people and bot on iPhone
Oct 12, 2022
5 MIN READ

Why You Need Conversational AI That’s More Than a Chatbot

David Barry Headshot
David Barry

Chatbots have become ubiquitous on the Internet. According to VentureBeat, eight out of 10 people say they’ve used a chatbot in the past, and many say they would rather use a chatbot than browse a website to find what they’re looking for. 

But while chatbots provide valuable information, they have limitations. Because they are largely rule-based, scripted programs, chatbots are best suited for providing an interaction based solely on the most frequently asked questions. A ‘FAQ’ approach only supports very specific keywords being used. 

Conversational AI, or voice AI, on the other hand, absorbs customer feedback and learns in real-time, which can be applied to the same question at a different point of a client’s journey. This eliminates the need for users to repeat key information over and over. Through advancements in speech technology, such as natural language understanding (NLU) and Automatic Speech Recognition (ASR), voice AI can also understand complex queries and compound questions, providing a superior customer experience.

To provide a closer look at the differences between chatbots and conversational AI and describe how each offers strengths based on specific needs, we’ll explore these topics:  

The first appearance of chatbots

What is an AI chatbot?

What is conversational AI?

How chatbots differ from conversational AI

A better customer experience with conversational AI

Why are chatbots still so popular?


The first appearance of chatbots 

While chatbots first started appearing on modern websites roughly 10 years ago, the first chatbot was created in 1966 by an MIT computer scientist, Joseph Weizenbaum, in the form of ELIZA. This chatbot was based on a limited, predetermined flow. Eliza used pattern matching and substitution methodology that gave users an illusion of understanding on the part of the program but had no built-in framework for contextualizing events.


Chatbots have evolved over the years since ELIZA and now also incorporate artificial intelligence and are frequently used in situations in which simple interactions with only a limited range of responses are needed. This can include customer service and marketing applications, where the chatbots can provide answers to questions on topics such as products, services or company policies. Where basic chatbots show their limitations is if they receive a request that has not been previously defined; they will be unable to assist, and spit back a “Sorry, I don’t understand,” response.

Common Ways Consumers Use Chatbots

18% of consumers use chatbots to find business hours

17% want product information

16% are looking for a nearby business location

16% make a customer service request

12% request technical support 

What is an AI chatbot?

Artificial intelligence chatbots are chatbots trained to have human-like conversations using a process known as natural language processing (NLP). With NLP, the AI chatbot is able to interpret human language as it is written, which enables them to operate more or less on their own.

In other words, AI chatbot software can understand language outside of pre-programmed commands and provide a response based on existing data. This allows site visitors to lead the conversation, voicing their intent in their own words.

What is conversational AI?

Conversational AI, often used in reference to voice AI, uses a voice user interface (VUI)  to significantly improve interactions between machines, products, services centers, and people. When used in the context of voice AI, conversational AI is a combination of key voice technologies that enable digital voice assistants to understand natural human speech and respond in kind. Unlike robotic command and control voice interfaces, conversational voice assistants use a combination of Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU) technologies to understand speech in real-time and create meaning even as the user is talking.   

When implemented on an advanced voice AI platform, these natural-sounding voice interfaces are also context-aware. They remember previous parts of the conversation and eliminate the need for users to repeat key information over and over. Large content domain libraries equip conversational voice assistants with the knowledge they need to respond to natural speech accurately. Multilingual capabilities allow brands to deploy voice assistants for users across the globe.

The result is an incredibly human-like engagement. 

How chatbots differ from truly conversational AI

Traditional chatbots and more advanced AI chatbots are not the same as conversational AI in the voice AI context. Although chatbots are referred to as conversational AI solutions, they are not able to respond conversationally due to their text-only limitations and lack of advanced machine learning capabilities and reliance on if/then statements and decision trees. 

Conversational AI deployed via a voice assistant with machine learning capabilities offers more of the true AI experience, since it responds to a spoken query with a human-sounding voice.  With a chatbot, you’d have to be exact with your verbiage in order for the machine to provide the answer you’re searching for based on user inputs.

A better customer experience with conversational AI

Consider the scenario of a chatbot used by an e-commerce company. One of the most common questions customers will ask about is the status of their shipment. 

If you ask the chatbot, “Where is my package?” then you’ll get an exact answer depending on how the decision tree has been built out. But what if you say something like, “My package status shows delivered but I did not receive it. Was it lost or delivered to the wrong address?” You may run into the problem of the chatbot not having the skillset needed to understand this complex query, and responding to the user with a frustrating error response.

A better customer experience would be a chatbot that is powered by conversational AI that actually learns from the input being given and produces an answer based on analyzing the incoming customer queries and using contextual awareness. Voice AI will be able to understand the intent and sentiment behind customer queries by training on historical data and past customer tickets and won’t require human intervention. This form of a chatbot would understand what is being asked based on the sentiment of the message and not specific keywords that trigger a response.

With the proper AI tools, messages that don’t explicitly say, “Where is my package?” will still be understood to be asking the location of an item. This goes a long way for many scaling customer support teams and enables them to automatically deflect incoming customer queries with artificial intelligence while still maintaining high customer satisfaction.

table comparing conversational AI vs chatbot
For companies wishing to offer the best customer experience, conversational AI offers more human-like experiences than chatbots.

Why are chatbots still so popular?

One can easily look at the chart above and wonder how, despite the obvious benefits of conversational AI, chatbots are massively prevalent and utilized on a global scale with more than 300,000 individual chatbots deployed on Facebook, and a market size that is expected to grow from $2.6 billion in 2020 to $9.4 billion by 2024.

But it’s easy to see why. Chatbots were around long before voice AI. They are often cheaper to deploy. Also, many companies have not been aware of voice AI, don’t know how to implement it, or maybe are not convinced it’s the right solution.

The answer to this question is also rooted in the specific requirements of companies of varying sizes, sectors, and business models. Let’s say, for example, that you are the owner of a medium-sized apparel chain. You want to grow your business and ramp up your customer engagement efforts. You conclude that the missing piece of the puzzle is a solution that will assist your burgeoning clientele with tracking the status of their online purchases. This is one case where a chatbot is a perfect tool for the job. 

You buy a DIY chatbot builder or recruit a freelancer to design a predetermined conversational flow that will allow users to enter their order number, check its status, or request exchanges and refunds. Basic chatbot technology can move this kind of conversation forward via bot-prompted keywords or UX features like suggestion buttons. As long as the user doesn’t deviate from this exact task, there’s no real reason to invest in a generally more expensive conversational AI platform. 

But to provide customers with faster, more convenient, and personalized shopping experiences, some  retailers are deploying voice-enabled mobile apps and websites to help customers complete their retail transitions. Businesses in the retail space are adopting conversational AI to provide greater choice and filtering capabilities in the competitive e-commerce space and are looking for ways to include voice-enabled kiosks and other voice experiences for better in-store convenience and efficiency. Ultimately, these capabilities should drive up consumer satisfaction while helping to alleviate staffing churn.

What’s clear is that chatbots  have their place in environments where a company only requires a fairly defined and structured engagement with a customer. For those wanting a voice assistant that can make real-time decisions, answer follow-up questions with ease, and quickly transfer calls to appropriate live agent when the human touch is required, conversational AI is the best option. 

At SoundHound, we have all the tools and expertise needed to create custom voice assistants and a consistent brand voice. Explore SoundHound’s independent voice AI platform at SoundHound.com or speak with an expert or request a demo below.

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David Barry Headshot
David Barry is a high-tech storyteller with a passion for communicating complex ideas simply. He met his wife in a taxicab in San Francisco.

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