Dive into the AI trends setting the pace for insurance
Insurers are actively deploying AI across underwriting, claims, and operations.
This second annual State of AI Adoption in Insurance takes a clear-eyed look at how insurers are putting AI to work today. It highlights where momentum is building, where progress is slowing, and what continues to separate experimentation from measurable operational impact.
Based on survey responses from insurance professionals across underwriting, claims, servicing, IT, and operations, the report reflects how AI is being evaluated, tested, and deployed in real insurance environments, providing a grounded, real-world view of AI adoption across the industry.
More than a snapshot of trends, this report serves as a practical benchmark. Whether your organization is exploring use cases, validating solutions, or running AI in production, the findings help assess readiness, identify gaps, and pressure-test priorities against peers. It’s built to support smarter decisions as AI shifts from isolated initiatives to an expected, scalable capability within insurance operations.
Benchmark your AI adoption against peers across underwriting, claims, and servicing
Understand which AI use cases are delivering real operational value today
Clarify what it takes to move AI from exploration into production
Learn how leading insurers are overcoming data, security, and ROI challenges
See how insurers are designing AI with human oversight and governance built in
Apply proven best practices to move AI initiatives from pilots into scalable execution
AI engagement has become mainstream, with 93% of insurers actively using, exploring, or implementing AI, signaling that adoption is no longer experimental but a strategic industry standard.
of insurers actively using, exploring, or implementing AI
Confidence remains high: 86% of respondents report being confident or very confident that AI can help achieve business goals, reinforcing strong belief in its value across core insurance functions.
Automation leads adoption (53%), with unstructured document processing (46%) and copilots (45%) close behind, showing that operational relief in high-volume workflows remains AI’s clearest near-term payoff.
Insurers are moving into execution: 58% are now either testing AI (31%) or running solutions in production (27%), marking a clear shift from evaluation to operational deployment and highlighting an emerging gap between leaders and laggards.
Accuracy of AI outputs (51%), human oversight (41%), and alignment to measurable business goals (40%) rank as the most critical success factors, showing that scalable AI adoption will depend on reliability, accountability, and clear KPI-driven value within regulated workflows.
Insurers are moving beyond skepticism into practical implementation, with data quality (51%), security and privacy (48%), and unclear ROI (40%) emerging as the top barriers, reinforcing that continued AI progress will be driven by stronger data foundations, governance, and measurable business value.
Nearly three-quarters of insurers (73%) have an AI governance committee established or in progress, signaling that formal oversight is becoming essential for responsible adoption and highlighting a divide with the 25% who have no defined plans.
Insurers prioritize decision-support AI in core operations to drive efficiency, speed, and cost control
What are the top 3 outcomes related to underwriting you hope to achieve with AI adoption in the coming 12 months?
Insurers are under pressure to scale without adding headcount, making automation and AI a priority for reducing submission friction and improving throughput.
In competitive segments like small commercial and workers’ compensation, speed-to-quote affects placement outcomes directly, making operational efficiency a growth lever, not just a cost play.
What are the top 3 AI use cases your organization is actively exploring or implementing within underwriting?
What are the top 4 outcomes related to claims you hope to achieve with AI adoption in the coming 12 months?
Claims organizations face sustained pressure to reduce cycle times and control loss adjustment expenses while managing high volumes of unstructured data.
Manual handoffs and siloed systems compound intake delays and drive rework.
AI is expected to accelerate document handling, triage, and information access, while adjusters retain accountability for coverage decisions and customer communication, where speed and clarity directly shape retention.
What are the top 3 AI use cases your organization is actively exploring or implementing within claims?
What are the top 3 outcomes related to claims you hope to achieve with AI adoption in the coming 12 months?
Policy servicing operations face pressure to absorb rising transaction volumes across endorsements, renewals, invoicing, and document management without scaling staff or spend alongside them.
Manual processing and fragmented workflows create operational burden and rework across the policy lifecycle.
AI is being positioned to reduce variability, stabilize execution, and scale routine servicing work while maintaining accuracy and control.
What are the top 3 AI use cases your organization is actively exploring or implementing within policy servicing and operations?
Establish clear ownership and executive accountability
Align AI investments to business outcomes and measurable value
Build cross-functional governance and operating alignment early
Design human oversight into core workflows
Prepare the workforce through role clarity and change management
Use proofs of value as structured decision points
Strengthen data readiness through use-case driven scope
Recognize that not all AI is the same
Operationalize continuous monitoring and feedback loops in production
