Insurance AI is leaving the pilot parking lot; EYs consulting lead says the ticket out is governance, legal involvement early, and traceability back to a system of record.
Via Complete AI Training: Insurers prioritize governance and legal frameworks for AI adoption
Insurance companies moving AI from pilot programs to enterprise-wide deployment must address governance, risk frameworks, and legal compliance, according to Chris Raimondo, EYs global and U.S. insurance consulting leader, as summarized by Complete AI Training (July 16, 2026). The shift from experimentation to adoption, the piece argues, demands clear build-versus-buy strategies, model-risk management, and regulatory transparency.
Carriers are already using AI to speed underwriting, claims, customer service, and fraud detection. The article frames AI as an enterprise capability with C-suite sponsorship and dedicated funding-moving from a productivity tool toward a growth driver. Agentic AI is expected to handle pre-bind tasks, triage low-severity claims, and support decisioning, while enterprise and physical AI aims to prevent losses rather than only predict them.
Raimondo describes a hybrid model: vendor systems for core functions, proprietary builds for underwriting, pricing, and decisioning-reserving internal investment for areas that improve loss ratios, growth, and customer experience. Carriers with modern, cloud-based, API-first cores move faster; vendor partnerships remain central because most insurers will not build the full AI stack themselves.
Governance is becoming a strategic enabler of AI scale, not just a control function, Raimondo said, as quoted. Insurers need a framework covering 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 cores, he argues, provide a foundation for scaling without recreating basic controls.
That list-model risk, third-party risk, data protection, privacy, regulatory compliance, and traceability-is not academic. It is the minimum viable control set when AI moves from summarizing emails to influencing binds, claims triage, and pricing decisions. Without a source system of record behind an output, the model said so becomes an indefensible sentence in a market-conduct exam or a customer dispute.
Complete AI Training emphasizes legal teams as core stakeholders from the start: regulatory compliance, transparency, accountability, explainability, auditability, plus clarity on 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 confidently rather than retrofit controls after a regulator asks uncomfortable questions.
Complete AI Trainings professional takeaway is blunt: AI success in insurance 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 is what separates carriers that scale from those that collect regulatory setbacks and eroded trust.
For independent agencies reading the same memo, translate enterprise framework into lighter artifacts: an approved-tool list, a paste ban for client PII into public chatbots, human sign-off on AI-drafted client communications, and a vendor questionnaire that asks where training data goes. The spirit matches Raimondo even when the org chart does not.
Governance is becoming a strategic enabler of AI scale, not just a control function, says EYs Chris Raimondo-traceability included.
You do not need EYs global practice to implement the spine Raimondo describes. AgentsROI.ai brings the same control logic to owner-led agencies and mid-market carriers that cannot staff a standing model-risk committee. A Fractional AI Officer owns the governance cadence: model inventory, third-party reviews, and decision rights. A Shadow-AI Risk Assessment finds tools already making underwriting- or claims-adjacent decisions without a system-of-record trail. Managed AI Operations keeps traceability intact when vendors change models underneath approved workflows.
If your carrier or agency is still celebrating pilot counts, flip the scoreboard: number of models with an owner, number of outputs traceable to a system of record, number of third parties reviewed, and number of decisions that still require a licensed human. Those metrics scale; demo theater does not.
A lightweight carrier or MGA starter kit mirrors Raimondo without the Big Four invoice: model inventory with owners; third-party AI addenda covering training and liability; data-classification rules for prompts; and an audit path from output to system of record. Legal sits in kickoff meetings, not only in breach retrospectives.
The Complete AI Training / EY message is not anti-AI. It is anti-amnesia: if you cannot trace an output to a source system of record, you do not have an enterprise capability-you have a story that collapses in a dispute file.
If your AI program is still pilots plus enthusiasm, start with a Fractional AI Officer engagement. Book a no-pressure assessment.
Carriers and agencies that skip this work will still buy AI. They will just buy it twice: once as a pilot, and again as remediation after a regulator, reinsurer, or client asks for the audit trail that never existed. Governance is not the opposite of speed. In insurance, it is what makes speed survivable.
This article summarizes publicly reported information and is for general informational purposes only. It does not constitute legal, tax, financial, investment, security, or compliance advice. AgentsROI.ai is not a law firm, accounting firm, or registered investment adviser. Facts, pricing, statistics, and product capabilities cited here reflect the sources listed at the time of writing and may change. Readers should verify current information independently and consult qualified professionals regarding obligations specific to their industry, jurisdiction, and circumstances-including applicable New York State and New York City requirements. AgentsROI.ai may have commercial relationships with vendors mentioned; where material, such relationships are disclosed. Nothing in this article is an endorsement of any specific AI product, model, or provider.
