Once upon a time, large and lumbering insurance companies created financial models based on the statistical sampling of previous performance to predict future outcomes. They created risk pools based on zip codes and crime rates, gender and age. This is how the insurers established premium costs. Of all the industries that fall under the broader subset of financial services, insurance was always a little bit slower to innovate. Against that backdrop, the new players saw an opportunity.

For insurance disruptors, that opportunity involved the use of IoT sensors to price coverage based on real events, in real time, using data linked to individuals rather than samples of data linked to groups. This meant safe drivers could pay less for their policy, because the data — not some generalization based on zip code — showed they took fewer risks. They were no longer part of a broad pool of risk assessments, but a series of individual data points analyzed by machine learning software that extracted patterns in driving habits and determined the risk each person posed to the insurance company.

So how exactly did the disruptors do it? By installing an IoT sensor in drivers’ glove compartments, of course. The sensors can track everything, from customer speed, stop frequency and turn motions, to frequency of maintenance and other specific driving habits. This produced volumes of driving data that allowed car insurance companies to move from pricing based on the likely behavior of the risk category to the actual behavior of individuals. Known as usage-based insurance, these new technologies showed the power of combining smart IoT sensors and AI analytics.

The result: Customers save money and live happily ever after. Insurers improve their ability to assess risks. Everybody wins.

What industry is next for IoT? The better question might be, what industry isn’t next.