It is easy to get excited about what artificial intelligence (AI) offers the insurance industry. It has been a bright, shiny object on and off for a few decades now. This time around, it feels like the glitter is turning into gold, especially for insurance carriers and self-insureds that are deliberate and comprehensive with their approach.

Executive Summary

Integrating AI into core insurance platforms doesn't just involve investments in data quality and data pipelines. It requires a skilled workforce of AI and data specialists, writes CLARA Analytics VP Mubbin Rabbani.

Here, he observes that these "elite employees" create 10-times more value than average performers while pay scales don't reflect the difference, suggesting that carriers may need to break existing HR practice molds to retain needed talent. He also offers advice on how to change "us-vs.-them" (core-vs.-elite) cultures into cultures of innovation.

For carriers that can't overcome the hurdles of building talent and data pipelines to incorporate AI into existing legacy systems, Rabbani describes the advantages of implementing AI as software as a service—an alternative that comes with its own set of challenges.

AI has taken its lumps from initiatives that have overpromised and underdelivered in past cycles. And while AI has taken the blame and created a generation of skeptics, much of that blame is misplaced. Integration, or lack thereof, of well-built AI algorithms and decision models with a company’s core technology infrastructure is the leading factor associated with AI initiative failures.

Some of the blame falls on the data scientists that build AI models. They are prone to thinking that “it’s all about the model.” It’s easy for data scientists and the executives funding them to fall into this trap. After all, mining insights from the massive amounts of data insurance companies possess has been every CEO’s dream. And often, the data science team has no problem creating insights from a company’s data. However, they often don’t stop to think and ask, “Are these insights useful?” And more importantly, the question, “How can these insights be implemented given our legacy technology?” is one that no one seems to ask until everyone is ready to go.

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