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Artificial intelligence is not new to insurance. For years, finance and back-office teams have leaned on automation to improve efficiency. What is new, however, is how deeply AI is now embedded in the everyday operational heart of insurers. Rather than sitting quietly in the background as a modelling or analytics tool, AI is actively shaping the work insurers spend the most time and money on: claims processing, underwriting decisions, and the management of complex insurance programmes.
This shift marks a critical step in Ensuring effective AI in insurance operations, where technology directly supports frontline teams instead of operating as a standalone innovation project.
Over the past year alone, major insurers such as Allianz, Zurich, and Aviva have publicly demonstrated their move from pilot initiatives to production-ready AI systems. These tools are no longer experimental—they are embedded into real workflows and used by claims handlers, underwriters, and commercial insurance specialists on a daily basis.
Streamlining claims: Fewer delays, faster outcomes
Claims handling has emerged as one of the clearest use cases for AI in insurance. The process typically combines heavy documentation with time-sensitive human judgement, making it an ideal environment for intelligent assistance.
Allianz, for example, describes its Insurance Copilot as an AI-driven tool designed to remove repetitive administrative burdens from claims handlers. Instead of searching across multiple systems, the Copilot gathers and summarises key claim and policy details upfront, giving handlers a clear overview in seconds.
From there, the system analyses documents by interpreting contracts, checking claims against policy terms, and flagging inconsistencies. It can even suggest logical next steps. Once a human decision is made, the AI helps draft relevant, context-aware communications.
The impact is practical and measurable: shorter turnaround times, smoother settlements, and less friction for both staff and customers. Allianz also notes that AI can help prevent unnecessary payouts by highlighting critical details that might otherwise be overlooked—directly supporting profitability while maintaining fairness.
Turning complex documents into decision-ready insights
Underwriting quality depends heavily on the clarity and completeness of information. Aviva highlights a common challenge: GP medical reports, which can run into dozens of dense pages.
To address this, the insurer is rolling out a generative AI summarisation tool that analyses medical reports and produces concise, structured summaries. This allows underwriters to reach informed decisions more quickly without sacrificing rigour.
Crucially, Aviva is clear about the role of AI here. The technology does not replace underwriters—it reduces reading time. Human experts remain responsible for reviewing summaries and making final calls. This distinction is essential in a technical and sensitive area where accuracy, traceability, and accountability matter.
To support Ensuring effective AI in insurance operations, Aviva has emphasised extensive testing and governance. Around 1,000 cases were processed during a controlled test phase to ensure the system met strict internal standards before wider deployment.
Managing complexity in multinational insurance programmes
Commercial insurance introduces a different level of complexity, particularly for multinational programmes operating across multiple jurisdictions. Policy structures, regulatory requirements, and local nuances can vary significantly from one country to another.
Zurich points to generative AI as a way to manage this complexity more effectively. By processing large volumes of unstructured information, AI helps teams build clearer, more consistent views of multinational programmes and simplifies submissions across regions.
One notable benefit is improved contract certainty. GenAI tools assist internal experts by comparing, summarising, and validating coverage documents in their native languages, dramatically reducing the time required to interpret layered contracts and regional variations. While these capabilities are largely internal, they enhance responsiveness by enabling underwriters, risk engineers, and claims professionals to work more efficiently.
Zurich also highlights AI’s ability to identify patterns and trends across vast datasets—connections that would be difficult for humans to spot at scale. In this way, AI strengthens expert judgement rather than replacing it.
A shared approach: Augmentation over automation
Across Allianz, Aviva, and Zurich, a consistent philosophy is emerging:
AI takes on high-volume tasks such as reading, searching, and drafting.
Human experts remain accountable for critical decisions, with “human-in-the-loop” controls firmly in place.
Governance, testing, and domain-specific tuning are treated as essential, not optional.
This balanced approach underlines the industry’s focus on Ensuring effective AI in insurance operations, where trust, control, and scalability are just as important as speed.
What this means for the insurance sector
The benefits are clear: faster processing, improved consistency, reduced manual workload, and greater scalability. The challenge now lies in responsible implementation—ensuring secure data use, maintaining explainability where required, and training teams to question and validate AI outputs.
AI is no longer a headline-grabbing novelty in insurance. It is becoming an everyday operational partner—a practical, reliable presence embedded in the routine work that underpins insurance performance and profitability.
