Writing emails, summarizing meeting notes, creating documents — insurers are already beginning to realize the efficiency potential of generative AI’s “first wave.” But preparing the insurance value chain to use the next two waves, AI-powered automation and decision-making, will require some homework first.
Generative AI is advancing how insurance companies think about an already evolving, digital-focused value chain. While insurance carriers have used AI to automate processes and deliver more customer services digitally, the insurance value chain remains a gold mine of optimization opportunities.
Like most industries, insurers recognize that generative AI and other large language model (LLM) generative AI technologies represent great potential for increasing efficiency. From generating emails to send customers to summarizing meeting notes, preparing presentations, and building bots for routine customer questions, AI can take a lot of time and drudgery out of workers’ lives.
Improve the Insurance Value Chain with AI
However, as at least one technologist has noted, efficiency represents only the first wave of generative AI. Think of it as the “low-hanging fruit.” In the second wave, AI will join forces with other digital tools to systematically drive more costs out of the value chain while increasing accuracy, efficiency and customer satisfaction.
Consider the claims process. Many claimants can now submit claims more easily using an app or website, but evaluating the claims and processing them still requires human intervention. Claims also come with other documents, such as police reports or medical records, that insurers must also process. The data on those documents can appear in various places on forms or even be handwritten, which adds additional challenges to processing.
In the second wave of generative AI, proven solutions such as robotic process automation (RPA) and machine learning will combine with AI to interpret and evaluate such “unstructured data.” The combination — known as hyperautomation — will move AI beyond simple efficiency into the realm of understanding. For example, with one client, we’ve demonstrated that AI can learn to recognize and process similar types of data on differently structured forms. For another, we created AI that can turn mathematical expressions into human speech in multiple languages.
Generative AI for Insurance Company Decision Making
It will take a while — and large investments — to mature these second-wave AI technologies so they can scale to meet the needs of a large, diverse industry. However, when insurers realize the potential, they will likely want to start adopting them.
Carriers and brokers that advance through the first two waves will enter the third and most impactful wave of AI: generative AI-powered insights and analysis.
In this wave, insurers will use generative AI to look across the insurance value chain and enable valuable insights that capitalize on vast amounts of interconnected data across the enterprise. The third wave of generative AI involves an interactive approach to being data-driven, providing leaders with relevant information when it’s time to make key decisions.
While tangible examples of this wave are still emerging, the potential is clear. Consider an underwriting leader with relevant trends at their fingertips as they contemplate changes in appetite. They can evaluate market trends, internal loss, and submission data instantly in a consumable way by interacting with a model tailored for that exact purpose. Similarly, a claims manager responding to a catastrophe (CAT) who needs to quickly understand the size and scope of the company’s exposure could use a curated generative AI assistant to understand the full scope of the loss.
Prepare for AI-Driven Decision Making
So, how can carriers and brokers gracefully navigate the waves of generative AI?
Our advice is to step back and start with the basics — your specific business problems. At the very highest level, insurers are very similar. They sell products to customers, collect premiums, invest premiums, and pay out claims. However, each insurance company’s problems will be unique. Maybe you serve a large population of people who speak languages other than English or Spanish. Maybe you are in a part of the US subject to recurring catastrophic events. Maybe your workforce resists change. The possibilities are limitless.
Once you’ve identified your business problem, you need to ensure your leadership adopts a data-driven mindset. Being data-driven means:
- Leading with a data strategy.
- Basing decisions on the value of data, not the cost of technology.
- Expanding decision-making beyond the IT department.
- Knowing what data has the most potential before creating storage or processing solutions that seek to address all your data.
- Identifying the most important reports and metrics you hope to gain from your data.
- Being open to building on small victories rather than overnight, transformative change in your data strategy.
Conclusion
Adopting a data-driven mindset is critical. It paves the way for the next steps you must take to prepare your data for generative AI — identifying and removing (or storing elsewhere) redundant, obsolete and trivial data. Then, you must create a data governance strategy for using your data safely with AI.
These steps will help prepare you for the next waves of AI and whatever future waves may bring.
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