Have you ever yelled at a customer service agent over the phone? How about an AI-powered virtual customer service agent? If you answered yes to the latter, then thanks, you’ve made a significant contribution to the evolution of artificial intelligence.
That’s because virtual assistants, and the machine learning “brains” behind them, need exposure to natural human language to learn and adapt to the world around them. And how you speak to them is very important. Picking up the intricacies of spoken language requires exposure to slang, back-and-forth conversations, figures of speech, new words, curses, and everything else that’s fluid to the human ear.
Although our everyday language is infused with these details, we leave them out when talking to mainstream virtual assistants like Alexa or Siri. So while these consumer virtual assistants remain our de-facto AI mascots, it’s the assistants dealing with business interactions like customer service that are really pushing AI forward.
It’s not what you say, it’s how you say it
When we talk to consumer virtual assistants, we tend to change our speech patterns to fit the “formula” that works for the technology. Listen to any person ask their phone about state capitals or salmon recipes — they over-pronounce words, exaggerate consonants, and speak in short, concise sentences. It’s a form of human-to-machine “dialect” we’ve developed to guarantee the technology understands what we’re saying. In other words, rather than us teaching AI to understand us, AI is re-teaching us how to speak.
But in the customer service space, enterprise virtual assistants allow for a much more natural, open-ended way of speaking. It’s the difference between deciphering “Where is the closest Hyatt hotel?” and “So I want to stay at a Hyatt nearby for the next three nights. I need a king size bed with a view of the city. What’s the closest place I can get?” Ask your smartphone this question and you’ll be lucky if it pulls up the Hyatt website. But the machine learning “brains” of enterprise virtual assistants are fed on a higher volume and larger array of input. Instead of simply hearing direct questions like “How much does the moon weigh?”, they get tons of different requests and questions.
They’re also becoming more comfortable with deciphering human emotion. Since customer service is inherently focused on problems, many customers start their interaction already frustrated. As a result, the assistant is left to work with an angry customer shouting their issue in a jumble of sentence fragments, disorganized thoughts, and possible expletives — a firehose of information; some of it relevant, but most of it not. […]