What Is Conversational AI? – Definition, Examples and Benefits

Customer preferences keep changing; without strong CX fundamentals and the right technology, businesses can quickly fall behind. 

To stay ahead, companies must include advanced technologies (such as artificial intelligence) to create exceptional CX strategies. But don’t just take our word for it—check out this illustration showing what CX leaders think about how tech advancements can actually enhance, not eliminate, the human touch in customer interactions.

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With the latest developments in artificial intelligence technologies, consumers are more open to communicating their concerns to bots. This is where conversational AI comes in. Conversational AI can help modern-day businesses gain a competitive edge, especially since 51% of customers prefer bot interactions over human ones when it comes to receiving immediate service.

In fact, in Nextiva’s 2025 report on customer experience, business executives and senior leaders from different sectors said that to keep up with customers’ expectations and beat the competition, it will be essential to focus on artificial generative intelligence-based tools. 

In this blog, we will discuss everything you need to know about conversational AI—including how it works, its benefits, examples, and more. But first, let’s define conversational AI.

What is Conversational AI?

Conversational AI refers to various technologies that mimic natural and interactive human communications using computer systems. 

It typically powers automated messaging services, such as text to speech online applications like the Google Voice Assistant and AI chatbots that power the AI customer service of companies.

Using the principles of contextual understanding and language generation, conversational AI models handle intricate customer interactions, adjust to user inputs, interpret different languages, and gain advanced conversational skills over time.

How Does Conversational AI Work?

As mentioned above, conversational AI works by integrating four main types of artificial intelligence technologies, namely natural language processing (NLP), natural language understanding (NLU), generative AI (GenAI), and machine learning (ML).

Here is an illustration of how it works:

Let’s delve deeper into the details and understand the components of conversational AI.

1. Natural Language Processing (NLP)

Natural language processing (NLP) is the overarching technology that allows machines to read, interpret, comprehend, and make sense of human conversations. It makes for the initial stage of processing user input in conversational AI. Here is an illustration showing the role of NLP in conversational AI:

NLP uses strategies like part-of-speech tagging, which labels words as nouns, verbs, and so on, and tokenization, which divides sentences into words or phrases. In this way, natural language processing (NLP) helps conversational AI tools process human language input and prepare it for additional analysis.

NLP also plays a crucial role in call transcription software, where spoken conversations are converted into text, enabling businesses to analyze and archive customer interactions more effectively.

2. Natural Language Understanding (NLU)

Natural language understanding (NLU) is a branch of NLP that is dedicated to deciphering the meaning and context of user input. Understanding the core intent of a user query involves much more than merely analyzing words. Here is an illustration depicting how NLU factors into conversational AI:

NLU includes tasks like sentiment analysis, which determines the user’s attitude or opinion, and entity identification, which identifies important items like names, locations, and dates. This aspect of natural language processing (NLP) is essential for determining how the system should react to a certain query or request.

3. Generative AI (GenAI)

Conversational AI uses generative AI to generate natural language, drawing from NLP and NLU to create coherent, contextually relevant responses that satisfy user intent.

GenAI moves beyond pre-written replies, generating real-time responses tailored to individual conversations. It enables all generative AI tools, chatbots, and voice assistants to mimic natural language patterns for smoother, more engaging interactions.

4. Machine Learning (ML)

Last but not least, machine learning algorithms enable conversational AI systems to learn from user interactions, training on large datasets to recognize user patterns and preferences.

With natural language understanding, this training helps AI predict optimal responses, improving its ability to handle complex queries. As interactions increase, the system becomes more effective at understanding and responding accurately.

So, is conversational AI worth your time and money? 

Let’s find out by diving deep into its benefits below.

Benefits of Conversational AI

Here are some of the benefits of using conversational AI models in your business:

1. Higher Adaptability and Greater Sophistication

AI-driven chatbots offer greater efficiency than traditional rule-based chatbots by handling large datasets, mimicking human-like responses, adapting to scenarios, and improving responses over time. They provide multilingual support and can automate a wider range of tasks, serving as intelligent virtual agents for customers.

2. Superior and More Personalized Experiences

Conversational AI tools enhance customer experience by delivering contextually accurate, human-like responses, resulting in quicker issue resolution. They personalize interactions using data from customer behavior and account activities, such as offering product recommendations and customized status updates. This improves response times and overall service quality, with 30% of US consumers rating AI chatbot interactions as “very effective.”

3. Increased Cost-Effectiveness

Did you know that conversational AI models have been found to reduce customer support costs by more than 30%? 

By automating tasks and handling numerous inquiries simultaneously, conversational AI optimizes agent productivity and ensures efficiency with 24/7 availability, minimizing reliance on large support staff and reducing costs through self-service options and streamlined interactions, thus ultimately offering significant financial savings across various industries.

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4. Deeper Insights into Customer Behaviors

Businesses can use conversational AI to gather and evaluate massive volumes of data in real time, which big data service providers can further use for customer experience measurement.

This can help companies extract quick insights upon which to make informed decisions. 

Conversational AI enhances customer engagement and optimizes strategies to align marketing and sales departments, fostering brand loyalty and driving revenue growth.

Also, integrating conversational AI with client management systems can further streamline communication and ensure that customer interactions are personalized, leading to stronger relationships and improved client retention.

Now that you have a comprehensive understanding of conversational AI, how it works, and what benefits it can bring to your business upon implementation, we would like to introduce you to some examples and use cases of conversational AI in the following section.

Conversational AI Technology Examples

If you are wondering whether you should invest in conversational AI technologies, it is imperative to know what tools are available on the market. 

Some examples of conversational AI technologies are given below.

1. AI-Powered Agents

AI-enabled chatbots and live chat software simulate human interactions, are trained on quality datasets, and can be integrated into websites, social media, and apps. They handle tasks like information delivery, enhancing customer engagement, and customer retention.

2. Generative AI Bots

AI customer service chatbots powered by GenAI handle diverse queries, deliver precise, personalized responses, and learn continuously to improve.

Using models like GPT-4, they provide human-like responses from a company’s knowledge base, enhancing accuracy and customer satisfaction.

3. Interactive Voice Assistants

Interactive voice assistants (IVAs) are AI systems that use natural language processing, for speech-to-text recognition to understand spoken commands. 

Examples include Amazon’s Alexa, Apple’s Siri, and Google Assistant, which help users with tasks like playing music, managing smart home devices, making calls, setting reminders, and accessing information.

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Conversational AI Use Cases

Following this, let’s dive into some popular use cases of conversational AI tools below.

1. Customer Service Automation

Conversational AI is a game-changer for global customer service teams. From greeting customers to providing round-the-clock support and self-service options and offering personalized recommendations during the shopping experience, conversational AI tools can be super useful in creating stellar customer experiences.

Here is an example of a conversational AI chatbot being used by Cookie-script, a SaaS, to help its customer service team attend to customer queries more simply and effectively.

2. Healthcare Support

In the healthcare sector, conversational AI can assist individuals in understanding their health issues and promptly connect them with the appropriate medical specialists. AI chatbots and voice assistants reduce wait times, prevent call drop-offs, and provide patients with relevant information, improving appointment scheduling and treatment guidance. 

Here is an example of a healthcare-focused conversational AI assistant developed by the World Health Organization (WHO). 

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Human Resources

Conversational AI can enhance HR processes by automating onboarding, payroll, benefits, and other tasks. These tools improve employee satisfaction, streamline operations, ensure data accuracy, and efficiently address FAQs for new and existing employees.

There are many types and use cases of generative AI, many of which might feel similar to systems and technologies you already know and implement in your organization. 

As an illustration, conversational AI has certain similarities with generative AI and traditional chatbots. To help you comprehend their differences, we have highlighted them separately in the following sections. Let’s get into it!

Conversational AI Vs. Generative AI

Conversational and generative AI sometimes overlap in their capabilities. However, remember that these AI technologies function in distinctly different ways. 

Conversational AI aims to enable ML and NLP-driven dialogues, allowing users to get information without waiting for a support representative. Unlike generative AI, it is trained on human dialogue to understand and process various input types, including text and voice.

On the other hand, Generative AI focuses on creating content like text, images, and videos. Used by marketing teams to generate collaterals such as ad visuals or social media captions, it learns patterns from neural networks to produce high-quality content.

Conversational AI Vs. Chatbots

While conversational AI may seem similar in function to chatbots, there are also differences between these two technologies.

Conversational AI uses NLP, machine learning, and sometimes voice recognition to process and respond to complex human inputs. It improves with interactions, providing accurate and relevant responses, powered by contextual understanding.

On the other hand, chatbots are simple programs that automate tasks like answering FAQs or guiding users through basic processes. They follow predefined rules and scripts to handle repetitive queries, and are thus, best for predictable tasks such as booking appointments.

Conversational AI: What’s Next in 2025?

Today, bot-based consumer interactions that satisfy user intent are not a stretch of the imagination, thanks to conversational AI.

“Voice assistants, chatbots, and other tools powered by conversational AI are enabling businesses across many industries to optimize their operations and offer digital customer service in unprecedented ways.”

[ – Ian Nicholls, Founder of AutomobilGarage]

In case you are wondering what the future of conversational AI holds, here are our predictions for 2025 and beyond:

Overall, the future of conversational AI is expected to be more strategic and results-driven, all while being more integral to different aspects of both personal and professional life, to drive more efficient, engaging, and personalized interactions. 

And that’s exactly what we do every day at Customerly. Would you like to take a look and see how we leverage conversational AI to transform our customer service?* Book a demo with Luca the Customerly CEO, and get your customer service automation up and running in just one week!

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