Three Pillars of AI for Contact Centers

By Donna Fluss

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Artificial intelligence (AI) is a very broad concept and set of technologies, which must be targeted to a specific challenge in order to be effective. This means that the solution must utilize at least one of three pillars of AI for the contact center: natural language understanding/generation/processing (NLU/NLG/NLP), machine learning and real-time analytics. Additionally, the solution must either come with or enable direct access to a large repository (or multiple sources) of the data that is needed to make the solution smart.

Natural Language Processing

The first of the three AI pillars is a grouping of technologies that allow organizations to understand what customers are saying. This is referred to as NLP and has been available in one form or another for over 3 decades. These technologies include transcription, speech-to-text, and text-to-speech, and are designed to find meaning and insights in conversations, whether spoken or written. NLP enables computers to understand the meaning without a pre-defined syntax for the content. It also allows computers to respond to people in their own language. Practical applications of these technologies in contact centers are speech analytics, intelligent virtual agents (IVAs), robotics, and other types of capabilities.

Real-Time Analytics

Another pillar of AI is real-time analytics. This encompasses a highly diverse group of technologies and applications. Real-time analytics frequently takes and acts upon the input from an NLU solution. It may also draw upon historical data, a customer relationship management (CRM) solution, sales system, marketing databases, inventories, etc. to determine the most appropriate action to take. The challenge is to be able to do this fast enough to enable a live or automated “agent” to act in near-real time. Billions of dollars have been invested in a large variety of real-time analytics solutions over the past 20 years. All of the older applications were rules-based, and most were too slow to be helpful in a servicing scenario, such as in a contact center.

In the last few years, the speed of processing has improved to such a degree that real-time applications can obtain needed information quickly enough, even when it is coming from multiple internal and external sources, to add value to a customer (or employee) interaction. (Additionally, the cost of processing continues to decrease.) Examples of real-time analytics are real-time guidance, proactive servicing, predictive analytics, and behavior analytics. These applications are most effective when they are able to continuously learn and get smarter so that they can anticipate customer (and employee) needs. This brings us to our third pillar of AI in service organizations, machine learning (ML).

Machine Learning

Machine learning is a highly complex set of algorithms that can automatically “learn” and identify trends, patterns, opportunities, etc. in a data set. (A data set may include recordings or transcribed texts, such as tweets and emails.) Machine learning can operate in 3 modes: supervised, semi-supervised and unsupervised. Machine learning can be used to provide the most current information about the customer journey, which should be a priority for all service organizations. Machine learning is already being used in a growing number of contact center solutions, including speech analytics and workforce management. Speech analytics solutions utilize machine learning on a forensic basis to surface previously unidentified trends. Workforce management applies machine learning to the scheduling process to determine the algorithm best suited to optimize agent and employee schedules.

Final Thoughts

AI isn’t magic. It is a highly sophisticated set of technologies and applications that can be used to improve and optimize the performance of customer service departments and contact centers. There are dozens of AI technologies available today, but the 3 that are at the core of service are NLU/NLP/NLG, real-time analytics and machine learning. Scientists and vendors have been investing in these 3 underlying capabilities for decades, and are finally seeing some applications that provide true AI functionality to companies, though there is room for innovation. In the next 10 – 15 years, companies will find many more uses for AI, and it is going to help revolutionize the service world.