In today’s digitally connected world, consumers demand an unprecedented level of 24x7x365 customer service. In fact, most unsatisfied customers will not return. Watson Assistant solves this. It empowers enterprises to continuously address and resolve customer and employee inquiries across multiple channels with ease.

What is conversational AI?
Conversational artificial intelligence (AI) refers to technologies, like chatbots or virtual agents, which users can talk to. They use large volumes of data, machine learning, and natural language processing to help imitate human interactions, recognizing speech and text inputs and translating their meanings across various languages.

Components of conversational AI
Conversational AI combines natural language processing (NLP) with machine learning. These NLP processes flow into a constant feedback loop with machine learning processes to continuously improve the AI algorithms. Conversational AI has principle components that allow it to process, understand, and generate response in a natural way.

Machine Learning (ML) is a sub-field of artificial intelligence, made up of a set of algorithms, features, and data sets that continuously improve themselves with experience. As the input grows, the AI platform machine gets better at recognizing patterns and uses it to make predictions.

Natural language processing is the current method of analyzing language with the help of machine learning used in conversational AI. Before machine learning, the evolution of language processing methodologies went from linguistics to computational linguistics to statistical natural language processing. In the future, deep learning will advance the natural language processing capabilities of conversational AI even further.

NLP consists of four steps:

- Input generation: Users provide input through a website or an app; the format of the input can either be voice or text.
- Input analysis: If the input is text-based, the conversational AI solution app will use natural language understanding (NLU) to decipher the meaning of the input and derive its intention. However, if the input is speech-based, it’ll leverage a combination of automatic speech recognition (ASR) and NLU to analyze the data.
- Dialogue management: During this stage, Natural Language Generation (NLG), a component of NLP, formulates a response
- Reinforcement learning: Finally, machine learning algorithms refine responses over time to ensure accuracy

How to create conversational AI Conversational AI starts with thinking about how your potential users might want to interact with your product and the primary questions that they may have. You can then use conversational AI tools to help route them to relevant information. In this section, we’ll walk through ways to start planning and creating a conversational AI.

Frequently asked questions are the foundation of the conversational AI development process. They help you define the main needs and concerns of your end users, which will, in turn, alleviate some of the call volume for your support team. If you don’t have a FAQ list available for your product, then start with your customer success team to determine the appropriate list of questions that your conversational AI can assist with.

For example, let’s say you’re a bank. Your starting list of FAQs might be:
How do I access my account?
Where do I find my routing and account number?
When will my debit card arrive?
How do I activate my debit card? How do I order checks?
How do I talk to a local banker?