Executive summary
AI is moving from pilots into everyday decisions for insurance leaders, from underwriting and claims to member service and portfolio analysis. As use cases bring both increased efficiency and heightened risk for organizations and their customers, insurers need to evaluate where AI brings the greatest value to their business and ensure it’s being deployed responsibly to explain AI-enabled activities, establish accountability and retain human oversight in necessary areas.
Where AI is being deployed across insurance
Artificial intelligence has moved from experimentation to execution across the insurance industry. Organizations are using AI to improve underwriting, claims processing, customer service, fraud detection, actuarial modeling, finance and operational efficiency. But as adoption increases, insurers need to demonstrate how AI-enabled decisions are made and whether controls work as intended.
Grant Thornton's 2026 AI Impact Survey found that while 52% of insurance organizations report revenue growth from AI, only 24% are highly confident they could pass an independent AI governance review. Additionally, 68% of insurers said that AI controls exist but remain fragmented across teams and technologies.
“Organizations with stronger governance are significantly more likely to achieve measurable AI-driven business outcomes,” said Matthew Cassidy, Risk Advisory Services Partner at Grant Thornton.
Leading insurers are shifting their focus from simply deploying AI to governing it. Successful organizations are building frameworks that emphasize accountability, explainability, data lineage, human oversight, auditability and clear ownership of AI-enabled decisions.
Property and casualty: Managing AI risk throughout the policy lifecycle
Property and casualty (P&C) carriers are applying AI throughout the policy lifecycle, including underwriting, fraud detection, claims triage, catastrophe modeling, loss estimation, subrogation support and customer service. In claims operations, AI can process structured and unstructured data to identify anomalies, accelerate workflows and improve decision-making consistency. Finance and operations teams can also automate reconciliations, exception management and reporting.
“Because AI is increasingly influencing claims outcomes and underwriting decisions, governance needs to include risk-tiering methodologies, AI inventories, model monitoring, approval workflows and human-in-the-loop review processes”,” Cassidy said.
Rather than relying on ad hoc governance committees, organizations are establishing formal operating models that define accountability across business, technology, risk, compliance and internal audit functions. These controls help provide evidence that AI-generated recommendations are explainable, traceable and reviewed by professionals before they affect policyholders or financial reporting.
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Life insurance: Enhancing underwriting while proving fair, explainable decisions
Life insurers are using AI to support underwriting, risk assessment, medical-data analysis and streamline policy administration. AI can help carriers evaluate large volumes of applicant information, helping reduce underwriting cycle times while improving consistency and accuracy. AI is also being used to assist with customer service, policyholder engagement, lapse prediction and financial forecasting.
Because life insurance decisions can significantly affect consumers, explainability, fairness testing, data quality controls and model decision documentation are particularly important. Governance programs should assess AI use cases for risk before deploymentand establish clear ownership for validating AI-generated outcomes.
“Increasingly, insurers are treating both internally developed models and third-party vendor AI solutions under the same governance standards,” Cassidy said. “Using a vendor doesn’t remove the insurer’s responsibility for outcomes."
| Insurance sector | Illustrative AI use case | Human-in-the-loop activities to retain |
Property and casualty | AI helps triage claims and flag anomalies. | Teams monitor model outputs and require human review before recommendations affect claims outcomes. |
| Life insurance | AI helps assess applicant and medical data in underwriting. | Teams test for fairness, document how recommendations are reached and retain human review of underwriting decisions. |
| Health insurance | AI helps summarize clinical documentation and support member interactions. | Teams control access to sensitive information and monitor whether the tools continue to operate as approved. |
| Reinsurance | AI helps analyze catastrophe exposure and complex risk portfolios. | Teams document model inputs and assumptions, and validate outputs when AI-enabled analysis informs reserving or capital decisions. |
Health insurance: Driving member experience and operational efficiency
Health insurers are applying AI across care management, prior authorization workflows, member service, provider analytics, fraud detection and administrative operations. Generative AI and agents can assist with member interactions, routine inquiries, clinical documentation summaries, clinical documentation and identify opportunities for cost containment and care optimization. Health insurers also use AI for finance and compliance functions through automated reporting, monitoring and exception management workflows.
Because health insurers manage sensitive personal and healthcare information, governance frameworks should address privacy, security, data stewardship and applicable regulatory compliance. Organizations are implementing structured intake processes for AI use cases, formal approval mechanisms, ongoing monitoring and audit-ready evidence repositories. Legal, compliance, cybersecurity, privacy and business teams need clear review and escalation responsibilities to ensure that AI systems and use cases comply with both healthcare and insurance regulatory expectations.
“The goal for health insurers is not simply to deploy AI safely, but to continuously demonstrate that controls remain effective as models evolve and scale,” Cassidy said.
Reinsurance: Scaling AI across complex risk portfolios
Health insurers are applying AI across care management, prior authorization workflows, member service, provider analytics, fraud detection and administrative operations. Generative AI and agents can assist with member interactions, routine inquiries, clinical documentation summaries, clinical documentation and identify opportunities for cost containment and care optimization. Health insurers also use AI for finance and compliance functions through automated reporting, monitoring and exception management workflows.
Because health insurers manage sensitive personal and healthcare information, governance frameworks should address privacy, security, data stewardship and applicable regulatory compliance. Organizations are implementing structured intake processes for AI use cases, formal approval mechanisms, ongoing monitoring and audit-ready evidence repositories. Legal, compliance, cybersecurity, privacy and business teams need clear review and escalation responsibilities to ensure that AI systems and use cases comply with both healthcare and insurance regulatory expectations.
“The goal for health insurers is not simply to deploy AI safely, but to continuously demonstrate that controls remain effective as models evolve and scale,” Cassidy said.
Governance: The next competitive differentiator
As AI use cases move from isolated pilots into core business processes across insurance organizations, the governance principles remain remarkably consistent across each sector.
“Successful insurers are establishing frameworks built around four core pillars: traceability, explainability, accountability and control maturity,” Cassidy said. “These organizations recognize that governance is key to making technology trustworthy for leaders and consumers.”
Governance is most effective when it is embedded directly into AI operations through standardized intake processes, risk-tiering methodologies, clear decision rights, continuous monitoring and human oversight. Organizations that can prove how AI decisions are made, who owns outcomes and how risks are managed are significantly more likely to scale and realize measurable business value from AI, while maintaining the confidence of regulators, boards, auditors and customers.
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This Grant Thornton Advisors LLC content provides information and comments on current issues and developments. It is not a comprehensive analysis of the subject matter covered. It is not, and should not be construed as, accounting, legal, tax, or professional advice provided by Grant Thornton Advisors LLC. All relevant facts and circumstances, including the pertinent authoritative literature, need to be considered to arrive at conclusions that comply with matters addressed in this content.
Grant Thornton Advisors LLC and its subsidiary entities are not licensed CPA firms.
For additional information on topics covered in this content, contact a Grant Thornton Advisors LLC professional.
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