If you're an enterprise organization, especially in a highly regulated sector, you understand the struggle to innovate and drive change while maintaining your security and compliance posture. In particular, your banking customers' expectations and needs are changing, and there is a broad move away from traditional branch and ATM-based services towards digital engagement. With this shift, customers now expect personalized product offerings and services tailored to their needs. To achieve this, a broad spectrum of analytics and machine learning (ML) capabilities are required. With security and compliance at the top of financial service customers' agendas, being able to rapidly innovate and stay secure is essential.

SAN FRANCISCO – The ascension of natural language processing through the ranks of artificial intelligence technologies is fairly evident. Its consumerization is demonstrated in a number of audio-related household gadgets, it's found in the most effective text analytics tools, and it's an integral aspect of speech recognition systems. Still, NLP is arguably producing the greatest impact on the enterprise in furthering the self-service movement, particularly in terms of the various implements required for Business Intelligence. According to MicroStrategy VP of Product Marketing Vijay Anand, however, the business value delivered by NLP hinges on more than simply comprehending the intention of the user: "Even with natural language queries and Alexa and all of these natural language tools, the problem of the deficit of these tools as we examine it [is] while it's easy to ask a question, I, for one, certainly believe that most people don't know what the right question is. You need to have that sort of understanding of the business to ask the correct question to get the right answer, first of all."

Here is the brief story of this evolution of self-service. Not that long-ago, shop clerks pulled merchandise for us from high shelves behind a counter. Today, we push squeaky, wheeled, metal baskets around the well-lit warehouses we call supermarkets. Innovation in retail shopping--as with all service businesses--happens through shifts in business processes and technologies. Automated self-service fuses process and technology so customers can serve themselves.

Machine learning in customer service is used to provide a higher level of convenience for customers and efficiency for support agents. Support-focused tools enabled with machine learning are growing in popularity thanks to their increasing ease-of-use and successful applications across a variety of industries. Gartner predicts that by 2021, 15 percent of customer service interactions will be handled completely by artificial intelligence. Despite it's growing popularity, there's still a lot of confusion concerning how artificial intelligence, and more specifically machine learning, fits within our current understanding of customer service. So let's clear some of that up.

Chatbots are making their way into HR. At the beginning of this wave of change, we predict chatbots will change the relationship people have with HR technology. As the application of this technology matures, it will fundamentally alter the relationships people have with each other. Chatbots are software robots that enable technology to interact with humans on human terms. With a well-trained bot, users can say or type what they need, and the bot will locate and provide information, answer questions, and take any action you train them to do.

We all long for the day when we will naturally interact with machines so seamlessly it feels human. Iconic films like "I, Robot" and "Avatar" depict a future where man and machine coexist. In "I, Robot," he continued to learn from his interactions with people and became more and more humanlike. And this isn't far off -- advances in artificial intelligence (AI) and machine learning are shaping the self-service industry today and are foundational to our future of creating humanlike machines. There are many faces to self-service.

Artificial intelligence (AI) technology has risen to the forefront of call center automation priorities. Its promises to lower costs, increase efficiency, boost productivity, and enhance the customer experience are proven now that so many brands have introduced call center AI. But what drove them to this technology? As we all know, you don't need a solution if you don't have a problem. Let's examine the top four challenges that call centers are facing today and how AI has proven to be a valuable solution.