Conversational AI Primer: Your 2024 Guide to the Basics, Applications, and Benefits

Conversational AI Primer: Your 2024 Guide to the Basics, Applications, and Benefits

January 17, 2024 | Convoso

AI. AI. It’s everywhere. You can’t escape it. And if you’re doing business, it’s probably on your mind, too.

With all the discussion of AI comes lofty talk about its capabilities: It’s a revolution, they say.  It’s going to unlock all kinds of breakthroughs in business and beyond. It’s going to change life as we know it.

Now, that’s all well and good. Great, even. But you’d be forgiven for wondering just what the heck a lot of this technology is that’s going to change the world—and conversational AI, in particular. 

In this primer on conversational AI, we breakdown the basics: the what, how, and why of conversational AI. Get up to speed on conversational AI (and some of the other buzzwords you’ve surely heard lately), then see how it might impact your own industry.

Making Real Sense of AI

First things first. Before we get to the main event, let’s get on the same page about AI itself.

Thanks to decades of sci-fi and pop cultural references, any mention of AI can easily conjure images of cyborgs walking the earth. (Or, maybe you can’t help but hear Arnold saying, “Hasta la vista, baby.”) 

Actual AI, though, is actually at once both much bigger than that and much simpler.

So, what exactly is AI? Here’s the best definition for our money, courtesy of Google DeepMind CEO, Demis Hassabis

“A.I. is the science of making machines smart.”

When you put it that way, it helps to cut through some of the haziness created by all the AI-related buzz out there. At its core, the field of AI is simply focused on making machines—the tools we humans use to make, do, and understand things—smarter and more effective. And naturally, that’s going to help make businesses more efficient.

Of course, the nitty-gritty of AI can get much more complex than that. (There’s a reason many of the field’s leading minds have one or more PhDs.) But if you’re trying to understand the basics of AI, and what conversational AI in particular is all about, that’s a great place to start. And it’s more than enough to begin understanding the potential uses and benefits of conversational AI.

What Is Conversational AI?

Conversational AI refers to a specific type or subset of AI technology that’s designed to understand, process, and respond to human language. 

In other words, the machines that we’re trying to make smarter in conversational AI are tasked specifically with language-related tasks. Specifically, the primary goal of conversational AI is to get computers to interact with language in a natural and contextually appropriate (i.e., conversational) manner. 

This whole conversational AI thing: it’s working. As tech has continued to advanced, it’s become more and more capable of handling complex conversation. It’s better and better at handling all the nuances of human language—sarcasm, humor, idioms, you name it. 

The primary goal of conversational AI is to get computers to interact with language in a natural and contextually appropriate (i.e., conversational) manner. 

And because conversational AI can possibly add value in virtually any field where conversations are key to day-to-day operations, potential conversational AI applications can almost seem limitless. But more on that in a moment.

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How Does Conversational AI Work?

You’ve probably seen some of conversational AI’s most common applications—in fact, you might already use it. Conversational AI is what powers voice assistants (think Alexa and Siri) and intelligent virtual agent (IVA) solutions. But how?

(Get ready for more acronyms.)

At the core of conversational AI systems are a few different capabilities: natural language processing (NLP), natural language understanding (NLU), and natural language generation (NLG) algorithms, as well as machine-learning (ML) processes. Let’s break those down quickly.

Machine Learning (ML) is actually a sub-field of AI. Essentially, it’s concerned with teaching computers to make decisions or predictions on their own. By feeding a computer lots and lots of data, and using algorithms to help it learn patterns from this data, computers get better at making accurate decisions and predictions.

In conversational AI, machine learning processes are applied to language:

  • With NLP, machine learning can help train computers to perform tasks like translation, sentiment analysis, or speech recognition.
  • Then, NLU will use ML to go further: the computer will be trained to understand the meaning and context of language, requiring more understanding of nuance, ambiguity, and other complexities.
  • This process will ultimately aid the computer in NLG, where a response is actually generated.

Perhaps the most exciting, most tangible part of conversational AI, natural language generation, or NLG, is where computers do the work of generating human-like text or speech. And if you’ve messed around with any of the latest tools, you’ll know that conversational AI is getting pretty good (spookily good) at this part of the process. If you haven’t, then, you’re in for a treat below when we discuss use cases of conversational AI.

What About ChatGPT? Generative AI vs. Conversational AI

Speaking of the latest tools…ChatGPT. OpenAI’s chat-based tool is one of the many Generative AI solutions that have made headlines since bursting onto the scene in late 2022. You might be wondering, is this conversational AI?

The answer: not quite. Or, perhaps, yes and no. ChatGPT and other large language models (LLMs) are typically classed as “Generative AI,” though it does naturally have some overlap with and utilize conversational AI capabilities. 

The difference between generative AI and conversational AI: Generative AI is focused on the generation portion of things—the creation of original, human-like content based on patterns in the data it’s been trained on. Meanwhile, conversational AI is focused on the complete interaction between human and computer. However, that’s not to say the two subsets of AI don’t have some overlap—or that they can’t work together.

Conversational AI vs. Chatbots

Speaking of similarities, the terms conversational AI and chatbots are often used interchangeably. However, a closer look shows why that shouldn’t be the case.

What’s the difference between conversational AI and chatbots? Well, for starters, conversational AI is a relatively broad term that refers to the technology that some, but not all, chatbots rely upon in order to simulate human conversations over text. 

While the most advanced chatbots leverage NLP and AI to deliver more life-like, complex interactions, many chatbots simply function based on a series of rules or a script. These bots, sometimes known as basic chatbots or decision-tree bots, are effective at answering common questions and solving regular issues. However, once prompts or queries exit the “safe zone” of previously scripted info, human intervention will be required.

Some, but not all, chatbots rely on conversational AI to simulate human conversations over text.

Conversational AI-driven solutions, on the other hand, are able to better understand intent and deliver helpful responses to more complex issues. And with the help of AI technology will only continue to learn from each conversation they have and improve their answers over time.

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There are already countless use cases of conversational AI—and more and more will emerge as the technology continues to improve. Here’s how a range of industries are already using, and could use, conversational AI solutions.

Conversational AI in Customer Service

If there’s an area where conversational AI applications have taken off and already begun to really change the way work gets done, it’s in customer service. Particularly in inbound settings, conversational AI solutions for customer service have helped organizations scale their support operations and deliver much-improved, and always-on, assistance.

Rather than direct customers through an interactive voice response (IVR) system with relatively limited possibilities, conversational AI can interpret a much broader array of customer input. From appointment-setting and order management to complaint resolution and billing support, solutions like IVAs are supplementing the work of service agents—and freeing up those agents to focus on the most complex cases. 

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Conversational AI in Sales

Likewise, sales and lead generation teams are increasingly counted among the early adopters enjoying the benefits of conversational AI applications. 

Sales operations deploying AI are, in effect, able to add more agents to their teams. Because whether it’s automating text-based outreach as part of an omnichannel strategy, qualifying prospects before transferring them to a live agent for conversion, or following up with sales leads as part of automated workflows, conversational AI greatly increases their capacity and efficiency, laying the foundation for scaling their operation.

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Conversational AI in Healthcare

Although AI-powered healthcare delivery might seem to some like a far-fetched scenario straight out of science fiction, conversational AI is already helping to make it a reality. 

In addition to helping automate administrative tasks behind the scenes, the latest AI technology is able to assist providers with triaging care. Taking information from patients over text or in person about their symptoms, conversational AI can deliver insights into health issues without patients needing to wait for a medical assistant. 

Outside of centers of care, conversational AI can help pharma companies and other research organizations collect data from patients and study participants—enabling them to cut costs and reduce the potential financial barriers to important study. 

Conversational AI in Retail

By now, you’ve likely run into chatbots during a variety of ecommerce experiences. Advances in conversational AI are expanding the use cases of these virtual assistants online, equipping them with the intelligence required to deliver personalized product recommendations, exclusive offers, and more. AI is also making it easier for shoppers to get what they need using a voice-activated device or a virtual assistant on their smartphone.

Conversational AI in Financial Services

Banking and finance businesses are also using conversational AI solutions to help customers get a better handle on their personal finances. In fact, AI assistants have steadily become an integral part of digital banking experiences. Bots are now not just helping with customer service, but providing personalized financial and investment advice, as well as accelerating payments and other previously manual banking processes.

Whether you’re looking to improve customer service or improve efficiency for your workforce, conversational AI has the potential to innovate in an increasingly wide variety of scenarios. To stay on top of the latest developments in AI and more game-changing technologies for sales, service, and marketing, stay tuned to Convoso’s omnichannel blog

And to learn more about how our conversational AI-powered, omnichannel IVA solution,, can help you drive more interactions and growth across channels, schedule a free demo of Convoso’s powerful cloud-based contact center software today.

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