The role reversal between humans and technology in which humans play an assistive role and add value through productive constraint will require us to rebuild some basic competence in two important areas:

Conversational intelligence: The give and take of a conversation where each party is providing data, context, and emotional cues more closely resembles the interactions large language models are optimized for. Unlike search engine algorithms, generative AI solutions (specifically chatbots) are extremely adept at altering what information is provided to a user and in what form. This requires the user interaction with technology to be more iterative, conversational, collaborative, and even playful than what we've become accustomed to, in order to get the most useful data and presentation back from our inquiries.
Critical thinking: The highly variable nature of what information is provided to a user, and in what form, requires us to approach generative AI solutions with a healthy skepticism. Generative AI solutions are context engines that alter their responses to suit what they believe the user is asking for, and are known to suffer from bias, seasonal laziness, and hallucination; generating inaccurate information as if it were true. Users of generative AI must be mindful of these limitations and not mistake the perceived authoritativeness of AI outputs as absolute truth.

Do these things now

As with all innovation, knowledge workers who see change coming and adopt a growth mindset will be the ones most likely to thrive in a future embedded with generative AI solutions. Insurance knowledge workers should consider doing these four things to broaden understanding and competence with these game-changing tools.

Learn: One need not be an expert in AI development to realize its benefits, but developing a very basic understanding of how large language models and generative AI solutions are built and trained is a good first step. Any number of resources are available from free YouTube videos to low-cost learning paths offered by online course providers, with the volume and quality of instruction growing rapidly.
Play: There is no substitute for using generative AI solutions to understand their behavior and how they respond to inquiries with both information and context. Free versions of chatbots such as OpenAI's ChatGPT and Google's Gemini are great tools to start building conversational skills. Ask the tool to respond to a question or summarize something you're interested in and observe how it responds to follow-up requests for more detail or for an answer to be provided in a specific format.
Use: Whatever your day-to-day work may be, consider how use of an AI solution could augment your productivity. Is there research you're engaged in that could be accelerated through generative AI? Business requirements or rules it could help author? A proposal or presentation it could check for accuracy and originality? By applying your own expertise and a critical eye to the AI solution's contribution, you will begin to learn where its greatest value may lie for your current role. As you do, it is important to be mindful of any AI use policies your enterprise has in place, and ensure that your obligations to protecting customer data and trade secret information are never compromised.
Share: Most enterprises now understand the potential of generative AI, and many have taken steps to either build proprietary small language models or pursue targeted proofs of concept (PoC). Going forward, moving from PoC to large-scale implementation will require a better understanding of specific return on investment for various use cases. By sharing what you learn through experimentation, you can establish yourself as a resource and contributor to the firm's knowledge base. Choosing to engage, learn, and share are hallmarks of a growth mindset, and may likely be the behaviors that differentiate those who adapt and thrive in the future of insurance knowledge work.

Chad Sands MBA, CPCU, CLU, FLMI is an IT leader and architect at State Farm Insurance. The opinions expressed are the author's own.

Special Reports /

Generative AI and the transformation of insurance knowledge work

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