The latest iteration of artificial intelligence is emerging as a co-pilot, and possible competitor, to insurance professionals.
Click here to read the story at Leaders Edge.
There are several lessons for brokerages and human agents based on the emergence of AI agents, those interviewed said.
First, as AI assistants progress to AI agents and worthy co-pilots, brokers and agents who do not know how to exploit their capabilities will be at a decided disadvantage. They will be making recommendations and taking actions based on a smaller knowledge base and set of possibilities, will not be checked for things they may have forgotten to do (much as the AI in Spellcheck currently finds spelling flaws in communications), and some functions without AI assistance will take longer.
Brokers and agents would do well to begin experimenting with AI, which is being embedded in Internet browsers, word processors, and other applications they’re already using, The Institutes’ Miller notes. By experimenting with IT uses with a data set with which a human agent is thoroughly familiar and the AI agent has been trained on, the technology’s capabilities and limitations can more readily be gauged. Dial up the complexity or obscurity of the question posed and see what happens.
Organizations should also bring their stakeholders together to understand which processes may be ripe for agentic AI and test some use cases, says Joe Schueller, data analytics director at Waukee, Iowa-based brokerage Holmes Murphy. The company’s first hackathon, in November 2024, was a “smashing success” that demonstrated solutions that AI and agentic AI could provide to sticky business problems, he says.
Schueller says that while initial AI use cases are likely to involve existing process automation, they could eventually evolve into more sophisticated agent responses, such as whether a certain loss event is covered by insurance: “For use cases where accuracy is paramount, you have to be really buttoned down and I am not sure we are there yet with this technology. But I believe the technology will develop to that point in due time, perhaps sooner than any of us think.”
Agencies and brokerages should refine, digitize, and train secure LLMs on their proprietary data as soon as possible, so that AI agents can increase efficiencies in their various work processes and become reliable agentic assistants. Training on such a database could allow brokers and agents to learn from, and use, the best practices and experiences of the entire firm if the information from those experiences is accurately and consistently digitized.