The future of AI in the insurance industry

Once in a great while, a technological innovation comes along that changes the world, and businesses have to adjust—or potentially decline into irrelevance. The Industrial Revolution’s steam engine and the mechanization of production allowed for a shift from largely agrarian to urban lifestyles. The birth of the internet brought us enhanced real-time communication, e-commerce, cloud computing, and more.

Now, it’s AI’s turn. This powerful technology is rapidly transforming workflows, driving innovation, and reshaping industries. As with other transformative changes brought by technology, it will be hard if not impossible for companies, including insurers, to ignore AI. About two decades ago, as e-commerce became ubiquitous and more sophisticated, consumers got used to seamless ordering and fast delivery and came to expect those capabilities from all merchants. Similarly, AI has changed consumer expectations to the point that customers now expect higher accuracy and reliability during the consumer journey, human-like conversations with AI bots (whether text- or voice-based), hyperpersonalized offers and communication, and on-demand products and interactions tailored to their needs.

Gen AI and agentic AI in particular can be game changers. One key difference from previous technological leaps is that gen AI is capable of levels of reasoning, judgment, creativity, and empathy that far exceed previous innovations’ capabilities—skill sets with particular salience to insurers. That’s why gen AI has the capacity to truly transform the insurance industry.

At its core, insurance involves gaining an accurate understanding of the underlying risk, and effectively and empathetically assisting people in distress as efficiently as possible. AI can transform all of this: Traditional analytical AI understands patterns in data; gen AI enhances those capabilities with greater understanding of unstructured data forms and enables the addition of hyperpersonalization and empathy into responses; and the latest advances in agentic AI add unprecedented levels of automation to complex workflows, allowing insurers to maximize benefits. Because of this versatility, insurers are using AI in all core areas, including sales productivity and hyperpersonalization; automation and improved accuracy of underwriting; augmented claims management; customer service operations with voice agents; and transformation of back-office functions such as finance, actuarial, and IT.

As with other groundbreaking technological innovations, consumers will come to realize that AI can make their lives easier and will then expect it from their service providers. Insurers that seize the opportunity to deeply integrate AI into everything they do will be poised to come out on top. They will be able to conduct more business, faster, in a more personalized manner, and with a better understanding of the underlying risk. Insurers that merely dabble in AI risk being left in the dust, unable to keep up with their AI-native peers.

To create lasting business value from AI, insurers need to set a bold, enterprise-wide vision for AI’s potential, and deeply, fundamentally rewire how they operate across the various business domains (underwriting, claims, distribution, customer service and more), embedding the technology into every part of the organization. They will need to completely retool workflows, rethink operating models, work toward a modern data and tech stack, and scale AI by harnessing reusable components for various use cases and business areas. And they will need to do this in a manner that creates meaningful improvements in unit economics. Processes will need to be revamped end to end to extract value from AI, rather than simply layering AI on top of existing processes, or even worse, inserting an additional step in a workflow with an unnecessary AI tool.

AI continues to innovate rapidly. For example, in the near future, nearly all customer onboarding functions in insurance could be delivered through AI multiagent systems, which could act as virtual coworkers. An intake agent would ingest information, communicate with customers or intermediaries to clarify data points, and seamlessly extract data from complex documents such as medical records or engineering reports. A risk profiling agent could build a comprehensive risk profile for each case, using existing underwriting guidelines. A pricing and product agent could automatically price the case and suggest policy structures to meet customer needs, for instance, by adding critical illness or disability riders to a life insurance policy. A compliance and fairness agent could review the entire process to ensure regulatory compliance and high ethical standards. A decision orchestrator agent could aggregate input from various other agents to determine if the policy can be automatically approved or if it needs to be escalated to a human senior underwriter for review given the size of the policy or other factors. A learning and feedback agent could continuously refine models, use human feedback to improve, and track drift, or degradation of a machine learning model’s performance over time. Of course, humans will continue to be involved across different business areas in insurance, particularly those that include touchpoints with customers.

While AI holds immense potential for insurers, scaling it enterprise-wide remains challenging. Security risks, high costs, the risk of getting locked in with suppliers, talent shortages, cultural resistance, governance gaps, and legacy infrastructure often hinder progress. A true transformation requires addressing these barriers head-on—and doing so in a thoughtful way that avoids creating “tomorrow’s legacy” with the proliferation of approaches and solutions we are seeing today.

That’s why change management is an integral part of AI transformations. According to the authors, in their experience, change management represents half the effort required to secure both financial and nonfinancial impact, while efforts to bring clean data to the models, the modeling itself, and the integration of AI account for the other half.

In line with the Rewired framework, insurers can make six signature moves to build organizations that will outperform in the age of digital and AI:  Align the C-suite around a business-led road map for AI transformation.  Build the right talent bench.  Adopt an operating model that can scale.  Use technology for speed and distributed innovation.  Embed data everywhere. And iInvest in adoption and change management.

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