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Several key insurance carriers began to experiment with AI in the last decade, including Progressive, All-State, and State Farm. Although not as large as the banking and retail industries, the AI vendor landscape in insurance is growing.

You might not know it when reading AI vendor websites or press releases from enterprises, but when you dig deep enough into why enterprises actual adopt AI, the pattern is clear:

In the past, we’ve explored the need for insurance companies to adapt to millennial buying preferences through customized policy offerings and a more personalized customer experience. AI could help to these ends, but how could insurance carriers reach this point of AI transformation?

Companies looking to apply AI are looking for a competitive advantage in their industry, something that will give them an edge in the market and help them grow. However, not every AI application can give a company a competitive advantage. Many AI applications are simply going to become the new normal.

The retail and eCommerce sectors were among the first to adopt natural language processing (NLP) in the enterprise, particularly by way of chatbots and conversational interfaces. In this article, we cover three ways retailers can use NLP to automate business processes and offer the customer a better experience. We also give examples of AI vendors that offer this technology and describe their products. The NLP capabilities we discuss include:

One of the many concerns that business leaders have around automation is how they're going to adapt their workforce to new technology. The digital age has brought a rapid shift in the skillsets employees need to go about their jobs effectively, leaving little time for business leaders to come up with strategies for how to reskill and retrain their employees, prevent their companies from drawing disdain from the public, and how to compete with their peers.

There are numerous AI initiatives in progress across the healthcare industry; some of these are for mental health and well-being. In this article, we offer an overview of how AI is facilitating mental healthcare.

Over the last four years, interviewing hundreds of AI researchers and AI enterprise leaders, we've consistently heard the same frustrations about AI adoption said time and time again.
"Culture is hard to change."
"Leadership doesn't know what they're trying to accomplish."
"Nobody knows what to do with these data scientists we've hired."
etc...
In our one-to-one work with enterprise clients, we've taken the most prevalent, recurring challenges to AI deployment and put them together into a framework of "prerequisites" to AI deployment.

Emerj Technical Advisor, German Sanchis Trilles, PhD, defines natural language processing as:
“...everything which is related to human language. If you have a system that needs to recognize what a human wrote, that’s NLP. If you have a system that tries to understand what a human said with his voice or with her voice, that’s NLP as well. If you want a system to speak and to do some speech synthesis, that’s NLP as well.

The thoughts and opinions expressed by the author of this piece and the interviewee, Maruf Hossain, are entirely their own and do not reflect the opinions, views, or stances of the companies at which they work.