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AI in Your Contact Center: Understanding the Basics
At Enterprise Connect 2018 last month, I attended as many sessions as possible regarding artificial intelligence (AI). What I observed is that while some companies represented in the sessions can provide partial AI implementations within your contact center, none can support the entire breath of what's needed within the customer engagement environment.
In this article, I'll provide a framework of the various AI-based applications so you can better understand which vendors are able to support your contact center AI automation needs.
What Is AI?
Let's start by defining AI. You'll find many different definitions for AI, but the consensus is on a mimicry of human behavior.
For many people, machine learning (ML) is necessary for true AI. ML is the ability of the machine to identify content, actions, behaviors, or intents that aren't clearly understood. You could use statistical models to calculate a probability; however, human guidance or governance in an enterprise is the best way to proceed. If you're considering AI in your enterprise, be sure to understand how you will manage or govern the ML process.
Learning is a human trait, but in some situations ML can be problematic. Consider Tay, the general-purpose chatbot Microsoft pointed at Twitter. Within 24 hours, Microsoft had to pull Tay off line because a few Twitter users taught it some very bad, racist habits. As I mentioned above, be sure you know how the ML process works for AI tools you might select.
The customer engagement corollary is the use of conversation recordings to train a Web chatbot or voice assistant. It would be great if agents handled every customer interactions precisely in the correct way, but this is never the case. When delivering a conversational AI interface with the use of previously recorded interactions be sure you're able to edit out bad behavior and incorrect answers.
When exploring AI, you'll also need to understand cognitive processing and scale.
Cognitive processor refers to the computing platform that supports the AI application. This platform can comprise a single processor or multiple processors, or even a neural network of thousands of processors. Any of these will work fine for contact center use cases, and are able to run any of the applications discussed in this article -- however, scale is an issue.
Regarding scale, a best practice is to start with a limited production implementation -- in the contact center, this means limiting the number of concurrent users your bot can support. You might start with 10 or 50 concurrent users, but not hundreds. The reason is that every use case is different, and you'll have to measure bot performance during the first couple months in order to understand how best to add processing capacity to meet your enterprise's scale.
Further, ML will tend to decrease processor load over time. Running a limited implementation for a couple months will allow processor requirements per interaction to stabilize enough so you can calculate production scale. The use of cloud-based, "leased" processors on a month-to-month or year-to-year basis will mitigate the risk of buying too much processor power.
Useful AI tools within the customer engagement environment break down into the following categories:
- Automatic speech recognition and text-to-speech (ASR/TTS)
- Robotic process automation (RPA)
- AI-based analytics (based on analysis of natural language and/or metadata)
- Conversational AI -- natural language processing (NLP) and its components: natural language understanding and natural language generation
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