By Sreenivas Gudavalli When it comes to customer service, long wait time and continual call rerouting can be discomforting besides not being a pleasant experience for customers. Even the world's most prominent companies having high volume customer inflow struggle with sluggish customer service engagement, as well as the delayed turn-around times in handling customer queries, leading to negative outcomes, including possible damage to brand reputation. Utilizing tech to enhance UX Over the years, there has been constant evolution in the customer experience (UX) and technology. That said, the incorporation of artificial intelligence (AI), machine learning (ML)-powered speech analytics into customer service operations are transforming how businesses interact with their customers. Today, more businesses are taking advantage of AI to automate and improve their Customer Relationship Management (CRM) than ever before.

For many consumers, the concept of "customer service" is frustrating to the point of humor. Even with the many advancements in digital technology today, consumers still experience lackluster resolution to often simple problems. They find themselves upset because whenever they have an issue, the in-person employee doesn't know how to help them or they "never get to talk to a real person" when automated responses do not answer their question. Customer service shouldn't be this way! The solution is an Anticipatory mindset that starts with a foundational understanding that exponential digital change will only increase, and consumers' wants and needs will transform as well.

Conversational AI, along with natural language processing (NLP), automatic speech recognition (ASR), advanced dialog management, and machine learning (ML), are changing the way humans relate and communicate with machines. Conversational AI and its associated technologies enable humans to have conversations with machines in much the same manner as they do with one another. A recent report from Markets and Markets revealed that the global conversational AI market size is expected to grow from $4.2 billion in 2019 to $15.7 billion by 2024. The evolution of AI over the last decade has produced applications that are capable of convincing a person that they are having a conversation with another human, aka the Turing Test. It's not just the "human" level of interactions that makes conversational AI so important, but rather the ability for the AI app to make informed decisions based on the actionable insights it has gathered from data.

Insurance is a serious and complex subject. When it comes to securing their lives, their health, and their finances from any possible eventuality, customers understandably want to leave no stone unturned. During the process of buying insurance, they require access to detailed information while evaluating multiple options, in order to make an informed decision. And they will also need constant post-purchase support when it comes to making inquiries about their policies or filing claims. Needless to say, insurance firms across the globe receive massive volumes of queries every day, from prospective customers looking to buy insurance, and existing customers looking for help.

As we continue our digital transformation journey and just headed into 2020, we will go over each Technology Trend that is already impacting our lives or soon will. Machine learning („ML") is one of those digital trends that will become more and more relevant to us all. Machine Learning is an application of Artificial Intelligence („AI") with the objective to search and find relevant patterns within different data-sources with the use of sophistic mathematical models. When such patterns are detected, these results can be used for multiple purposes in AI applications or use cases. Machine Learning provides results on the basis of historical data, which gets more and more real-time as sensors from Internet of Things („IoT") fuels large datasets in milliseconds. The quality – and the performance – of these patterns (models) is highly dependent on both the quality and quantity of the data that was used to ‚train' it. Side note: don't rely on Machine Learning to change bad or inconsistent data too useful data. To date, Machine Learning has been used in multiple industries with different use cases. According to independent research, the global Machine Learning market is expected to grow at a CAGR of 48.3% to reach $19.40 billion by 2023, during the forecast period of 2018-2023. More and more organizations are starting ML-centric projects to gain market share with new business models driven on ML outcome. Pinterest uses Machine Learning to find patterns in pictures to enhance their spam moderations and content discovery for advertising. This application of ML and AI enhances the end-user experience and drives additional revenue to Pinterest. Allot of websites have already implemented chatbots to enhance their user experience. Chatbots are the small'pop-up screens' that make you believe that you are directly talking to a service representative, instead, you are ‚interfacing' with an AI that uses ML to provide you with accurate answers or solutions. If for whatever reason, the solution cannot be found or processed, you could be connected to a service representative. I need to say'could' as some websites already 100 percent rely on chatbots. This allows organizations to lower personal costs whilst in parallel increase user experience as for example, no waiting times apply for ‚talking' to a service representative. Twitter is using the ML patterns to protect users from spam and evaluates each tweet in real-time to ‚score' them according to various metrics. Ultimately, the Twitter algorithms then display tweets in your feed that are likely to drive the most engagement. The decisions, which show up in your feed, can be adjusted on your individual preferences resulting again in higher engagements. Salesforce, a global Customer Relationship Management („CRM") software company, uses AI (which they named ‚Einstein') to predict new leads and/or when you should follow up on a specific email or phone call.

As such, it should be no surprise that many forward-thinking companies are already experimenting with artificial intelligence (AI) technology to improve their processes and service their customers better. Primarily, AI is currently being deployed in customer service as a means to either augment, or in some cases replace human agents. The primary goals of these initiatives are to improve the customer experience, and reduce costs associated with human service agents. While it is of course true that AI and automation technologies are not yet sophisticated enough to perform all of the tasks currently undertaken by human representatives, many routine consumer requests are simple enough for AI to handle without human input. Perhaps more importantly, AI can deliver a level of responsiveness to an influx of customer requests that isn't humanly possible – or at least isn't possible without spending a fortune on staffing.

Artificial Intelligence is currently being deployed in customer service to both augment and replace human agents – with the primary goals of improving the customer experience and reducing human customer service costs. While the technology is not yet able to perform all the tasks a human customer service representative could, many consumer requests are very simple ask that sometimes be handled by current AI technologies without human input. In this article we'll shed light on the current trends and use-cases that business leaders should be considering today. We've broken this article on AI for customer service into the following four sections: According to the Bureau of Labor Statistics there were just over 2.7 million Americans employed as customer service representatives with a mean wage of $35,170. Any technology that could improve the efficiency of customer service representatives or make some of these positions redundant would potentially produce significant business savings.

Airlines are coming off a rough six months of brand perception. Forget about mishandled luggage, the bigger problem is mishandled passengers. Customer service – or lack of it – has given the airline industry a "black eye." Even the most customer-focused airlines are not immune to computer outages and winter storms that cause thousands of flights to be canceled. And because of the negative publicity the industry has received, it is under a microscope.

Going beyond traditional rules-based voice or chatbot digital banking solutions, a non-bot, natural language banking experience is being offered to USAA members in an Amazon Alexa pilot with Clinc. Last September, a team of computer science professors at the University of Michigan introduced an application developed in their research lab that they believed would change the way consumers would do banking in the future. Combining the science and technology from academia, with the needs for a better voice-first mobile banking capability, Clinc won'Best of Show' honors at Finovate in New York City with their Finie ("the financial genie") application. The intelligent personal assistant uses sophisticated natural language processing engines that have been trained with a deeper knowledge of the financial and banking industry as opposed to using a rules-based approach. Unlike solutions that currently exist from Siri, Alexa and Cortana, Clinc's machine learning capability allows the application to expand knowledge and improve responses with every query.

Going beyond traditional rules-based voice or chatbot digital banking solutions, a non-bot, natural language banking experience is being offered to USAA members in an Amazon Alexa pilot with Clinc. Last September, a team of computer science professors at the University of Michigan introduced an application developed in their research lab that they believed would change the way consumers would do banking in the future. Combining the science and technology from academia, with the needs for a better voice-first mobile banking capability, Clinc won'Best of Show' honors at Finovate in New York City with their Finie ("the financial genie") application. The intelligent personal assistant uses sophisticated natural language processing engines that have been trained with a deeper knowledge of the financial and banking industry as opposed to using a rules-based approach. Unlike solutions that currently exist from Siri, Alexa and Cortana, Clinc's machine learning capability allows the application to expand knowledge and improve responses with every query.