In this article, you’ll learn everything about conversational AI. We will explain what it is, how it works, and why many companies are adopting it.
What is Conversational AI?
First of all, let’s define Conversational Artificial Intelligence (AI). According to Deloitte, it is a programmatic and intelligent way of offering a conversational experience to mimic conversations with real people, through digital and telecommunication technologies. These interactions are automated and can be both written or oral. For example, chatbots, voice devices like Siri and Alexa, apps, websites, and social platforms.
Businesses are using Conversational AI for marketing, sales, customer support, and more. Users want to get fast and clear responses to their requests and conversation AI does that organically. Consequently, it helps companies to increase their conversion rates by engaging with customers at every step of their experience.
How does conversation AI work?
In order to understand the process of conversational AI, first, we need to define two important concepts:
- Natural Language Processing (NLP): Quoting IBM, Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand texts and spoken words in much the same way human beings can.
- Machine Learning (ML): According to IBM, Machine learning is a branch of artificial intelligence and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
The process starts when the message is received. Conversational AI uses techniques of NLP such as intent recognition to understand the request even if it is phrased differently and entity recognition to extract all the relevant information needed to accurately fulfill the user’s intent. The goal here is to reduce users’ objectives to a simple and predefined task.
Secondly, the computer needs to understand the intent and act consequently. It requires machine learning to recognize the user’s intent and learn how to respond correctly. ML also allows computers to learn and improve without the need to be programmed by a human.
Technologies like Siri, Alexa, and Google Assistant are perfect examples of conversational AI. Machine Learning trains these virtual assistants to perform tasks and learn constantly without having a human regularly programming it. So Alexa, Siri, or Google Assistant can recognize when they make an error while responding to a specific query and they are able to learn and rectify this mistake in the future.
How do Conversation Designers design AI assistants?
Conversation designers can influence the conversations of the AI assistant and the experience as a whole. We can think of it as the path where the interactions between the user and the computer will take place. How do we create that path? By defining patterns, rules, and different elements of the script.
Conversation designers can design the bot persona, understand the context, use other design artifacts and methodologies to design a great user experience, and many other things… To make sure you’re designing a great Conversational AI experience, you need:
- Design the right user case: make sure this Conversational AI project is adding something special to the final user or solving a stakeholder problem; otherwise, it will not generate the results you would expect.
- Continuously improve your AI technology in the backend: make sure you’re providing healthy training data and are improving your AI model to make sure it can understand the users’ intents and respond to it at scale.
One of the main difficulties we face when designing conversational AI is the impossibility to anticipate customers’ questions and responses. In order to simplify the practice, the team of Rasa brought the concept of Conversation-driven development (CDD). It is the process of listening to your users and using those insights to improve your AI assistant. This practice includes sharing your prototype as early as possible to get feedback, reviewing the conversations and annotating examples of real messages to get insights and tracking conversations to understand what’s working and what’s not in order to improve.
How can Botsociety help you to design your next Conversation AI project?
Inspired by Rasa, we came with the concept of Conversation Driven Design. The problem we were facing was how much time it took to create and validate a conversation design. Our solution to that problem was the Auto-testing with Botsociety. It is as simple as start designing and clicking a button, then we ask OpenAI to act as the human to test your design and you gather the feedback to move on.
Companies are implementing conversational AI to interact with their customers more and more nowadays. It is a fast and simple way to give users solutions in a matter of seconds without the intervention of human agents. You can design your next AI conversation using Botsociety.