No one sets out to fail, but 70% of digital transformation efforts do fail. In data-heavy, highly regulated, knowledge-intensive industries like insurance, these failures extend beyond the obvious costs. They distract employees, add to their already hectic schedules, involve long learning curves, and ultimately fail to deliver the sweeping change they promised. To top it off, insurers are also limited by what they can and cannot do and the amount of risks they can or cannot take, especially around policyholders. These compelling reasons are why insurers do not turn on a dime every week with every new tech tool.
How can insurers ensure their digital transformations are successful? Success today requires a solution that is built around the customer— cohesively linking all functions, from customer service to internal operations, into one connected enterprise. Generative AI (Gen AI) brings a new dimension to this integrated framework. Unlike traditional tools, Gen AI democratizes AI, radically simplifying the interface to a point where anyone, regardless of their programming skills, can tap into the power of AI.
Predictably, it truly turned the heads of insurers. The past year was marked by rampant experimentation across the board. Some insurers chose the vertical path—integrating Gen AI across different touchpoints of a customer journey. Others took a more use-case approach by integrating non-core insurance functions, which have no direct impact on the policyholder but help improve the bottom line. It is an efficiency play for some, while for others, it is about differentiation and capturing more market share.
A decade ago, when robotic process automation (RPA) emerged, insurers dove into automating high-volume tasks, particularly in operations and finance. The return on investment (ROI) was impressive, leading to continued investment in these projects. However, automation wasn’t scaled across all functions as tasks with lower volume and frequency didn’t promise the same substantial ROI.
Today, our understanding of ROI has evolved. It now includes not just financial returns but also improvements in work quality and employee quality of life. This broader perspective on ROI leads the way for more extensive AI integration. With Gen AI now within reach and capable of processing large datasets and synthesizing information, the potential use cases have multiplied.
For instance, consider the work of an underwriter evaluating waivers for a children’s day camp. Previously, reviewing a two or three-page waiver for specific legal terms could take hours. Now, with Gen AI, this can be done in seconds. It doesn’t mean every output gets green-lighted. There is a human in the loop. While the system quickly identifies compliance with most requirements, it can be instructed to flag items needing further review. For example, if the waiver should mention both “bodily injury” and “accidental death” but only addresses “bodily injury,” the underwriter is alerted to this oversight.
The ROI here is not just in saving time but in the overall precision of risk assessment and management and allowing underwriters to start their review with a clear focus on a few critical items instead of starting from scratch.
As we move from rudimentary automation to more sophisticated AI, it’s a good moment to reflect on where our organizations fit in this evolution.
The insurance sector is niche and demanding, and it’s about to face a major shift, with an estimated 50% of current professionals expected to retire by 2036. This significant turnover and demographic shift highlights the need to augment the teams’ abilities with AI. Insurers must shift their focus from isolated tasks to systems that the teams engage with and utilize in daily operations.
While this sounds straightforward, it’s far from simple. The industry’s traditional focus on tactical, siloed efforts geared towards effort and cost savings is proving to be a significant barrier. Often, the drive for quick, visible wins results in fragmented initiatives scattered throughout the organization. These efforts help only a handful of specialists and fail to connect data across the enterprise. How can insurers avoid falling into the trap of adopting point solutions for quick wins? What can insurers do to advance in this maturity spectrum?
Take a scenario where a severe hurricane—a Category 5 or 6 storm — is forecasted to hit a particular state. Previously, the response from claims organizations might have been sluggish, kicking into gear only after the event.
But now, we have a whole ecosystem of automation and AI capabilities available. With real-time data feeds, such as those from a new weather forecasting channel, organizations can immediately align their response. The system can pull data from various treaties and policy repositories and then predict the financial impact on specific zip codes in the hurricane’s path.
Stakeholders and claims processors gain immediate access to critical data, including which zip codes and policies are most at risk and which key clients have properties in the affected areas.
By leveraging these ‘Lego blocks’ from various sources, we can now craft a fully integrated system that doesn’t just react but predicts and recommends based on unfolding events.
All said and done, people still want to work, contribute, and create value, and this wave of change is no different. What is different is the significant cultural shift required. For example, Gen AI co-pilots help summarize documents, manage Excel tables, and even provide summaries of our meetings. But with great tools comes the need for new skills, like learning to prompt effectively to get the most out of AI tools like ChatGPT. The concept of the ‘bionic worker’—whether it is an underwriter or an agent—where we’re essentially supercharging the daily tasks of our employees should become the norm.
Integrating Gen AI in the insurance industry begins with solid foundational steps, but these are merely starting points. To truly transform, we need strong data governance, refined process discovery, and a deep understanding of customer journeys—areas where insights from a technology partner could prove invaluable.
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Over the last year, experts in technology and insurance have teamed up to spot potential uses for Gen AI. Insurance companies can tackle these ideas one at a time, scoring small wins project by project, easing friction along the way. But what if we could aim higher?
Gen AI could be the magic bean that helps us stitch together these individual efforts and connect all these pieces into coherent, efficient systems and workflows ready to handle whatever comes next. Considering these exciting possibilities, it is essential to evaluate where your organization stands on the AI maturity spectrum. Are you moving in the right direction?
Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies
