Conversational AI and Its Applications

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Conversational AI and Its Applications- Current and Future

What is conversational AI? Majority of the people have a preconceived notion about conversational AI that it is all about chatbots. But, it’s not the case when you delve deep into the subject matter.  Chatbots are software programs created by incorporating a certain set of questions and answers related to a particular topic.

The surge in the use of chatbots in various businesses leads to the misapprehension that virtual assistants and chatbots are the same. Hence, it is necessary to understand the differences between these two. In a webinar hosted by REVA Academy for Corporate Excellence, Dr. J. B. Simha gives an idea about these differences and how Conversational AI is going to progress in the years to come while conversing on the topic ‘Conversational AI and Its Applications’.

Evolution of Artificial Intelligence

There are mainly four layers of evolution in the artificial intelligence domain viz. imitation, data-driven systems, robotics, and artificial general intelligence.

Conversational AI comes in the second and third layer of the evolution, in which data-driven systems are created in the present era. However, artificial general intelligence has yet to be developed in which the machines will have the capacity to execute the tasks intellectually like a human being.

So, what is common among Siri, Cortana, Google Assistant and Amazon Alexa?

Majority of people think that they are conversational AI, but they’re not. We have yet to develop a system with a real-time conversational AI that can interact with the customers like a real-life human being.

Conversational AI – Going Beyond Chatbots

What is a chatbot?

Chatbot is not a conversational AI in a real sense, even though it can be related partially as conversational AI. In reality, a chatbot is a software application with certain predefined questions and answers. A chatbot is nothing but a rudimentary conversational AI and rule-based conversational AI as it can answer only the predefined questions.

There three levels of chatbots: FAQ bots, Virtual Personal Assistants and Virtual Customer/Employer Assistants.

FAQ bots are machines with response system for basic questions, which are also called as chatbots. The user needs to enter the right keywords to get a suitable response.  However, these systems are not considered as conversational AI.

Virtual assistants serve as simple dialogue machines but they are suitable only for context-based conversations. The more advanced Virtual Customer Assistants are used to serve a specific purpose and these systems can enhance customer experience with one-to-one context-based conversations.

So, what is conversational AI?

Conversational AI is 95% different from chatbots and this 95% has yet to be developed. Can we do that?

What We Expect From a Conversational AI

Everyone expects a certain maturity level of conversational AI, not a system with prebuilt responses. It has to respond based on the context and more proactive in terms of answers.

  • Conversational AI should engage in conversation just like a human being. It should make the conversation more engaging than humans. Aren’t the customer service representatives engaging? People who are working in customer service are definitely engaging, but only 40% of them are capable of engaging the customers and the remaining 60% are annoying the customers.
  • It should be asynchronous, which means that it should be able to engage the customers tirelessly. In short, the conversational AI should converse with the customers without any fatigue.
  • Cross-channel capability to understand the interactions happening in various social media channels
  • Conversational AI should be data-driven so that when it converses with human beings so that it can adapt the answers based on the questions asked.
  • It should know how to prompt others to get information by asking the right questions
  • Cost-efficiency of the conversational AI is crucial to replace a human representative
  • Adaptability is the most important factor for the conversational AI to stay updated.

Challenges of Conversational AI

Conversational AI is going to face several challenges, especially when it comes to the quality of interactions. Customers always expect to have an interactive conversation so that they can get customized answers for their questions. Here are the few challenges faced during the development of conversational AI systems:

  • Language barriers: Languages, accents, dialects, slang, and jargons will create a communication barrier between a customer and the system.
  • Simultaneous conversations: Conversational AI is not capable of handling several speakers at a time as it may not be able to identify who is speaking and on what context. Noise cancellation has to be taken care of while developing a conversational AI.
  • Emotions: It will not be able to handle emotions like a human assistant as it cannot identify various emotions such as tone, sarcasm, and anger in the voice of the customer.
  • Knowledgebase: The limited knowledgebase of conversational AI restricts itself from answering unscripted questions that may result in unplanned responses. It also cannot understand gibberish communication.

Conversational AI – Technology

The technologies that are using in developing conversational AI are Machine Learning, Natural Language Processing (NLP), Automatic Speech Recognition (ASR), and Advanced Dialog Management.

Applications of Conversational AI

Conversational AI applications are beneficial for industries such as healthcare, education, customer service, IoT maintenance, personal assistants, and entertainment.

Final Thoughts

Conversational AI is an area where more advancements have to happen. The problematic areas should be resolved with a lot of research and development. Once the researchers overcome the challenges, it can gain the trust of customers by enabling a real conversation with them.


Dr. J. B. Simha

Chief Mentor, RACE CTO, ABIBA Systems

Dr. Simha’s core competency is in R&D, business intelligence and analytics consulting. He has implemented several large scale systems for telecom, BFSI and manufacturing industries in business intelligence and analytics. He has been recognized as one of the ‘Top 10 Most Prominent Data Science Academicians in India: 2019’ by Analytics India Magazine.

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