Artificial intelligence has been part of the insurance sector for many years, and in many companies, the finance department is often the first to be automated. But what’s remarkable about the AI example is how directly the technology is integrated into day-to-day operational tasks. Rather than being used in the background as a niche modeling function, AI is being used where insurance companies spend most of their time and money, such as processing claims, underwriting, and running complex programs.
Over the past 12 months, industry giants Allianz, Zurich and Aviva have released evidence of a shift from experimental to production-grade tools that support frontline workers in real-world workflows.
Simple argument: Fewer administrative bottlenecks
Insurance claims operations are a natural testing ground for AI, as they consist of a combination of paperwork and human judgment, and are typically conducted in time-sensitive environments. Allianz describes its Insurance Copilot as an AI-powered tool that helps claims processors automate repetitive tasks and bring together relevant information that would otherwise require multiple searches in separate systems.
Allianz outlined notable changes to workflow. CoPilot starts with data collection, summarizing billing and contract details so agents can quickly get just the information they need. The algorithms then perform operations such as document analysis, contract interpretation, and comparing claims and policy details. The tool flags discrepancies and suggests next steps. Once a human operator makes a decision, CoPilot helps draft a context-aware email.
This is a day-to-day business that insurance companies care about, and using AI tools can reduce turnaround times, make payments smoother, and reduce friction with staff and customers. Allianz also frames AI as a way to reduce unnecessary payments by highlighting important factors that might otherwise be missed by adjusters. This has a clear impact on the company’s overall bottom line.
From complex documents to useful decisions
The quality of underwriting depends on the quality of information available. Aviva gives the example of an insurance company that needs to read a GP’s medical report. The company says it will launch an AI-powered summarization tool that uses genAI to analyze and summarize these reports. Reports can sometimes amount to dozens of pages of medical text. AI capabilities enable insurers to make faster and more informed decisions.
The immediate value here is not that AI will replace insurance companies, but that the technology will reduce the time spent on readings. Insurers have made it clear that underwriters, not AI, will review the brief and make the final decision. This distinction is important because underwriting is a technical and Sensitive; Compressing documents into decision-ready summaries can speed up processing, but also raises questions about accuracy, omissions, and auditability. Aviva addresses this issue by pointing to “rigorous testing and controls.” According to the company, approximately 1,000 cases were processed in an active testing phase prior to rollout to ensure the required standards were met.
Uncertain contracts and services in multinational programs
Commercial insurance is a field with unique challenges, including the complexity of working across multiple jurisdictions and regional differences in insurance policies and stakeholders. Zurich says generative AI’s ability to process unstructured information will allow multinational insurance to operate more easily in multiple countries, build a faster and more accurate picture of commercial insurance products, and simplify filing in different countries.
Zurich also emphasizes contract certainty as a practical outcome. Multinational programs involve multiple layers of documentation, varying local requirements, and the need for ongoing checks is widespread. GenAI says it helps in-house experts compare, summarize and verify program coverage using operators’ native languages, and does so in “a fraction of the time” compared to the manual effort required to translate and capture the nuances of international differences. Although this field is not customer-facing, genAI improves a company’s responsiveness by enabling underwriters, risk engineers, and claims professionals to work more efficiently.
Zurich also notes that AI can “connect the dots” and identify trends in data that human staff might not notice given the amount of information. In fact, AI will augment expert judgment rather than replace it.
Common denominator: It’s not automation for automation’s sake, it’s augmentation.
Across these three examples, a consistent pattern emerges.
- AI handles the heavy lifting: reading, searching, and drafting. A large number of tasks in insurance operations.
- Humans remain responsible for the resulting decisions, whether they are paying claims or accepting underwriting. (Allianz describes a “human participatory” approach, and Aviva and Zurich similarly emphasize that experts retain control of decision-making).
- Operations management and scalability are treated as key concerns, and piloting, testing, domain-specific alignment, and expansion into business areas are an integral part of this story.
What this means for the industry
Insurers are finding a path to faster cycle times, greater consistency, less manual work, and expansion. Their challenge is to implement the tools responsibly, defined by secure data processing, explainability where appropriate, and training teams to appropriately question output.
AI is becoming less of a headline in this space and more of an everyday reality, becoming a practical silicon colleague in the day-to-day operations of insurance profitability.
(Image source: “house fire” by peteSwede is licensed under CC BY 2.0.)
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