Insurance has always been built on information. Insurers assess risk, interpret policy wording, manage claims and support customers through situations that are often stressful or unexpected. Today, the volume of information involved in each of those stages is growing rapidly, and many firms are looking at AI for insurance to help teams work more efficiently while maintaining the care, control and judgement that the sector requires.

AI is not replacing the need for experienced underwriters, claims professionals or customer service teams. Instead, it is beginning to take on the repetitive, time-consuming work that can slow those people down. Used well, it can help insurers make better use of their data, respond faster to customers and give employees more time to focus on cases where expertise really matters.

Making sense of complex information

Insurance businesses hold enormous amounts of data. This can include policy documents, renewal information, claims notes, call transcripts, broker correspondence, risk reports and regulatory records. Much of it is unstructured, meaning it sits in emails, PDFs and free-text notes rather than neatly organised fields.

AI can help extract and summarise relevant information from these sources. For example, a claims handler may be able to review a concise summary of previous conversations, policy details and submitted evidence instead of manually searching through several systems. An underwriter may be able to identify missing details in an application or spot information that needs further review before a quote is issued.

This does not mean every decision should be automated. Insurance decisions can have significant financial and personal consequences, so human oversight remains essential. The value of AI is often in surfacing information quickly, highlighting patterns and helping professionals make well-informed decisions with a clearer view of the case.

Improving the customer experience

Customers do not usually think about their insurance until they need help. Whether they are making a claim, querying a renewal or trying to understand their cover, they expect clear answers and timely communication.

AI can support customer-facing teams by making it easier to find accurate information during a conversation. It can also assist with routine enquiries, such as providing updates on the progress of a claim, explaining what documents are needed or directing customers to the correct team.

The important distinction is between using technology to remove friction and using it to avoid human contact. Customers dealing with a complex claim or a difficult personal situation may need reassurance, empathy and nuanced advice. AI should make it easier for staff to provide that support, not create another barrier between the customer and the insurer.

A well-designed approach can also improve consistency. If customer service teams have access to approved guidance and current policy information, they are better placed to give clear answers across phone, email and digital channels.

Supporting quicker, more accurate claims processes

Claims are one of the most practical areas for AI adoption. The process can involve gathering documents, checking policy details, reviewing evidence, communicating with suppliers and identifying potential fraud. Many of these tasks are manual and can create delays, particularly during periods of high claim volumes.

AI can help categorise incoming claims, extract information from uploaded documents and identify cases that may need urgent attention. It can also flag inconsistencies that should be reviewed by a specialist, helping teams focus their time where it is most needed.

Fraud detection is another area where AI can add value. By reviewing patterns across large datasets, systems may identify unusual behaviour or combinations of factors that warrant investigation. However, a flagged case is not proof of wrongdoing. Clear processes, human review and appropriate governance are needed to make sure customers are treated fairly.

For insurers, the goal is not simply to settle claims faster. It is to create a process that is efficient, transparent and proportionate, while giving complex cases the attention they deserve.

Giving employees more time for high-value work

Many insurance professionals spend a large part of their day on administrative tasks. They may need to write meeting notes, prepare summaries, locate information, draft standard correspondence or transfer details between systems. These tasks are necessary, but they can take time away from more valuable work.

AI tools can assist with drafting, summarising and organising information, allowing employees to review and refine outputs rather than start every task from scratch. This can be particularly useful for teams handling high volumes of similar requests.

The best results tend to come when technology is introduced with employees, rather than simply handed to them. Teams need to understand what the tools can and cannot do, when they should challenge an output and how to handle sensitive information responsibly. Training and change management are therefore just as important as the technology itself.

Governance cannot be an afterthought

Insurance is a highly regulated sector, and AI adoption must reflect that reality. Firms need to consider data protection, security, transparency, accountability and the risk of bias in automated processes.

It is important to know where data is being processed, who can access it and whether confidential information is being used to train external models. Organisations should also define clear rules around which use cases are approved, which information can be entered into AI tools and where human approval is required.

Regular testing and monitoring are essential too. AI outputs can be inaccurate, incomplete or overly confident, particularly when dealing with complex policy wording or unusual circumstances. Robust governance helps ensure that AI supports sound decision-making rather than introducing avoidable risk.

A practical route forward

The most successful AI projects are usually focused on a genuine business problem. Rather than trying to transform every process at once, insurers can begin with a clear use case, such as improving document handling, helping service teams find information or reducing the time needed to prepare claim summaries.

From there, they can measure results, gather feedback and refine their approach. This gives businesses the opportunity to build confidence, identify risks early and ensure that adoption remains aligned with customer needs and regulatory responsibilities.

AI will not remove the human side of insurance. If anything, it has the potential to strengthen it by reducing unnecessary admin and helping skilled people spend more time making informed, thoughtful decisions. With the right strategy, governance and technology foundations, insurers can turn AI from a talking point into meaningful operational improvement with support from BCN.

 

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