Artificial intelligence (AI) in the insurance industry is best understood as a powerful, context-dependent tool rather than a universal solution. Companies must distinguish between general AI and specialised tools, recognising that the latter often excel in specific tasks such as translation or data structuring. When adopting AI, it is crucial to consider tolerance for errors, potential efficiency gains, and economic viability, as AI systems are not infallible and may shift costs within the organisation. In underwriting, AI can automate processes for low-complexity, homogeneous data, but serves mainly as a support tool in complex cases. Its strengths include processing unstructured data, standardising information, and aiding in claims management and fraud detection, though final decisions remain with human experts. Ultimately, the effectiveness of AI hinges on high-quality data and thoughtful deployment, with companies benefiting most when they acknowledge both its capabilities and its limitations.

AI has become a prominent topic within the insurance sector, often surrounded by considerable hype. There is a prevailing notion that AI serves as a universal solution to every challenge, whether it be a problem, a need for improvement, or a desire for greater efficiency. However, this perception does not reflect reality. AI is not omnipotent; rather, it is a powerful tool when applied appropriately and in the right context…