In this article, we will be going to take a look at:
- What conversational AI is
- The components of conversational AI
- How does conversational AI work?
- How to implement conversational AI?
- What are the challenges of conversational AI?
What is conversational AI?
Conversational AI technology is a broad term that describes different methods of allowing computers to move forward with the conversation with humans. This AI technology can be anywhere from simple NLP (natural language processing) to complex machine learning models that translate a large variety of input and move the conversations forward.
The common use of conversational AI is being found in chatbots. It uses NLP to convert a user’s language and move forward a conversation. Some other examples of conversational AI are voice assistants, virtual assistants, and customer service chatbots.
Now, customers want to communicate with brands through their websites, messaging channels, mobile apps, chatbots, and interactive voice response (IVR). All of them like a personalized, consistent, easy, and fun experience to interact with brands.
To meet consumers’ expectations, brands need to use intelligent automation across their marketing channels. Conversational AI fuels those conversations with customers, improves CX, boosts customer satisfaction, increases customer lifetime value (LTV), and drives loyalty.
What are the different parts of conversational AI?
There are five components of conversational AI. All the five parts move together to let a computer know and reply to customer interactions:
Natural language processing
It allows a system to know the human language and reply to them in an easy-to-know language. It also includes learning the definition of words, the formation of sentences, slang, and idiomatic expressions.
NLP makes use of machine learning to train systems to know the human dialect. NLP algorithms use large sets of data to figure out how words are interrelated and how they can be used in a range of situations.
It is an area of AI that allows computers to know data without explicitly being programmed. The algorithms used in machine learning automatically boost their work as they get put together in front of new data.
It is also used to recognize different structures in data and develop replicas of how things work, involving the human brain.
It is the procedure of pulling out different details from text input. The information is then used to figure out various parts of the sentence, like, verb, object, and subject. Text analysis also identifies distinct types of terms in a sentence, like, as adjectives, nouns, and verbs.
With text analysis, one can also know the relationship between different words, the factors of a sentence, the sentiment of the text, and also the topic of the content.
It is defined as the ability to explain and know different digital pictures. This also includes recognizing different parts in a picture, along with the orientation and location of those items.
Computer vision is used to recognize the different contents in a picture, and the relationship between different items in a picture. The technology can also help to identify different expressions of people in an image, and the factors of that picture.
It is defined as the ability of the computer to know human speech. It also involves recognizing various noises in a verbal sentence, along with the syntax and structure of the sentence.
The AI technology is also used to translate verbal words into terms, explain the definition of words, and expressions of customers talking in a video, and know the meaning of dialogue.
How does conversational AI work?
Taking the help of a typical machine learning and deep neural networks (DNN), here’s what a typical conversational AI workflow looks like:
- It includes an interface that lets users enter text or spoken words through Automatic Speech recognition (ASR) which converts speech into text and inputs into the system.
- After that NLP finds out the user’s intention from the entered speech or term and translates the term into the data structure.
- Natural language knowing (NLU) processes the data based on grammar, factors, and meaning to figure out the intent and entity. Ultimately, acting as a dialogue management tool to create relevant replies.
- Lastly, there’s an AI software that forecasts the best answer for the buyer depending on the artificial intelligence’s training model data and the user’s intention. Taking all the above steps, natural language generation (NLG) generates a relevant answer for the asked question.
In most cases, the AI model, NLP, and the user interface are all provided by the same service provider, known as a conversational AI platform provider. Though, it’s also possible to use these components from different service providers.
How to implement conversational AI?
The most used method is to use NLP to transform the content into a language that the machine can understand. Later, this data can be used in a chatbot or other conversational AI tool to help users.
As mentioned before, NLP is used to convert text into machine language. It’s also used to know commands and questions given by the user. And it can also analyze and reply to user feedback.
Various methods can be taken to NLP. Some machines use machine learning to teach systems to know the natural language. While other systems use a rule-based method where a user writes a set of rules that mentions how a computer should explain and translate to user input.
After the computer system is trained, it can then use the given information to fuel a chatbot or any other conversational AI tool. Then, the tool can be used to answer questions, handle customer queries, and perform other duties that involve human conversation.
What are the challenges of conversational AI?
The technology used in conversational AI has rapidly advanced over the years. Still, there are some challenges that are being faced by the users while using conversational AI tools. The challenges include:
- Advancing natural language processing technology that can know and explain information imputed by the users. This is a tough task that contains a lot of hard work and also requires development and research.
- knowing the meaning of a sentence so that the conversational AI tool can give the correct response to the user. Often, it can be challenging when the situation includes various topics or multiple users.
- Building and integrating a conversational AI tool into the current business model can be a big challenge on its own. In order to do a successful implementation, proper planning and implementation need to be done.
- Also make sure that any shared information between the user and the chatbot in all the messaging channels is secure and protected as per the CCCP, GDPR, and other country-specific regulations.
- Making all the chatbot conversations relevant according to the customer’s needs can be a challenge in itself. As the user preferences change frequently, you need to keep updated with the trends often.