriers now treat AI as an enterprise capability with C-suite sponsorship and dedicated funding. The technology is also moving from a productivity tool to a growth driver. Agentic AI will soon handle pre-bind tasks, triage low-severity claims, and support decisioning, while enterprise and physical AI aim to prevent losses rather than just predict them. For more on how insurers are using AI, visit AI for Insurance.
Build-versus-buy: a strategic decision
Insurers are adopting a hybrid model, relying on vendor systems for core functions while building proprietary capabilities in underwriting, pricing, and decisioning, Raimondo said. The goal is to reserve internal investment for areas that improve loss ratios, growth, and customer experience. Carriers with modern, cloud-based, API-first core systems are moving faster. Vendor partnerships will be central as deployments scale, since most insurers will not build the full AI stack themselves.
Governance as a strategic enabler
"Governance is becoming a strategic enabler of AI scale, not just a control function," Raimondo said. Insurers need a framework that covers model risk, third-party risk, data protection, privacy, and regulatory compliance. Traceability is critical: "Carriers need to trace model outputs and the data behind them back to a source system of record so they can defend decisions, meet regulatory expectations and resolve disputes with confidence." Strong vendor-based core systems provide a foundation for scaling AI responsibly without recreating basic controls.
Legal's early role in AI implementation
Legal teams are core stakeholders from the start of AI programs, focusing on regulatory compliance, transparency, and accountability. Insurers must ensure AI systems are explainable and auditable, and clarify data rights, model ownership, intellectual property, and liability-especially with third-party tools. Involving legal and compliance early puts carriers in a better position to scale AI confidently. AI for Legal resources can help legal professionals stay ahead of these requirements.
Why this matters for insurance professionals
For insurance professionals, AI success depends on governance and legal foundations as much as on technology. Knowing how to evaluate build-versus-buy choices, demand traceability, and engage legal early will differentiate leaders who scale AI responsibly from those who face regulatory setbacks or erode customer trust.
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