AI in Insurance Moves from Pilot Projects to Daily Operations

AI in insurance is reshaping claims, underwriting and complex programmes as Allianz, Zurich and Aviva deploy production-ready tools for daily use

Artificial intelligence has been part of the insurance sector for years, particularly within finance teams that were early adopters of automation. What has changed recently is how deeply AI in insurance is now embedded into everyday operational work. Rather than sitting in the background as a modelling or analytics tool, AI is being used directly in claims handling, underwriting, and the management of complex insurance programmes—areas where insurers spend most of their time and resources.

Over the past year, major insurers such as Allianz, Zurich and Aviva have published updates showing a clear shift from experimentation to production-grade systems. These tools are no longer pilots running in isolation but are actively supporting frontline staff in real workflows, signalling a new phase in the adoption of AI in insurance.

Faster claims with fewer bottlenecks

Claims processing has become one of the most visible proving grounds for AI. Allianz, for instance, has rolled out an Insurance Copilot designed to support claims handlers under time pressure. The tool gathers data from multiple systems, summarises claim and policy details, and highlights discrepancies between contracts and claims.

Instead of manually searching through documents, handlers receive a concise overview that helps them move faster. The AI also assists with document interpretation and drafts context-aware emails once a decision is made. Allianz says this reduces turnaround times, improves staff efficiency and leads to smoother settlements for customers. Importantly, it can also flag factors that may prevent unnecessary payouts, directly influencing profitability and reinforcing the business case for AI in insurance.

Turning complex documents into decisions

Underwriting is another area seeing tangible benefits. Aviva has introduced a generative AI tool that summarises GP medical reports, which can often span dozens of pages. By condensing dense medical information into decision-ready summaries, underwriters can focus on judgement rather than reading.

Aviva is clear that the technology supports, rather than replaces, its underwriters. Humans review the summaries and make final decisions. The insurer says the tool went through a rigorous testing phase, processing around 1,000 cases before launch, to ensure accuracy and control. This approach reflects broader industry caution as AI in insurance moves into sensitive, high-stakes workflows.

Simplifying multinational insurance programmes

For commercial insurers, complexity increases across borders. Zurich highlights how generative AI helps manage unstructured information across multiple jurisdictions. Multinational programmes often involve layered contracts, local regulatory differences and constant verification.

Zurich says AI enables experts to compare and summarise coverage in different languages far faster than manual processes. This improves contract certainty and allows underwriters, risk engineers and claims professionals to work more efficiently, even if the technology is not directly customer-facing. In this context, AI in insurance acts as a connector, spotting patterns and trends that might otherwise go unnoticed.

Augmentation over automation

Across these examples, a common theme emerges. AI handles reading, searching and drafting, while humans remain accountable for decisions. Insurers emphasise “human-in-the-loop” models, careful testing and gradual scaling. As a result, AI in insurance is becoming less of a headline innovation and more of an everyday operational reality—quietly reshaping how insurers work, one workflow at a time.