FREMONT, CA: Several years ago, Gartner predicted that 85 percent of customer interactions would occur with some form of a chatbot or a virtual assistant by the year 2020. Although it is not likely to hit that mark at present, due to various factors, the AI-driven customer experiences have shown a 10-point improvement in customer satisfaction. This is a turbocharged factor for the enterprises to mitigate risks and investigate to find tangible results from the previous AI experimentations.
Defining Conversational AI
Machine Learning (ML) is a sub-branch of the complex AI—a veteran of the technological field—has awoken in the recent decade to provide customers a data-rich experience. ML scientists create a mathematical model for the prediction of outcomes, which is then fed with data to improve the overall predictive accuracy of the model. The method above is called the training of the model.
Natural language Processing (NLP) is the field of AI that enables the computers to respond to human dialogue, whether audio or text. NLP allows users to communicate with devices and vis-à-vis. The speech recognition function is the section of NLP which has developed to over 95 percent accuracy.
When in concern with the customer, NLP, speech recognition, and ML combine to provide services for digital assistants, chatbots, and automated call centers. The module which the speech recognition functions on converts processes and contextualizes the spoken words. The ML module recognizes a method to handle and react to the verbal input. Each module on their own is continuously learning and improves using new data fed to them.
The availability of API libraries and cloud computing have offered enterprises an efficient way to integrate AI into their sales processes. The cloud enables multiple technology platforms and service providers to assist a plethora of economic models.
The cloud, NLP, and speech recognition, along with ML, are the elite of the technologies that are predicted to change the workings of customer engagement by 2020.