When thinking about advancements in technology and data-driven insights, insurers are often confused about AI and BI—what they are, and how to even know if they are valuable to their business.

We have found that to be true in our interactions with carriers across the country, no matter what their size. When we are faced with questions about “intelligence,” we end up first defining these two categories and then introducing a third term: “insurance intelligence.” This new term opens the door for insurers to begin moving toward the next evolution of their business.

Having seen the interest and attention during customer discussions, this article will walk through these stages while also getting into why “insurance intelligence” is the best way forward for the industry.

How Is Business Intelligence Viewed in the Insurance Industry?

From a general standpoint, BI has taught businesses how to make better decisions by more effectively analyzing and interpreting data. The insurers that have implemented BI have typically worked with horizontal players that initially impressed by delivering new insights that made incremental improvements to understanding business operations and predicting future business outcomes.

While the honeymoon with BI is nice, it quickly fades, and insurers often begin trying to justify their purchases by making countless charts and reports to demonstrate the value of the technology to peers and senior management. Unfortunately, the opposite often happens, and internal confidence in the value of BI slowly wanes.

Artificial Intelligence Must Be the Ticket, Right?

For insurers that were brave enough to experiment with BI, they will have been pitched the idea of AI from their vendor partner or they internally decided to evaluate AI. There is no argument that AI, which really is machine learning (ML), has exciting potential. True AI where businesses and consumers are benefiting from sentient robots and computer programs is still a ways off. This is why I referred to it as ML, since the two are often confused. Machine learning is the precursor to AI, and like BI, it has the power to extract new insights from insurer data but with more powerful analytics capabilities to help insurers make stronger automated decisions about internal operations and pricing than BI can make on its own.

The slight but crucial difference between BI and ML is the element of deeper data insights coupled with the automation that ML provides.

Are BI and ML Enough for Insurers to be Successful in the 2020s?

Before the pandemic, there was not as much attention from the insured on insurance products, customer experience and communication. Insurance was to some extent viewed like going to the dentist or doctor—something consumers and businesses should not do without. However, within a couple of short years, every business in every industry is under the microscope by customers for innovation and exceptional customer experiences. I would argue BI and ML are not enough to support long-term growth.

Yes, the insights from BI and ML are valuable to help predict certain business or industry trends along with automating select customer engagements and business functions. However, this is more internally facing for insurers than for the insured.

The Need for Insurance Intelligence

Throughout my career, I have noticed that while companies are trying to free themselves from the constraints of legacy technology, they burden themselves by locking into the hot new category (ML, AI as an example) without thinking about what technology functions will actually deliver value to the insured. Today, insurers are often not considering what technologies will enable the creation of modern products addressing risks of a more digital-centric world integrated with smart hardware impacting healthcare, transportation and many areas of consumer life.

This is why I believe there needs to be a more holistic and strategic approach to blending emerging technologies with legacy technology, coupled with insurance-centric knowledge and insights from actuaries and data scientists. In recent meetings with carriers, we have been calling this approach “insurance intelligence.”

Put simply, insurance intelligence is about starting from the business problems and customer value—for example, predicting new risks, creating modern products and improving profitability—and then determining what customized technology environment, which combines emerging and traditional technologies with other insights, can solve these problems. Insurance intelligence is the opposite of how our industry has done things where a technology platform or tool is built in a vacuum without these considerations and then force fit into carriers.

Insurance intelligence depends on the business problems being faced by the insurer and the insured to create customized solutions that deliver immediate and long-term customer value. There could be deployments of technologies that rely less on AI/ML and more on traditional tools, or vice versa. This type of technology strategy means insurers will not be locked into the latest technology wave in the news to give the appearance of being innovative, but it is about picking, choosing and uniting the right solutions from the last decade that make the best business sense to be a step ahead of what the insured needs.

The types of healthcare and automobile monitoring that will shape our world are powered by emerging technologies coupled with contextual intelligence about the respective industry. Beyond this type of monitoring, there is talk of flying cars and helicopters with auto-pilot capabilities. If these advances take shape in this decade or into the next, AI and BI will not be sufficient enough to predict the types of novel insurance products that customers will expect from their insurers to support their more technologically-centric lives. This potential gap is often where entrants from outside the insurance industry come in, most from technology or consumer industries with a new vision for integrated ecosystems and services where insurance is a component of many other services in a greater offering.

By focusing on insurance intelligence, carriers no longer are concerned about a particular tool failing. Instead, they have their eye on the real goal, which is not dependent on a particular technology category. There is still the need at times to use a Swiss Army knife tool like BI to solve a particular business problem. However, insurance intelligence is more like a complete house where the insured can interact with carriers about particular elements of their life or business that need protection. Similar to how living in a house sometimes means a person desires or needs to renovate a room instead of just fixing a leaky faucet, insurance intelligence is giving carriers a truly powerful ability to become trusted partners that the insured cannot live without.

At the end of the day, a homeowner does not care what type of pliers, hammer or wrench is used to renovate their kitchen; they care about the delicious food they will make for their family and friends. Insurance intelligence is the true path forward and our industry’s historical path that we have been derailed from in recent years.

Our customers do not care about the how, and it is time carriers take a broader and yet more granular look at all technology available coupled with real expertise about the insurance industry to thrive in the future.