AI Articles and Analysis about Customer service

The insurance industry is being disrupted like it hasn't in decades. Unlike other events like Hurricane Sandy or even the 2008 financial crisis, the coronavirus is impacting essentially every corner of the world and more or less every industry.

Progressive is one of the largest auto insurers in the US. The company has been experimenting with AI since the middle of the 2010s, with customer-facing applications that update insurance premiums based on driving habits and answer questions in a chat window. In this article, we discuss both of these AI use-cases. More specifically:

The retail industry collects massive amounts of data every day, and this makes its key processes ripe for automation with machine learning. Along with the manufacturing sector, the retail industry likely stands to benefit the most from one particular AI technique in the next few years: machine vision, also known as computer vision.

In banking and finance, chatbots have the potential to improve the customer experience by allowing customers to check their account balances, transfer money, learn about interest rates, change their billing addresses, and more.

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:

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.

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.