How Generative AI is transforming insurance operations

The insurance industry has long been known for its steady, methodical approach, but recent pressures are forcing a rethink. From the unpredictable impacts of climate change to the persistent threats of cybercrime and inflationary pressures on claims costs, insurers are navigating a landscape that demands faster, smarter solutions. At the same time, customer expectations for personalised, seamless service continue to rise, challenging traditional business models.

Generative AI (GenAI) is beginning to change the equation. Far from being just another technology trend, it is already helping insurers reimagine how they process claims, detect fraud, and personalise policies. With its ability to handle complex data and automate cumbersome processes, GenAI is fast becoming a practical tool to tackle the industry’s most persistent challenges. But is the sector moving quickly enough to embrace its potential?

Why insurers are betting on GenAI

For an industry built on data, insurers have always understood the value of technology. Yet, the adoption of generative AI represents a leap rather than an evolution. Unlike traditional analytics, which process structured data to reveal patterns, GenAI can draw insights from unstructured sources such as customer interactions, imagery, and historical records. This makes it particularly well-suited to addressing the complex, real-world challenges insurers face today.

Recent figures highlight why the industry is paying attention. According to Deloitte, 79% of CEOs believe GenAI will transform their business within three years, yet only a fraction—38%—have begun pilot projects. For those that have, the potential benefits are striking. GenAI is helping organisations improve operational efficiency, reduce costs, and deliver more tailored experiences to customers.

Take claims management as an example. This historically labour-intensive process can now be streamlined with AI-powered solutions that analyse accident photos, calculate damages, and generate settlement offers in real time. Such advancements not only cut costs but also improve customer satisfaction, offering a much-needed competitive edge.

Real-world applications of Generative AI

As insurers begin to explore the potential of generative AI, early adopters are uncovering practical applications that are driving meaningful change. These implementations are not just theoretical; they are reshaping processes across the insurance value chain, tackling inefficiencies, and improving both customer and employee experiences.

In customer support, for example, Canada-based insurer Definity turned to GenAI to address operational bottlenecks in its call centres. By automating tasks such as creating call summaries and extracting key insights from conversations, the company reduced the average handling time per call by over three minutes. AI-powered caller authentication further streamlined operations, cutting wait times and enabling agents to focus on more complex queries. These innovations didn’t just enhance efficiency—they raised customer satisfaction and freed up employees to concentrate on higher-value tasks.

Similar advancements are being made in claims processing. What was once a laborious, time-intensive workflow is now becoming almost instantaneous. With AI systems capable of analysing accident photos to assess damage and generate repair estimates, insurers can provide settlement offers in under a minute. This not only eliminates delays but also meets the growing demand for transparency and immediacy in customer interactions.

Fraud prevention is another standout use case. GenAI’s ability to process unstructured data and detect anomalies at scale makes it an indispensable tool for identifying suspicious activity. By augmenting traditional fraud detection systems, it enables insurers to audit claims more thoroughly without overburdening their teams, mitigating risks while maintaining efficiency.

What’s holding insurers back—and how to push forward

While the potential of generative AI in insurance is evident, its implementation comes with challenges that require strategic foresight and collaboration. Insurers must navigate issues such as data readiness, regulatory compliance, and organisational change to unlock the full value of this technology.

One of the most pressing hurdles is the state of existing infrastructure. Many insurers rely on legacy systems that are not optimised for the demands of AI-driven workflows. Transitioning to AI-ready architectures, often requiring cloud-based platforms and advanced data management capabilities, is a necessary but complex undertaking. Insurers like Definity, which partnered with Google Cloud to enhance its data platform, illustrate how strategic technology investments can lay the groundwork for successful AI adoption.

Regulatory compliance adds another layer of complexity. With customer data at the heart of AI applications, maintaining strict adherence to data privacy laws and ethical standards is paramount. This calls for robust governance frameworks that ensure AI deployments align with legal and ethical requirements while delivering measurable business value.

Organisational culture also plays a critical role. GenAI adoption demands a shift in mindset, where employees across departments—from actuaries to customer service teams—are equipped to work alongside AI tools. Training and upskilling initiatives, coupled with leadership support, are essential to fostering this cultural shift.

Leadership’s role in AI adoption

Generative AI has immense potential, but without strong leadership, it’s just another buzzword. For insurers, the role of CEOs is to turn potential into progress. This starts with asking the right questions: Where can AI deliver immediate value? How do we balance innovation with compliance? And most importantly, how can we bring our teams along on this journey?

Leadership isn’t about doing it all—it’s about enabling others to act. CEOs need to empower cross-functional teams to experiment with GenAI, identify the most impactful use cases, and scale them effectively. Success stories often begin with simple applications, like automating repetitive tasks, which build trust and momentum across the organisation.

Partnerships also play a crucial role. By collaborating with InsurTechs and AI specialists, insurers can bridge gaps in expertise and infrastructure, accelerating adoption without reinventing the wheel. Strong leadership ensures these efforts remain focused, strategic, and impactful.

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