Mar 09 2019 Incorporating AI Into Your Contact Center


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In the past year, there have been numerous content pieces about Artificial Intelligence (AI) and the future of work itself. While self-driving and cars and robot butlers are fascinating topics, AI’s applications in customer service and customer experience (CX) are here now and are permanently transforming how customers interact with brands.

Contact center decision makers may be well read on how technologies like IBM Watson are changing the landscape of CX. Industry professionals we have spoken to have two immediate thoughts. First, they agree with the value proposition of vastly improved CX & improved operating margins but are unsure how to navigate the chasm of cost and capability required to get there from where they are now. Second, they feel AI is not applicable to their business because they have made the strategic decision to have customers interact with a live agent in all circumstances. The reality is that AI can improve contact centers, regardless of human interaction strategies. AI can be deployed to automate routine processes OR to super-charge agents to vastly improve both customer and agent experience.

In the following blog series, we will provide some education on AI in the call center (with a particular focus on Natural Language Processing (NLP)) and how an organization might incrementally move toward this reality.

First, let’s get a better understanding of what AI is and its utility in the contact center. A recent McKinsey Global Institute report categorized AI in 5 broad technologies:

  1. Physical AI – this is robotics and autonomous vehicles
  2. Computer Vision – the ability to process images and video
  3. Natural Language Processing – the ability of machines to understand and interpret human language the way it is written or spoken
  4. Virtual Agents (Conversational Interfaces) – this is a subset of NLP.
  5. Machine Learning – systems that can learn based on previous experiences.

As you can see, the contact center has direct applications in at least three out of these five applications.

The following flow diagram illustrates this with a little more detail

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For the remainder of this blog series, we will focus primarily on NLP and later Virtual Agent capabilities. NLP has the promise of changing the way humans interact with machines. Effective NLP systems can ingest what is said, comprehend its meaning (this commonly known as natural language understanding (NLU), determine appropriate action, and respond back in language the user will understand across voice and text-based channels. NLP can also detect sentiment and emotion in conversations.

This technology can be used in an IVR to anticipate needs of customers, augment conversations by providing instant help with virtual assistance, and automate routine tasks saving valuable human agent time for those interactions where they’re most needed. In addition, speech analytics can be used to mine recorded or calls or monitor active calls to provide critical insights, ensure compliance, and vastly improve and personalize agent training.

In our next blog, we will discuss taking the first step toward meaningful AI adoption: Using “Natural Language-like” grammars in your IVR.