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.<\/p>\n
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.<\/p>\n
However, as at least one technologist has noted<\/a>, efficiency represents only the first wave of generative AI. Think of it as the \u201clow-hanging fruit.\u201d 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.<\/p>\n 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.<\/strong><\/p>\n In the second wave of generative AI<\/a>, proven solutions such as robotic process automation<\/a> (RPA) and machine learning will combine with AI to interpret and evaluate such “unstructured data.\u201d The combination \u2014 known as hyperautomation<\/a> — will move AI beyond simple efficiency into the realm of understanding. For example, with one client, we\u2019ve 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.<\/p>\n It will take a while \u2014 and large investments \u2014 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.<\/p>\n 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.<\/p>\n 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\u2019s time to make key decisions.<\/strong><\/p>\n 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\u2019s exposure could use a curated generative AI assistant to understand the full scope of the loss.<\/p>\n So, how can carriers and brokers gracefully navigate the waves of generative AI?<\/p>\n Our advice is to step back and start with the basics \u2014 your specific business problems. At the very highest level, insurers are very similar.<\/strong> They sell products to customers, collect premiums, invest premiums, and pay out claims. However, each insurance company\u2019s 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.<\/p>\n Once you\u2019ve identified your business problem, you need to ensure your leadership adopts a data-driven mindset<\/a>. Being data-driven means:<\/p>\n 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<\/a> \u2014 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.<\/p>\n These steps will help prepare you for the next waves of AI and whatever future waves may bring.<\/p>\n \n Generative AI for Insurance Company Decision Making<\/h2>\n
Prepare for AI-Driven Decision Making<\/h2>\n
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