What Is A Key Differentiator of Conversational AI?

By Deon Nicholas
One pawn stands alone as a key differentiator of AI.

Conversational AI, artificial intelligence, chatbots, machine learning, natural language processing, natural language understanding…so many buzzwords that often don’t really mean much to those who are utilizing the tools they power. 

And when it comes to understanding the differences between each piece of tech, things get slightly trickier. Despite this, knowing what differentiates these tools from one another is key to understanding how they impact customer support. 

There’s a lot that separates these technologies.

In this blog, we’ll go over how conversational AI is a game changer for customer support organizations who are looking to scale and grow their capabilities for their customers. It allows for a much more powerful interaction between 

Looking to improve resolution times? Conversational AI can help.

How Does Conversational AI Work?

Conversational AI, or conversational Artificial Intelligence, is the technology that allows machines to have human-like conversational experiences with customers. It refers to the process that enables intelligent conversation between machines and people. 

In a chatbot interaction, you can think of conversational AI as the “brain” powering these interactions. As we mentioned, conversational AI encompasses a variety of technologies that work together to enable efficient, automated communication that is understanding of customer intent and sentiment by being able to decipher language and context, and respond in a human-like manner. 

Artificial intelligence for conversations, or conversational AI, typically consists of customer- or employee-facing chatbots that attempt a human conversation with a machine.

The Difference Between a Chatbot and Conversational AI

You may have heard that traditional chatbots and the chatbots of today are not the same.

And that’s because they aren’t.

With a traditional chatbot, you have a lot of manual setup to deal with. It may not be super clear when you’re deciding to implement one because support leaders assume that things can be up and running in no time—that’s not usually the case.

Implementing a traditional chatbot takes time—time that support teams just don’t have, time to learn from company and customer data, time to set up if/then decision trees based on customer data, and time to get them up and running in order to gain valuable insight from customers and actually assist them.

Chatbots of today, powered by conversational AI, work much more efficiently for support teams looking to launch and use a new tool that can transform experiences for their customers and agents.

The main differentiator between conversational AI and a traditional chatbot is that conversational AI chatbots utilize much more sophisticated technology within their programming

In order to have a better understanding of what powers conversational AI, let’s break down each of the pieces of technology that come together to make improved customer experience possible. 

Conversational AI Technologies

Machine Learning

Machine learning focuses on the development of computer programs that can access data and use it to learn. At its core, machine learning is key to processing and analyzing large data streams and determining what actionable insights are there. Within customer support this is an advantage for teams implementing AI tech since their data can be read and understood by the AI models which are utilizing machine learning within them. 

Natural Language Understanding

Natural language understanding, or NLU, is reading comprehension for machines. It is a type of natural language processing that uses the computing power of AI to comprehend text or speech as a human would. 

NLU works hand-in-hand with natural language processing and machine learning since machine learning allows for data processing while NLU is focused on comprehending what is being said and NLP facilitates the back and forth flow you’d have between a real agent and customer. 

Natural Language Processing 

Natural language processing is another technology that fuels artificial intelligence.

It involves programming computers to process massive volumes of language in data. It involves numerous tasks that break down natural language into smaller elements in order to understand the relationships between those elements and how they work together—kind of like having a puzzle to break apart and understanding how each piece fits together. 

NLP is concerned with how computers are programmed to process language and facilitate “natural” back-and-forth communication between computers and humans. 

Other key differentiators between chatbots and conversational AI for customer support include:

Implementation Timeline 

With a conversational AI tool, you end up transforming your customer experience in a much shorter time than a traditional chatbot. Because conversational AI uses a combination of tech to learn from your past data, it very quickly learns what customers are asking about and knows how to answer and assist agents in helping customers. Most newer support tools are also easier to launch and begin using because they offer industry insights into what customers are frequently seeking support for within those industries. 

Agent Assistance

The work of a support rep is hard. They have to know everything about a business, and we mean everything—from specific department processes to deep product knowledge, knowing it all is difficult. Conversational AI has the ability to assist agents in assisting customers by providing them with suggested answers when handling needs. 

Chatbots, on the other hand, are meant to sit on the frontend of a website and only assist customers in getting answers to the most frequently asked questions and concerns.  

Lack of Lift

Chatbots need to be constantly updated with new customer questions or issues. If a financial institution decides to change the way they allow customers to log in to their accounts online, they’re going to have to create and configure an entire new potential customer interaction. They’ll have to create new decision trees and update them with new information regularly. 

That’s not the case for conversational AI which is constantly learning from the data that customers and agents are giving it. Every time a customer asks a question a little differently than the last person but still means the same thing, the AI stores that information to be helpful in the next interaction. There’s no need to update anything when the tool you use is doing the updating for you. 

Why Companies Leverage Conversational AI For Customer Service

There’s so much that conversational AI can do for customer service teams. A virtual agent powered by more sophisticated tech than traditional chatbots understands customer intent and sentiment and can efficiently deflect incoming customer inquiries. 

With the help of an artificial assistant, companies can help their customers get answers faster, with much more consistent and accurate answers, resolve their needs on their own, and have an overall improved customer experience. 

It doesn’t just stop there. The right AI tool is also beneficial for your customer support agents. With AI, agents have access to centralized knowledge and can get suggested responses when helping customers. Agents want to be able to help customers and meet their needs, but they can’t when the chatbots who are supposed to help them actually just bog down their work and send angry customers to the actual agents. 

In addition to being beneficial to customers and support reps, conversational AI is actually helpful to businesses overall. Because this AI technology adapts and learns, companies can utilize it to gain insight into what’s working and what is not within their support organizations. 

AI & CX 

AI can improve a company’s customer experience. It’s helped businesses like Route, Typeform, and Kajabi change how their agents help customers and given them the best insight into where they can improve. 

Interested in learning more? Let’s chat. 

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