What is Conversational AI?
Conversational AI: Computers that talk
Conversational AI systems are computers or programs that interact with people through a natural language conversation, just as you would have with a live person. Conversational AI draws from a group of technologies, that include Speech Recognition, Text-To-Speech, Voice Biometrics, Natural Language Processing (NLP) and Machine Learning (ML). Conversational AI systems make it easier for your customers to get support through your automated system because the complexity of the interaction can be dramatically simplified. We’ve all encountered IVR systems that lead you through a series of long and frustrating interactions where at each stage the caller has to play a game of “20 questions” by answering yes, no type questions. Conversational AI increases the percentage of inquiries that can be automated, helps you extend the hours of service and enables you to offer service to a larger audience. Conversations can be complex; it’s not just about the words you use, so a good Conversational AI system requires an understanding of context, tone, sentiment and previous conversations. Virtual Agents use Conversational AI to have a natural, free flowing conversation with a customer.
Conversational AI Defined
Artificial intelligence as a field has been around for many decades. It has arrived, so to speak, because processing and storage have gotten dramatically less expensive, while useful data has become more widely and deeply available. Machine learning (ML) is the subset of AI that has woken up in this decade to enable data-rich customer experiences. In a nutshell, ML scientists build a mathematical model
for predicting outcomes, then feed data to that model so that it improves overall predictive accuracy. This activity is more colloquially known as “training the model” for machine learning. Because the science and compute power have improved dramatically, ML has gotten very good in the last few years.
Why You Need to Embrace Conversational AI for Customer Care
Major League Baseball is all over machine learning, using it and the copious data that is tracked from every pitch in every at-bat, to fundamentally change the way the game is managed, from defensive positioning to pitch selection to lineup selection. Similarly, the real estate company Zillow uses ML and its vast database
of user interactions to predict what houses a specific shopper might want to see, adjusting as that shopper clicks through each offering. Companies are also using MLto train autonomous vehicles, improve manufacturing productivity, and to improve fraud detection in financial transactions.
Natural Language Processing (NLP) is the field of artificial intelligence that allows computers to interact with human dialogue, whether spoken or written. In other words, NLP allows people to talk or write to devices and for devices to understand what that person has said. NLP has also gotten very good in the last few years, to the point where 97% accuracy is not uncommon, close to call center benchmarks. The specific subset of NLP that deals with the spoken voice is called speech recognition, which itself is approaching 95% accuracy.
In customer scenarios, NLP, speech recognition, and ML hybridize together toprovide services via automated call centers and digital assistants such as smartspeakers and phones. The speech recognition module processes and rationalizes the spoken word and the ML module figures out how to handle and respond to the verbal input. These technologies are constantly learning and improving because their algorithms get better as they are fed more data.
The advent of cloud computing and the growth of powerful API libraries have provided enterprises an agile and efficient way to build or integrate AI into their sales processes. It is also the cloud that enables multiple technology platforms and service providers to support a variety of economic models (i.e., build vs. buy vs. subscribe).
The cloud, NLP, speech recognition, and ML are the key technologies behind Gartner’s prediction that AI has arrived and by 2020 will change the game forcustomer engagement. They have arrived. And everything is changing.