We all know what AI is and how does AI works. But very few know what conversational AI is, what are conversational AI examples, and what are conversational AI solutions. 

In this beginner’s guide to conversational AI, I will answer all these questions and more.

Let’s first start with the definition. 

1. What is Conversational AI? 

Conversational AI helps you talk to AI!

Let me explain how!  

When we talk about Artificial Intelligence, CEOs, CTOs, CMOs, other professionals often end up thinking about advanced data handling with big data, generating valuable insights through analytics, and easing human workload through automation. 

Even when they think of smart devices, their application and capabilities would be pretty much bracketed within what's mentioned above. 

Conversational AI is an entirely different ball game. This AI is customer-facing, and it reaps benefits for your business. 

As the name, conversational AI suggests the kind of artificial intelligence you can talk to and which will also respond to you. In other words, you can talk to AI!

Chatbot on a website or social messaging app, voice assistants or voice-enabled device, etc., are all technologies using this conversational AI.

2. Conversational AI examples 

Some of the famous examples of software that uses conversational AI or conversational models are:

  • The voice assistants in your smartphones like Siri and Alexa 
  • Voice assistants in your smart cars 
  • The bot Joaquin Phoenix fell in love within the movie - Her

Knowing about conversational AI examples will help you understand how to do AI works -- in a better way. 

In a nutshell, 

Conversational AI is software that enables human and computer interactions. It is a technology that allows computers to identify multiple human languages by processing the words being said and also determine the best way to respond in the most human-like way possible.  

Now that you know what conversational AI exactly means, let us understand how it works: 

How does Conversational AI Work?

Wondering how a chatbot finds the right answers to questions asked? Or simply how does AI works? Or what do we mean when we say it helps you talk to AI?

Here's a look into all such technologies that enable conversational AI.

To start with, conversational AI uses natural language processing (NLP) and natural language understanding (NLU).

A good chunk of backend workflow and algorithms are needed to make the conversational AI work.

It all starts when a human submits a request. The AI solution then articulates the meaning of the human's words utilizing natural language understanding (NLU). 

So what does Natural language understanding (NLU) do rather than simply reading statements? 

It works to decipher meaning in the user's words, regardless of how it's stated. With advanced NLU, the AI will be able to know the user's intent even if the statements inserted by them are grammatical mistakes, incomplete, and idiosyncratic. 

Since they remember the context from one statement to another while in a conversation, they are able to comprehend what is said and even what is not throughout the conversation. 

This is way different from recognizing a keyword or phrase and answering a canned response scripted for that keyword.

Conversational AI doesn't stop at NLP and NLU.

After it has used NLP and NLU on humans' queries, it will use machine learning to find out the best way to respond. 

We all know the fundamentals of Machine learning -- it helps the AI application enhance its learning with every human interaction. So, responses to your prospects and customers will only get better with time. 

Finally, using natural language generation, computes the data it has gathered from the interaction into a well-written response that humans can easily make sense of.

3. The Elements of Conversational AI 

These are the main elements of conversational AI, and once you know them -- you will learn how does AI works for your business or if it will work for you in the first place. 

Objectives and Context 

It is essential to know the objectives before deploying conversational AI for your business. It would be a wrong practice to do so, just because it is hype! 

Having said that, conversational AI is extensively used in the customer support silo to enhance their customer experience.

Also, note that: Conversational AI is best for simple, straightforward tasks.

Here are the three different contexts wherein a conversational AI can help business:

  • Valuable metrics regarding the prospect and customer like Demographic data and Psychographic data
  • Their previous interactions with the business and purchase history
  • The type of activity done, their mood, and user intent.

With this, it should be clear that a conversational bot might excel at letting a regular customer find a product based on their past purchase through Facebook Messenger. But it would obviously mishandle an anonymous ethics violation report shared with it in a text message.

Even though this example is very much on-the-face, it is essential to understand the objectives in the context of using conversational AI for any business.

Privacy and Security

According to Capgemini Research Institute -- nearly 50% are concerned about the security and privacy with voice assistants. You can see the data below:

To mitigate this: 

As a business, you should establish guidelines and norms for the data and conversations that occur between the bot and the people. 

This is because the interactions on a conversational AI are casual and natural and laden with data that may often contain sensitive information.

There should be careful handling of data both from a technical and policy. At the same time, you should also know that you are using this valuable data to enhance customer experience.

The two ways conversational AI engages with the customer: 

Reactive engagement

Conversational AI uses natural language processing techniques, machine learning, and natural language generating techniques to respond to a customer's inquiry.

Using reactive engagement, businesses can offer customers an easy and clear path to get information and answers without them finding the need to connect with real-life agents.

It can be chatbots ready to assist a customer across their buying journey. Dynamic search bars that simplify scrawling through FAQs answers are always available to customers when they need it most.

Proactive engagement

Conversational AI, through machine learning, learns about a customer with every interaction. And then offers those things they have not anticipated.

For instance, a conversational AI using a chatbot can ask a host of questions to a customer to understand their days and interests and then automatically recommend them products that they might even know they were looking for but are quite likely to need.

Using proactive engagement, businesses can reach out to customers to keep them moving along the customer journey. 

More often than not, it is not viable for businesses to have customer support teams active for all customers around the clock.

In a proactive approach to conversational AI, a business can serve customers regardless: 

  • The channel visitors and customers connect with the business like social media chat messages, email, chatbot, live chat, or even text messages
  • The time of day 
  • The language they speak (one can use many languages in this technology)

And engage consumers with tailored and contextual information at scale to create opportunities and establish strong relationships. 

This will lead to more sales, and longer customer retention, and increased customer acquisition. 

Conversational AI solutions allow businesses to intervene at critical moments, like when a visitor toggles back and forth between two product options or hesitates at checkout. It enables companies to support their customers outside of regular business hours quickly.

An ideal customer engagement strategy puts to use both approaches in action.

Types of Conversational AI

Here are the two main ways to operate conversational AI:

Black Box AI and White AI 

When looking at NLP natural language processing, let us understand what is happening on the back end or how the conversational AI is learning to have these reactive and proactive conversations. 

1. Opaque AI or Black Box AI

Black Box AI or sometimes called Opaque AI, is associated with deep learning. In this method, potential outcomes are NOT given to the computer system ahead of time by programmers. 

Instead, some unstructured data is inserted into the computer by the admin user, and the computer reaches new conclusions on its own by using deep learning algorithms.

2. Transparent AI or White Box AI

The other approach is White Box AI, also known as Transparent AI. In this method, structured data and pre-set algorithms are inserted manually when needed so that the outputs will match a predefined set of results.

In other words, all possible outcomes are in control of humans and known ahead of time since they rely on human programmers to map inputs to the right output. 

Most businesses prefer this transparent AI because it helps them stay in control all the time. And when this happens: 

  • You are more likely to please enraged customers
  • Give exact customer information as per their need 
  • Protect your brand image

Now that you have gotten an overall idea of what's is conversational AI and how it works let's understand how different AI approaches can help a business. Another way to name these is conversational AI solutions: 

4. Approaches to Conversational AI 

Yes, we know that conversational AI helps you talk to AI, but is there more? Yes, there is: here are the leading conversational AI solutions that will help your business:

Lead generation

Lead generation is the most important aspect of every business. This conversational AI technology can crawl across a massive amount of visitor data to help you ease the process of identifying your ideal and most rewarding customers. This allows marketing teams to allocate their resources in the right direction and streamline their marketing endeavors effectively. 

Customer data analysis

Being data-driven is the only thing that will give you maximum leverage over your competitors. 

You can automatically garner key performance indices with conversational AI like purchasing patterns, interests, and customer habits. It can gather valuable information on the kinds of questions asked and the complexity of those questions, allowing you to upsell more.

Customer engagement

The time passed between the first time a user interacts with you appropriately and converts into a customer can help you directly measure your lead-generating endeavors. Chatbots using conversational AI can help you enhance your lead generating efficiency multi-fold. With the use of Conversational AI-powered chatbots, it is easy for you to engage with leads in real-time, reach out to unhappy customers, and offer them customized messages and tailored offers.

Email marketing

Digital marketers want to shoot personalized email marketing campaigns based on user behavior. And for this precise style of marketing, conversational AI is needed. Digital Marketers can implement rule-based AI technology to send emails that get triggered only if the customer carries out certain actions. Marketers can also personalize content, which can further help marketers improve their email campaigns and increase the results.

Social media outreach

Conversational AI-powered chatbots can also help businesses scale their social media efforts by creating tailored, personalized content, automating customer outreach, and answering customer queries. It can also simplify social listening by conducting sentiment analysis through monitoring new comments, messages, and likes and respond in real-time by asking questions about the audience's needs.

5. Future of Conversational AI 

As per the reports by Chatbots life, Chatbots have become "a hyped technology" (Most in-demand and sought-after technology is on the way to replace 99% of the apps in the next few years.) In other words, chatbots are the best conversational AI example. 

Speaking further, by the year 2022, chatbots will take over the customer support area generating a whopping revenue of more than $8 billion per year. 

Additionally, according to Infopulse, the banking sector has also embraced a steep success curve of 90% using conversational chatbots. 

As conversation AI evolves, more and more industries and businesses will adapt to it, and consumers will be greatly benefited from the chatbots and needless human resources.

However, there are specific challenges that conversational AI will continue to face.

  1. Understanding the user's needs and problems and recommending or resolving queries completely won't be possible by AI alone, and this is where human intervention will be needed.
  2. Conversational AI is customer-facing and will only be able to solve simple tasks. There is a lack of versatility here.

Conversational AI is still in its infancy stage, so it is hard to tell if these limitations will remain in the future and predict its exact potential. 


In conclusion, it can be said that conversational AI will play a vital role in enhancing overall business operations across industries. It will never be able to take humans' place and will always work in sync with us. 

So in the future, the new face of development will be humans and conversational artificial intelligence systems working together to better the world. It will never be a battle against humans and the conversational artificial intelligence system.