Some insurers have implemented chatbots to start the auto quoting process on Facebook Messenger. For more complex discussions, representatives can then pick up the conversation by phone or private message.

Other chatbots can support an agent — via a conversational interface — to search and make queries against the agent's book of business (to access customer data, portfolio, and opportunity information), and their calendar and upcoming tasks.

Customers are warming to chatbots

So far, insurance customers have not shied away. In fact, they have taken a shine to insurer's digital helpers as much as they have to those of retail brands.

A recent Pointsource survey revealed that preferences for using chatbots with insurance are no different from the preferences of engaging with retail companies. A total of 77 percent of consumers are okay with interacting with chatbots if it means avoiding wait times for customer service representatives with insurance companies, versus 75 percent for retail. On average, about one in four consumers at this point prefer chatbots for many insurance interactions, while about half want to talk with live humans, and the remaining one-fourth don't mind one way or the other.

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The next step for insurance AI

There is enormous opportunity for insurers to use insights curated by AI to devise and optimize customer options and actions, such as recommending products and services best suited to customer needs, anticipating future requests, and anticipating factors that might cause customer churn.

For this reason, industry research shows data/text mining and machine learning as the AI technology area with the most potential for insurers. Vast stores of structured and unstructured data are collected and managed by insurers. Further insights from data on customers — including emails, notes and online postings from, to, and about customers— and on each customer's risks (aided by the growing amount of smart home, smart car, and other IoT data) can drive customer value.

Touchless auto claims are a good example. Chatbots can currently receive first notice of loss messages and uptake photos of the damage. The next step is AI algorithms, currently in development, that will take the images and estimate the repair cost, while simultaneously performing fraud checks, and promptly making a cash settlement offer to claimants. These AI applications represent a differentiating level of service by insurers that adopt them.

Expect chatbots to get smarter and grow longer arms and fingers that will scour vast libraries of data to return not just information, but patterns of information that can be parsed to make customer and agent interactions increasingly relevant and real-time.

Is AI a threat to insurance distribution?

Some look at the extraordinary capabilities of AI as a threat to the distribution system. But it's hard to see how the real value that brokers and agents offer is going to be replaced by AI. Brokers who understand how to use AI to get better at their job or focus more of their time on things that really matter to customers are likely to be more successful. Those that don't may find themselves at a disadvantage.

Expect, for example, that AI will uncover personalized risk data and coverage gaps that customers will be receptive to hearing about and mitigating through their agents.

What is holding insurance AI back?

There is no shortage of ideas for the application of AI. However, what will hold back insurers' full leverage of AI is their inability to provide AI-enabled insight in real time. For the majority of insurers, their operational footprint and analytics footprint are separate. The two need to be merged to overcome the technology gap and time delay this creates.

New cloud-native architectures for core, digital, and data operations are designed to address this bottleneck. By removing constraints on performance and data size, these new solutions will allow the application of all manner of personalized interactions leveraging AI for real-time user experience at point of sale or engagement.