The insurance industry is moving from generative AI experimentation towards real-world deployment, with insurers increasingly looking for measurable business value rather than simply testing what the technology can do. A recent Celent survey found that 48% of insurers surveyed are already using generative AI in production, and this figure is expected to exceed 50% by the end of 2026. According to industry experts cited by FinTech Global, the strongest value is emerging where AI supports experienced professionals in making decisions faster and handling large volumes of unstructured information. For example, AI can help underwriters review lengthy submissions and claims handlers collate relevant information without replacing their professional judgement.
The article also highlights that data quality is critical to successful AI deployment. Generative AI is particularly useful for high-volume, language-intensive activities such as triaging submissions, summarising reports and drafting communications, but incomplete or outdated data limits its effectiveness. In cyber insurance, for example, insurers need current information about an insured’s technology footprint and AI usage to assess risk accurately. The article notes that AI adoption by insured businesses can itself create an accumulation risk, as organisations may increasingly rely on common third-party AI models without adequately disclosing those dependencies. AI and cyber-risk analytics can help insurers identify such exposures during underwriting and renewal.
The central message is that AI should augment insurance expertise rather than replace accountability. Underwriting, claims and other insurance decisions can have significant financial consequences for individuals and businesses, making human oversight important. The article also cautions against adopting generative AI simply because it is popular when a conventional rules engine or workflow automation could solve the problem more efficiently and cheaply. For insurers, brokers and MGAs, the opportunity lies in combining specialist expertise, reliable data and AI-enabled analysis to strengthen underwriting discipline, improve risk visibility and identify emerging exposures before they become claims.
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