The Complete Guide to Conversational AI in 2022

AI has evolved over the years and is becoming more valuable every day. As a result, many companies are now investing in AI.

AI is being used by companies to improve the customer experience as well as the employees working in the company. Not only does AI reduce the time it takes for a customer to get help, especially when it comes to something that can be quickly sorted by AI, such as changing or canceling a booking, but AI can also save your employees valuable time that could be spent on other tasks.

The conversational AI market is expected to reach $18.6 billion by 2026. Not only is it growing rapidly, more than half of companies believe conversational AI is changing industries and believe their competitors are more likely to adopt such technology.

As you can see, conversational AI is becoming an important part of the marketing and customer service strategy of many companies.

It’s vital to master conversational AI and implement it in your business, so today we’re going to look at the complete guide to conversational AI in 2022.

What is Conversational AI

Conversational AI is like an updated version of a simple chatbot. It is used to send automatic messages and communicate between computers and humans. It’s still a chatbot, but it can have a more human conversation.

They can communicate like humans, understanding the meaning of sentences and then responding with text that mimics human text. The idea is to use these conversational chatbots to interact with customers and make them feel like they are talking to a real person.

This makes them feel more important and their experience is personalized.

The chatbot is also faster and can solve small problems that may take longer to answer and resolve.

Chatbots: who invented them?

ELIZA was the first registered chatbot in the history of computing in 1994. It was created by Joseph Weizenbaum of the Massachusetts Institute of Technology. This is where the term “talker” came from.

ELIZA worked by recognizing keywords or phrases from the input and then using those keywords to return a programmed response. Obviously, this means that ELIZA was not very personalized and often gave the same reaction to different phrases or sentences.

For example, if you mention your family, for example, “My father is a fisherman,” ELIZA will respond, “Tell me about your father.”

ELIZA recognizes the word “father” and has an automatic response associated with that word. Therefore, every time the word “father” or “dad” is written, he will offer the same answer.

Tell me what is the difference between conversational AI and a traditional chatbot.

Conversational AI is easy to confuse with a regular chatbot, but the differences are enough to tell them apart.

Conversational AI is at the core of what makes chatbots and virtual assistants work.

Conversational AI uses machine learning to analyze and understand what people write. From there, it can generate a response that matches the user’s handwriting.

Chatbots can use conversational AI, but many don’t. For example, basic chatbots typically use predefined responses or are programmed with rules rather than AI deciding what to respond.

Conversational AI is rule-free and chooses how to respond based on the context and intent of the user’s response.

A recent study suggests that the conversational AI market will reach $32 billion by 2030. Many endless companies are currently investing in it.

How does conversational AI work?

Conversational AI uses a framework of structures that can send individual outputs based on inputs.

Using machine learning, conversational AI can continue to learn and expand the range of queries it can answer or successfully answer. This is because each time the user talks to the AI, it can learn the context and purpose of the user’s answer, thereby learning new questions that may need the same answer.

It may seem simple at first, but machine learning is much more complex than questions and answers. Therefore, the correct structure of AI is critical.

These are some of the main components that make up natural language processing by spoken AI.

  • Machine learning (ML). Machine learning is a part of artificial intelligence based on constantly changing and improving algorithms and datasets. These algorithms learn from previous communications with humans by learning how a person responds to specific questions and answers and what the correct response to a human response is.
  • Automatic natural language processing (TAL). It is a language learning method that works with machine learning. It is currently in use, but with the advent of deep learning, most conversational AIs will switch to deep learning to help the AI ​​understand language better.
  • Analysis of contributions received. This is the part where the AI ​​parses the text sent by the user and parses it to determine the context and intent of the message.
  • Dialogue Management: After completing the NLP and parsing the input, the AI ​​should respond with an appropriate response. Conversation management is when the AI ​​decides which response is best to send to the user, using previous processes to select the response.
  • Reinforcement learning: Finally, the response of the user and the AI ​​is stored. Machine learning then parses the input and output and checks if they match correctly. From there, machine learning can check if the user’s intent and the AI’s response match, and better learn how to respond to the next similar input.

What is conversational AI used for?

Most people have already experienced some form of conversational AI and may not have even known they were talking to an AI and not a real person. Some chatbots are easy to spot, while others are not.

Customer service

There are many uses for conversational AI. For example, if you’ve ever spoken to customer support via messenger on their website, chances are it’s a chatbot. At this stage, it is regularly used for customer service, since FAQs are easily programmed as answers for a chatbot, as well as to manage bookings, schedules, and cancellations.

Information Service

Conversational AI can also be used to maintain desktop IT systems by helping with basic IT requests and fixes. Instead of forcing IT staff to deal with simple fixes all day, chatbots can help people who might have simple fixes and solutions. Chatbots can always send users to a real person if a problem cannot be solved.


Conversational AI can also be used to advertise and sell products. These bots can be configured to offer promotions or just sales and send them to the target audience. If you have a well-configured chatbot, it should be able to address a person by name and perhaps know some basic information about them.

These bots can force users to sign up for a subscription or go to your product page.

Data collection

Many companies forget that conversational AI can be used to collect data.

With countless interactions per day, your AI conversation program should be able to store all the information collected throughout the day and offer specific analytics about the day’s activities and messages.

  • Record all messages and calls from customers.
  • Make all conversations searchable to identify issues customers may be experiencing.
  • Track specific keywords related to the issue in all calls and messages and look for customer responses.
  • Collect important data such as call times, daily responses, and daily response results.

Examples of conversational AI across industries

Conversational AI is being used across many industries for a variety of purposes. Here are three examples of conversational AI being used across industries.

smart action

SmarAction is a scheduling automation software with built-in conversational AI that can understand booking requests, which we all know can be more complex than just specifying a date and time and booking them.

This AI has excellent natural language understanding and can handle any scheduling problems or queries the user may have.

Watson the Magician

IBM created Watson Assistant, and who better to create a conversational AI capable of supporting customer transactions?

This AI assistant can work in many industries, including fashion and healthcare.

It can answer simple questions, execute transactions, and contact agents when needed.

The study found that companies using Watson Assistant can reduce turnaround time by 10%, increasing customer satisfaction.


Cognigy is a great conversational AI tool that delivers efficient 24/7 customer service.

Cognigy is best used for customer service, maximizing the time it takes for a customer with questions to get the answers they need.

Many airlines use this software. This has become especially true since Covid, when airlines have faced numerous customer service issues due to cancellations and refunds. Here, artificial intelligence like Cognigy can be used to defer or refund eligible customers without contacting a customer service representative.

If you’re looking for other conversational AI tools, check out this list of the best conversational AI tools.


With so many uses for conversational AI, it’s no surprise that it is slowly taking over certain industries. Of course, this doesn’t mean you’ll never have to talk to a real person, but with simple tasks, conversational AI can speed things up when real people are too busy with other, more important things. .

Featured Image Credit: Photo by Andrea Piacquadio; pixels; Thank you!

The Complete Guide to Conversational AI in 2022

Shane Barker

Shane Barker has been a digital marketing consultant for 15 years and has been in influencer marketing for 5 years. He specializes in sales funnels, targeted traffic, and website conversions. He has advised Fortune 500 companies, digital influencers and a number of top celebrities.

Not all news on the site expresses the opinion of the site, but we convey this news automatically and translate them using software technologies on the site, and not from a human editor.

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