How Insurers Are Embedding AI Directly Into Core Operations
Artificial intelligence is not new to insurance, but its role has changed materially. What once lived in actuarial models or back-office automation is now embedded directly into the operational heart of insurers. AI tools are supporting the work where insurers spend the most time and money: claims handling, underwriting, and managing complex commercial programmes.
Large insurers are now moving beyond pilots, deploying production-grade systems that assist frontline staff inside real workflows rather than operating as detached analytics tools.
Claims handling without the administrative drag
Claims processing is a natural entry point for AI because it combines high document volumes, time pressure, and human judgement. Allianz’s Insurance Copilot illustrates how AI is being used to remove friction from everyday claims work.
The system gathers and summarises relevant claim and policy information, reducing the need for handlers to search across multiple systems. It analyses documents, compares claims against contract terms, flags inconsistencies, and proposes next steps. Once a decision is made, the tool assists with drafting context-aware communications.
This approach shortens settlement times, reduces manual effort, and improves consistency. Allianz also positions AI as a way to surface critical details that could otherwise be missed, helping limit unnecessary payouts while maintaining service quality.
Turning dense documents into decision-ready insight
Underwriting quality depends on how quickly and accurately information can be assessed. Aviva has focused on one of underwriting’s most time-consuming tasks: reviewing lengthy medical reports.
Its AI-powered summarisation tools condense large volumes of clinical text into structured, readable summaries that underwriters can review quickly. The technology does not replace underwriting judgement; it reduces the time spent reading and extracting key facts.
Aviva emphasises that underwriters remain responsible for final decisions, with AI acting as an accelerator rather than an arbiter. Extensive testing and controls were applied before rollout to ensure accuracy, traceability, and audit readiness.
Simplifying multinational insurance complexity
Commercial insurance introduces additional challenges, particularly when programmes span multiple jurisdictions. Zurich highlights how generative AI can process unstructured information across languages and regulatory contexts to support multinational programmes more efficiently.
AI tools help compare coverage, verify contract terms, and identify inconsistencies across layered documentation. By working in the user’s native language and capturing regional nuances, these systems reduce manual translation effort and improve contract certainty.
Although these tools operate behind the scenes, they materially improve responsiveness by allowing underwriters, risk engineers, and claims professionals to work faster and with greater confidence.
Pattern recognition at scale
Beyond document handling, insurers are using AI to detect trends and relationships buried in large datasets. At this scale, AI acts as an amplifier of expert judgement, highlighting patterns that would be difficult for humans to spot unaided.
Rather than displacing expertise, AI extends it, helping specialists focus on higher-value analysis while routine processing is automated.
Augmentation over automation
Across insurers, a consistent philosophy is emerging. AI takes on reading, searching, and drafting at scale, while humans retain accountability for decisions that carry financial, regulatory, or ethical weight.
Human-in-the-loop models, rigorous testing, and gradual expansion across lines of business are central to how insurers are deploying these tools. Automation is being applied selectively, not indiscriminately.
What this signals for insurance operations
The operational benefits are clear: faster cycle times, improved consistency, reduced manual workload, and better scalability. The harder challenge lies in responsible implementation, including secure data handling, explainability, and training staff to critically assess AI outputs.
As AI becomes less of a headline and more of a standard tool, insurers are treating it as a practical digital colleague—one that supports profitability by improving how everyday work gets done.
