Conversational AI is an area of artificial intelligence that can simulate human conversation.

It is enabled through Natural Language Processing that is focused on improving how compute systems understand and process principal human languages.

Other than Natural Language Processing, Conversational AI is enabled by foundation models and machine learning.

Conversational AI systems are trained on large amounts of data, such as text and speech. This data training helps the programmed systems to understand and process human language.

Integrated Conversational AI systems use this training data and programming models to interact with humans in a natural way.

The ability to respond in a human-like manner keeps improving over time, since the system keeps learning from positive and negative customer responses.

For an enterprise that invests in customer services, Conversational AI can reduce costs, increase productivity and efficiency.

By automating responses through trained virtual agents, enterprises can respond round the clock in a predictable manner without errors.

How it works?

The starting point for any engagement in conversational AI is Natural Language Processing. Natural Language Processing manages the
language grammar of the back-and-forth responses.

It corrects spellings, identifies synonyms, and interprets the grammar being used. The most important role is to break down the interprets the grammar being used. The most important role is to break down the

conversation into words and sentences that can be understood by the resident learning that it has built up to date.

Once the Natural Language Processing step has been executed the next step is Deep Learning.

This is where Machine Learning models start working on the conversation and this is known as Natural Language Understanding.

While Natural Language Processing worked on the grammar and correctness of the language, Natural Language Understanding looks at the topic, context and intent of the specific conversation.

It looks for triggers for specific information being requested that may require extraction from other sources.

The most important requirement here is to remove misunderstanding and this is usually the point of failure in Conversational AI.

Once the incoming conversation has been received, translated and understood, the third step in Conversational AI is preparing the response to be sent back to the customer.

The most important part of this stage is personalization and customization. This is where Conversational AI outperforms traditional chatbot solutions.

The response at this stage since it has been understood and personalized can be both complex and simple answers depending on what the customer is querying.

This is again where Conversational AI is many steps ahead of traditional chatbot assisted conversations.

Using this three stage process of Conversational AI, responses from such platforms begin to look increasingly like a human response rather than the typical machinelike, monotonic and rigid responses.

Enhancing the Learning

The key to success in Conversational AI is the ability to identify the right intent and requirement of the customer and provide the most suitable and personalized response for the customer.

While advancements in machine language, big data, structured and unstructured data frameworks, have helped to improve automated customer service engagements, a lot depends on training the data by a special breed of trainers.

In other words making the responses more humanlike, rather than machinelike. This requirement is cultivating a breed of AI trainers.

Customer service agents with years of experience are adept at understanding the requirements of customers and providing them with the most suitable response.

Now using data models linked to Conversational AI those years of experience can be used to train the data on how to respond.

By investing their years of experience to build the models of detecting the right context and mapping them to the right response, Conversational AI can reach its potential over time.

Selected and trusted members from the customer service teams are upskilled into AI and data trainer job roles.

They work alongside virtual agents and have the responsibility of increasing the scale of automated engagement with customers, boosting the engagement of customers into automated self-service areas, decreasing the workload on front line customer executives, and increasing customer delight.

Finally, and most importantly, since enterprises have elevated their own executives to drive the automation of customer services, they are now responsible for the engagement of their brand with the customer through a new automated service channel.

“For an enterprise that invests in customer services, Conversational AI can reduce costs, increase productivity and efficiency.”