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Chatbots vs Conversational AI vs Virtual Assistants: What’s the Difference?

 Oh thank goodness, the robots are fighting among themselves. I was so worried they'd turn on us instead. Wait, what, they've teamed up in an unstoppable alliance? NOOOOooo!

Robot Android GIF by Squirrel Monkey

Recently, AI and ML have moved out of the "exciting, innovative tech" category into the "essential to keeping up with your competition" category. In the very near future, AI is likely to become a necessity. In fact, it's estimated that 95% of customer interactions will be powered by AI by 2025. 

So, businesses from all industries are trying to find ways of streamlining their processes, saving their teams time, and reducing human error through a conversational solution for their customer experience. 

Chatbots, conversation AI and virtual assistants tend to be bandied around under the same definition, i.e. a robot that can help customers with their issues. But each category has a difference in not only their primary functions, but their level of sophistication. So, let's get into some definitions, and then a comparison between the three. Put your bets on now on who wins the robot wars. 

Chatbots

Chatbots are simple-ish programmes which are used to automatically engage with customer messages.

They can be programmed to respond the same way every time, can vary on their messages depending on the customer's use of keywords, or can even use machine learning to adapt their responses to the situation.

You might have come across chatbots through mediums like a website chat window, social media messaging, or SMS text.

bot GIF by Product Hunt

Though some advanced bots might be powered by AI, most are programmed with these scripted responses, and built-out conversational trees, like the one below. 

Conversational AI

So, if chatbots are scripted, rule-based, and pre-determined, conversational AI is the opposite. 

Okay, I know that's vague. Basically, conversational AI relies on natural language processing and understanding, machine learning, deep learning, and predictive analytics to provide a user experience that doesn't follow a rigid structure. So thin; more dynamic, less constrained. 

The main aim of a conversational AI is to help a user complete a task. So, that might be as a form of support, marketing, sales, etc. etc. 

Conversational AI provides the chance for brands to feel more human, providing that authenticity that chatbots lack. 

The standard architecture of conversational AI includes:

  • An automatic speech recogniser
  • A spoken language understanding module
  • A dialog manager
  • A natural language generator 
  • A text-to-speech synthesiser. 

So, the automatic speech recogniser takes raw audio and text signals, and transcribes them into word hypotheses. These hypotheses are then transmitted to the spoken language understanding module.  The goal of this module is to capture the semantics and intent of the words spoken or typed. Then, the dialogue manager will interact with the users and assist them. 

Virtual Assistants

If you think virtual assistants, think voice commands.

Virtual assistants utilise natural language processing, like our friend conversational AI, in order to understand and perform tasks from the user. But unlike conversational AI, virtual assistants use their AI technology to respond to user requests and voice commands on devices such as smart speakers. You might have already come across a few - Alexa and Siri. 

Mr D Comedy GIF by CBC

Consumers use virtual assistants for a few different reasons, the most popular being to access information, consume content, and issue simple tasks like checking the weather.

In fact, 44% of users say that access to important information is the primary benefit of using a virtual assistant. 

"So what?" you might be asking. "What's all this got to do with my very important business, and its very important marketing campaigns?"

Well, users increasing comfort with voice commands will potentially shift how businesses engage with people online, especially through search. People issue a voice command to their assistant, and expect it to understand the context perfectly. 

“The biggest challenge is people would expect this kind of device to understand natural language and the context,” said Max Gladysh, co-founder of Botscrew, an AI development company in Lviv, Ukraine. “Consumers have much higher expectations when it comes to understanding the context.”

So, it's important for a virtual assistant to be able to understand the nuances in human speech. 

Plus, your company should aim to optimise its content for user intent. So, it'll need to be able to respond to these nuances people have when asking an 'out-loud' question. 

On top of this, virtual assistants exhibit their own personality, and are uniquely associated with an individual user. It'll retain information about this user, in order to give personalised and contextualised responses. This is pretty good news for companies such as Amazon, who will have a direct line to the data that Alexa collects. 

What are the differences between Chatbots, Conversational AI and Virtual Assistants?

1. Chatbots operate on a single-turn exchange. A lot of chatbots work on 'single-turn exchange', which means an independent question or request, which is then interpreted for its intent, which is then mapped onto a specific task. So, it might be "What's the tallest mountain in the world?" which is a phrase not left up to debate or nuance, unless you're really argumentative and want a go at it. 

But when someone asks something like "How long does it take to run a 5K?" they're trying to figure something out behind the question, i.e. what they need to do to achieve this goal. So, a conversational AI will engage the end user, and understand the nature of the problem behind the question. 

Season 1 Episode 102 GIF by Rick and Morty

2. Virtual Assistants and Conversational AI are more advanced than chatbots. We've had a quick look into how chatbots are less advanced. But why are they more advanced? Well, Virtual Assistants and Conversational AI are driven by the latest advances in cognitive computing; natural language processing, and natural language understanding. Virtual assistants use conversational AI and can engage in complex, multi topic conversations. 

3. Virtual Assistants drive productivity. Chatbots are known as "cold software programmes", which means they aren't able to read and interpret the context of user requests. They also can't adjust their responses. 

This is where conversational AI can step in, contextualising and customising interaction, which can pick up on negative tones and can switch to a sympathetic tone. This means you can provide a resolution to customer complaints, keeping users happy. So, while the robots are doing this, your teams can move their skills to more immediate and less mundane jobs. Plus, there's less chance of bot breaks, and a lighter load placed on Live Agents.

4. Virtual Assistants and Conversational AI continuously learn. Conversational AI solutions feed from a bunch of sources such as websites, databases, and APIs. When the source is updated or revised, the modifications are automatically applied to the AI. This is in comparison to the manual updates needed for chatbots. 

Plus, as conversational AI has access to this database, it can turn on a dime to fit the needs of the customer. 

6. Virtual Assistants can understand user queries more effectively.  Chatbots have a very limited ability to tackle the minute details of customer complaints, as they are restricted by their scripts. However, as mentioned above, conversational AI and, as a result, virtual assistants, have the ability to move beyond. 

As conversational AI has the ability to understand complex sentence structures, using slang terms and spelling errors, they can identify specific intents. Like we've mentioned before, this is particularly useful with virtual assistants and spoken requests. Also, conversational AI is equipped with a simulated emotional intelligence, so it can detect user sentiments, and assess the customer mood. This means it can make an informed decision on what are the best steps to take. This capability reduces false positives by 5%.

On top of this, conversational AI can remove any ambiguity around the query. So instead of bugging out and refusing the request, the AI can ask additional, relevant questions to get to the crux of the matter, just like a human counterpart would.