What Is A Key Differentiator of Conversational Artificial Intelligence (AI)?

Did you know that the conversational AI market is expected to grow at a staggering 17.3% CAGR between 2023 and 2033. The market is expected to grow from US$ 9.6 billion in 2023 to US$ 47.6 billion in 2033.

With evolving technology, the knowledge base of the users are expanding astonishingly. The need for powerful machinery comes into play to handle never ending stream of queries.

Traditional chatbots can respond to queries consistently. However, the varying nature of incoming queries, brings up the need for an effective technology to handle this surge. This is where the role of conversational AI becomes evident.

Although we are a part of this environment and using AI in our day-to-day life, there still lies a void when it comes to differentiating these two technologies – conversational AI and chatbots. .

In this article, we will explore the conversational AI in-depth and identify the key differentiators of conversational AI from traditional chatbots.

What Is A Key Differentiator of Conversational AI From Chatbots? 

In simple words, consider Conversational AI (CAI) to be a well trained equestrian horse. Just like the horse knows how to jump over different hurdles irrespective of their positioning and height, conversational AI solves all your queries irrespective of the nature and origin of the query.

On the other hand, consider chatbots as parrots who are trained to talk. This indicates that those parrots can only repeat the vocabulary they are trained. they cannot solve queries they are not trained to answer.

The key differentiator between conversational AI and chatbots is the new-age combination of Machine Learning (ML) and Natural Language Processing (NLP). Conversational AI is like a scene out of a science fiction movie that can understand the intent and respond to you in a natural tone. However chatbots are more like robots with a predefined work order and fixed replies.

In the upcoming sections we will explore about conversational AI in detail and analyze its working procedure and performance.  

What is a Conversational AI? 

Conversational AI (CAI) refers to the advanced technology that has the ability to respond to users in a natural and human-like tone. CAI put into use large volumes of existing data as well as machine learning for continuous learning. These sources provide the CAI with up-to-date information and data about the latest happenings.

Further with Natural Language Processing (NLP), the CAI is designed to understand the intent and tone of the conversation. this helps the AI to respond in a similar manner, thereby, making the conversation more human-like. CAI has the ability to understand voice input in multiple languages and convert the input into different languages. This ensures that the users interact with the system in a personalized manner.

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How Does Conversational AI work? 

Conversational AI is a combination of National Language Processing (NLP) and Machine Learning (ML). This starts with the input, the processing, and the output. Let us look at the breakdown of these steps.

1. Input – This is the query from the user. this could be in the form of text, speech, or just expressions.

2. Processing – this step is further divided into multiple step. The NLP is used to understand the query and the ML assists in understanding the intent and emotions related to the query.

3. Output – After the processing, the output is returned to the user.

These simple steps help in making CAI effectively respond to incoming queries. In simple words, CAI is like a real human being serving the purpose of solving user queries. With NLP and ML, the results from AI are more user friendly, thereby, making this system better than the traditional chatbots.

Key Components of Conversational AI 

Conversational AI is an viable technology, however, you need to understand its different components effectively in order to implement this technology efficiently. In this section, you will learn about the 8 key components of conversational AI that play a crucial role in the overall success of the system.

1. Define The Value Proposition

Primarily, you need to define how your users would interact with the system. It is necessary for you to gather data of past interactions and use the to understand the nature of interaction. In addition to the existing conversation, you should also invest time to prepare for scenario where roadblocks may happen.

2. Privacy And Security

It is essential to ensure that the privacy and security functionalities of the system stays up-to-date. Because conversational AI includes human-like interactions, there are high chances that users share sensitive information too. In such cases, security breaches or privacy malware can impact the overall trust of the users on the system.

You must ensure that your CAI fulfills all the necessary protocols to prevent security and privacy breaches.

3. Machine Learning (ML)

Conversational AI is effective only when there is a continuous learning methodology. The system should be able to grow and understand the increasing complexities of incoming queries. Additionally predictive results and understanding the intent will help in building an effective bond with the users.

By building a strong bond with the users, CAI will drive in more engagement resulting in the overall success of the system you are using it for.

4. Natural Language Processing (NLP)

CAI is not only trained for chat based conversation like a traditional chatbot. With input in the form of speech in multiple language you CAI must be capable to understand multiple languages. Further, the system should be able to respond in a similar language, tone, and intent in order to provide a complete human-like conversation.

Further speech-to-text and text-to-speech functionalities, should be effective and capable enough to convey the necessary information to the user in the format they would want to see.

5. Information Source

With a lot of conversations happening, you need to choose a reliable source for feeding information to your system. Ineffective and unreliable source will hamper a smooth conversation and result in broken conversation.

For queries that require a human output, it is advisable to have a human writer to address such scenario. This will help you fill any gaps arising in the conversation.

6. User interface

The design, ease of use, and the positioning of your conversational AI together contribute to higher usage of CAI. With the right platform and channel, users will engage more effectively with the system. The user interface plays a key role in providing a human like interaction and avoiding any kind of intrusions in the process.

Further, when having to pass this conversation to a human, you can introduce an effective design that will provide a seamless transfer experience.

7. Content

Just like the user interface, the content shared by a conversational AI plays key role in enhancing the overall engagement with the system. The conversational content should not only be informational but also provide a human-like response. By building an effective format for the conversation, you will ensure an enhanced engagement with your users.

You can have humans to share content for typical queries. This will provide users with a complete human-like interaction.  

8. Feedback loops

This is one of the most important part in making your system effective. Continuous feedback mechanism is essential to understand the present conversation factor as well as improve the conversations.

In addition to collecting feedback from the users, you can also iterate the downstream behavior to understand the performance of your CAI. This will help you improve the performance of your system and enhance retaining conversations.

Breaking Down The Key Differentiator of Conversational AI from Chatbots? 

In this section we will explore the four basic functionalities that are the key differentiator for conversational AI and chatbots.

1. Personality

Unlike chatbots, conversational AI do not respond to queries just like a robot. AI has the ability to respond to queries naturally. This provides an user-friendly interface between the users and the AI. This is possible because of the continuous learning process where the machinery is improvised regularly.

Conversational AI are also trained to respond with a sense of humor, personalized greetings, and the ability to respond with emotions to a certain extent.

2. Natural Language Understanding (NLU)

Consider conversational AI as a small child going to school. By incorporating NLU, the CAI gets the ability to understand process natural intent and its meaning. This is possible with the help of sentiment and emotion analysis, understanding the language and intent of the user, and keeping track of the history.

All these functionalities put together helps in making conversational AI powerful enough to provide solutions and queries that are close and natural enough to real life conversations.

3. Machine Learning (ML)

Just like a student learn new skills continuously, AI is also trained to learn new things continuously. Machine Learning ensures that the system is up-to-date with the latest technologies and is able to provide response effectively.

This continuous process of learning is possible with the help of advanced algorithms that analyze interaction pattern and the overall intent of the users. By providing valuable, helpful, and relevant responses the AI stays in trend with the quickly changing technologies.

4. Multimodality

Unlike chatbots that are only restricted to text responses, conversational AI comes in handy in different interaction channels. This includes voice, face recognition, and even touch based interaction to provide a natural feel to the interaction.

A simple example include understanding the facial expression of the user and tailoring the response accordingly. This increases the engagement ratio of users with the system.

Conversational AI vs. Traditional chatbots 

A quick recap into the comparison between CAI and chatbot.