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I recently discussed some of the ways AI can be trained to make collaboration applications more effective. The...
key point is the technologies underlying AI improve as they are used. This improvement, in turn, is primarily based on how end users interact with the applications.
A codependency exists that ultimately makes the AI value proposition so strong. Essentially, chatbot AI applications are a clean slate, and they depend on input from end users to create utility. In return, as these applications learn how to perform tasks proficiently, end users will come to depend on them to make their jobs easier.
The same principle applies to the contact center, albeit with a different value proposition. For decades, self-service has been defined by the much-maligned interactive voice response (IVR) application. IVR is built around the phone's dial pad, where customers are prompted to press a number for a help option that pertains to their inquiry. Given today's technologies, IVR is woefully inadequate for what customers expect. And because it quickly makes for a poor customer experience, it has become a prime pain point for contact centers.
Creating a conversational experience with AI in contact centers
AI in contact centers can provide a much-needed improvement to self-service, mainly because the chatbots driving customer interaction are conversational. When properly engineered, they can create a two-way dialog free from predefined menu options.
Chatbot AI can ask open-ended questions and engage in ways that can make customers comfortable opening up about their issues. Furthermore, a good chatbot knows when it can't take things further, and it will then seamlessly hand the call off to a live agent who is better qualified to finish the job.
While performance like this seems too good to be true, it's precisely the way AI is evolving in the contact center. Today, chatbot AI's capabilities are still quite basic. But even that is an improvement over IVR, and that's why contact centers are making the deployment of AI a priority. When these applications mature, not only will customers enjoy much better forms of self-service, but agents will perform better. That's because they'll be spending more time handling the difficult problems and leaving the more routine inquiries to chatbots.
Decision-makers don't need to get too granular on the how behind AI in contact centers. That said, it's important to understand some AI basics. Most important, be careful with the term AI. It's an umbrella for many different technologies and is not itself a product or a single approach. Broadly, AI is about emulating human behavior with machines -- computers. And when we can properly train them to do that, those machines become more valuable to us.
Building blocks for AI in contact centers
For business purposes -- and, more specifically, the contact center -- there are two AI building blocks you should know about. The first is machine learning, which uses large-scale computational power to create algorithms that identify patterns in human behavior. For self-service, machine learning would be used to automate the process for solving simple or routine customer inquiries.
The effectiveness of machine learning improves when you have conversational chatbots for deeper engagement with customers. That capability comes mainly from the second building block, natural language processing (NLP), which applies speech recognition technology to enable those humanlike conversations.
Not only does NLP learn how to accurately recognize what is being said, but it also recognizes the context and the speaker's intentions. That's what elevates standard speech recognition to chatbot AI in contact centers. There's more to learn about machine learning and NLP, but if AI is new to you, getting acquainted with these technologies will help you begin to understand why AI is a better way to provide self-service.