This piece is by Quentin Colmant CEO and co-founder of Qover
Customer experience is a prized commodity in insurance. People don’t compare their insurance policies until the moment they need them most – and then it’s too late. When a claim is filed, insurers are no longer judged on marketing promises or policy wording. They’re judged on speed, clarity, and care.
Yet for too many policyholders, this moment is marked by confusion, silence, or delay. In a hypercompetitive market, where switching providers is just a few clicks away, every poor claim experience becomes a potential lost customer.
And with $170 billion in premiums at stake, the industry can’t afford to get this wrong.
We are talking about a sector where the product is 100 per cent defined in the terms and conditions. This makes insurance a hotbed for generative AI innovation, but its practical application is largely in its infancy. Building the insurance sector’s AI maturity has the opportunity to address the woes of claims past.
The cracks in claims
Claims are where insurance proves its worth. Yet many insurers still rely on legacy systems, driven by outdated technology and patchwork workflows. According to a Capgemini report, 80 per cent of insurers still struggle with high-severity claims, leading to long wait times and rising operational costs. What should be a moment of reassurance becomes an exercise in endurance. This inefficiency isn’t just a cost issue – it’s a loyalty issue.
Accenture research shows that nearly one-third of claimants are dissatisfied with their recent experience. With 74 per cent of insurance companies still depending on legacy systems, it’s clear the problem isn’t a lack of insight – it’s a lack of action.
In a landscape where every negative touchpoint risks a customer jumping ship, the failure to modernise claims isn’t just outdated. It’s preventing companies from reaching their full potential.
Practical AI moves from theory to immediate action
While some in the industry chase abstract promises of AI-driven transformation, others are already putting practical tools to work. Practical AI is the combination of skills and workflows used to make AI accessible and beneficial to non-technical knowledge workers.
It has the potential to transform core insurance operations, from flagging fraudulent claims and managing damage assessment or loss estimation to supporting online self-service claims processing.
Take Revolut, for example: it streamlined its everyday purchase protection cover with a digital-first claims process that leverages AI. By analysing claims data to offer recommendations to claim handlers and enabling policyholders to submit claims faster through pre-filled fields, this isn’t AI as window dressing – it’s a tool designed to enhance frontline customer support.
With the technology implemented, Revolut saw a threefold improvement in handling speed, significantly reducing the number of days required to resolve and close 66% of its claims. Not only has the technology helped Revolut reduce friction, improve accuracy, and speed customer service, AI is also helping human teams to focus where they’re needed most.
The Agentic Leap Forward
The next step isn’t about adding more tools – it’s about enhancing intelligence. Agentic AI platforms, which are emerging quickly, promise to elevate AI from mere task assistants to fully autonomous co-workers capable of independent decision-making. These advanced systems don’t merely offer improved chatbot responses; they actively anticipate needs, execute tasks proactively, and minimise the requirement for human oversight.
Gartner forecasts that by 2028, a third of enterprise software will integrate agentic AI, automating as much as 15 per cent of workplace decisions. In the insurance industry, this translates into virtual agents capable of independently managing claims, suggesting timely policy adjustments, and identifying trends before they escalate into significant issues.
However, the opportunity to lead in this space is rapidly narrowing. Although 75 per cent of UK financial institutions are currently exploring AI solutions, fully autonomous use cases remain rare, accounting for just two per cent of implementations.
Those who act decisively now – combining robust governance frameworks, insightful customer understanding, and practical deployment strategies – will set the benchmark for the next generation of insurance services.
If the insurance industry wants to stay relevant, it must confront the growing disconnect between customer expectations and operational delivery. The tools exist, the use cases are proven, and the urgency is no longer theoretical.
Customers will continue to walk if claims remain slow, confusing, or impersonal – and the financial stakes are only rising. What’s needed now is the will to act decisively. If insurers want to protect the aforementioned $170 billion dissatisfaction risk to insurance premiums, they must act now – with empathy, urgency, and practical AI.
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