Cost containment or reduction is a constant challenge for insurers, particularly in light of recent inflationary pressures. Innovation is key to unlocking unmet markets and better meeting customer demands economically – not only in how carriers use emerging technology, such as AI, but in who (or what) does the work in a hot talent market where certain skills are in high demand.
Already we see many insurers moving toward more digital business models to position themselves for future success. And whether through deploying advanced technologies, creating an enhanced customer experience, or delivering transparent communications around key topics such as the use of AI in their businesses, key work and talent strategies are essential accompaniments and drivers of success.
A central feature of those strategies is finding an operationally and cost-effective balance between technology and people. For example, a key emerging customer experience trend has been keeping humans in the center of the service. It's no surprise that interactions with a fellow human being helps customers feel more comfortable and engaged throughout the insurance claims lifecycle. The irony is that facilitating the most convenient customer experience often depends on technology.
This is the case with communication facilitated by AI-driven chatbots. Technology acts as the gatekeeper to a more targeted human connection. The advent of highly sophisticated AI language models called large language models (LLMs), such as ChatGPT, could be a game changer in how businesses interact with their customers. Given the capabilities of these new technologies, chatbots can become more conversational and capable of handling a wider range of customer queries. By reducing workloads for human customer service advisors, advanced LLMs can automate not only routine and repetitive tasks, but also variable and creative tasks that have been considered, until recently, ‘human only’.
What does AI mean for talent and training needs?
WTW’s analysis of top jobs and skills in demand in insurance shows a sector committed to the use of technology. We see the jobs of data scientist and data engineer among those most in demand, reflecting how AI continues to be a prioritized and disruptive technology. These jobs apply automation and computing skills (such as MLOps, DataOps, Python, R, and Data Analysis) to create more efficient processes and provide customers with more personalized solutions.
As insurance organizations integrate technology and digital solutions alongside their employees, co-work and collaboration between insurance talent and AI systems will increase, providing a unique opportunity to improve the relationship between human and AI systems.
This might involve improving training on the use of technology, facilitating more efficient troubleshooting where issues arise or establishing reverse mentoring schemes where digitally savvy colleagues provide guidance to those less familiar with digital solutions.
Another key step is creating clear communications around the purpose and work-role of AI, so that employees, management, and boards of directors understand the role of AI in the organization, the work it is doing, and how it is being managed (like the performance of an employee). As insurance talent’s and other stakeholders’ understanding and trust of these automation co-workers builds, there should be opportunities to improve employee experience (through increased productivity and reduced attrition) and customer experience.
With such improvements, though, also comes the need for awareness of potentially altered risk factors for the business. As insurance organizations, like those in other sectors, rely more heavily on technology to transform, different risk factors arise – cyber, operational, reputational, financial and many more. Our research shows that across all sectors over a third of organizations (37%) have been effective in managing people, business and operational risks over the past three years. Strengthening organizational capabilities to better manage these changes will be a key part in de-risking transformational progress.
The following capabilities can de-risk and create more resilience and agility during these transformations:
Insurance organizations may also find that by looking inward and applying the same diligent risk management approaches used to serve their external customers, they can navigate change and transformation not only based on the capabilities of their more technical talents, but also across the business.
As of today, many companies are already using process automation technologies to automate processes across their organizations. More advanced AI systems, such as LLMs, will further transform the way insurance companies operate – from pricing and underwriting to claims processing and customer service. The impending change has massive implications for organizations and their employees and for how work and rewards will be redesigned to unlock human performance with technology.
We used WTW’s work reinvention technology, WorkVue, to explore how work could be redesigned in underwriting with LLM. Our analysis showed that many work tasks in more homogeneous classes of business are highly likely to be impacted directly by this new technology. For example, an AI system can almost fully automate the examination of documents to determine the degree of risk and produce a summary report. It can also generate a list of additional questions for additional information and allocate these questions to respective recipients such as field representatives or medical personnel. In our test case, automating most tasks WorkVue indicated 71% work substitution, increasing employee capacity by 35%. This means that in the era of generative AI, the job of some underwriters could be completely reinvented, allowing for the fact that the more complex and unique the risk, the more the knowledge and experience of a human underwriter will come into play.
Such transformative changes also have a direct impact on employee performance. A recent working paper by two MIT researchers (Noy and Zhang, 2023) found that the use of ChatGPT for writing tasks made participants perform better, and much faster. Essentially, exposure to generative AI seems to increase efficiency and job satisfaction. More surprisingly, however, generative AI appears to act as an equalizer, benefiting worst performers more. If such evidence is confirmed by further research, this new technology will have a profound impact on how employee performance is evaluated and assessed.
What is clear, right now, is that – love it or hate it, AI technologies are going to have a growing role in the future success and cost management of insurance companies from operations to customer experience. And with that, job design, talent acquisition and retention, and training will have to adapt.
Noy, S., Zhang, W., Experimental Evidence on the Productivity Effects of Generative Artificial Intelligence, (2023), (working paper, not peer reviewed), [Online]
