What Is Conversational AI & How It Works? [2024 Guide]

As Amy Stapleton said, 

“We are entering a new world. The technologies of machine learning, speech recognition, and natural language understanding are reaching a nexus of capability. The end result is that we’ll soon have artificially intelligent assistants to help us in every aspect of our lives.” 

And this world is within our reach now. 

Think about it.

We already communicate with Siri, Google Assistant, Alexa, and chatbots on a daily basis. And Allied Market Research predicts that the conversational AI market will surpass $32 billion by 2030.

So, let’s prepare for this world by learning more about this technology.

In this article:

• What conversational AI is 
• Processes and components 
• How does it work?
• Examples and use cases 
• Benefits and challenges of conversational AI
• Guide to creating conversational AI
• Tips and best practices
• Best conversational AI platforms

If you’re interested in AI, check out these articles and in-depth studies on the topic:

• 10+ Essential AI Statistics You Need to Know
• AI Recruitment Statistics: What Is the Future of Hiring?
• Will AI Take Your Job? Fear of AI and AI Trends

So—

Let’s dive into the conversational AI definition for a good start.

What is conversational AI?

Conversational AI applies to the technology that lets chatbots and virtual assistants communicate with humans in a natural language. It also uses machine learning to collect data from interactions and improve the accuracy of responses over time.

Additionally, conversational AI apps use NLP (natural language processing) technology to interpret user input and understand the meaning of the written or spoken message.

Keep in mind that conversational AI technology doesn’t come in just one form. Some of the conversational AI categories include customer support, voice assistance, and the Internet of Things. 

So, is conversational AI synonymous with chatbots? 

Not exactly.

You can think of chatbots as a type of conversational AI. They typically appear in a chat widget interface and interact with users via text messages on a website, social media, and other communication channels.

On the other hand, conversational artificial intelligence covers a broader area of AI technologies that can simulate conversations with users. For example Lyro—our conversational chatbot is able to solve up to 70% of customer problems automatically with human-like AI conversations supported by NLP and machine learning.

Read more: Check out this in-depth comparison of chatbots vs conversational AI and discover all the differences. Also, make sure to learn more about what a chatbot is and how it works.

Now—what’s involved in conversational intelligence software?

Processes and components of conversational AI

Conversational AI systems combine NLP with machine learning technology to analyze and interpret user input, such as text or speech. Then, they extract meaningful information and respond in an appropriate way. 

This technology also learns through interactions to provide more relevant replies in the future. 

To understand the processes that go on in AI conversational platform, let’s get some of the definitions straight:

• NLP

Natural language processing is a method of analyzing language input. It processes unstructured data and translates it into information that machines can understand and produce an appropriate response to. NLP consists of two crucial parts—natural language understanding and natural language generation.

• NLU

Natural language understanding is responsible for making sense of the language data input. It brings out the context, intents, and structure of the information to determine the meaning of the input.

• NLG

Natural language generation produces language readable to humans based on the input provided. It modifies the structured data in order for humans to understand its meaning. 

• ML

Machine learning is a set of algorithms and data sets that learn from the input provided over time. It improves the responses and recognition of patterns with experiences to make better predictions in the future. 

• Input generation

This is the input that a user provides to the AI conversational software. This can be in the form of text or voice input.

• Input analysis

This is the process of analyzing the input with the use of NLU and automated speech recognition (ASR) to identify the meaning of the language data and find the intent of the query. 

• Dialogue management

In this process, NLG, and machine learning work together to formulate an accurate response to the user’s input.

These components and processes enable conversational intelligence software to untangle data into a readable format and analyze it to generate a response.

It sounds great, doesn’t it? But how do these processes actually work together?

How does conversational AI work?

In simple terms—artificial intelligence takes in human language, and turns it into a data that machines can understand. Then it creates a relevant response to the user. But there’s actually more going on behind the scenes than you might think.

Conversational artificial intelligence platforms usually work in three main stages after a user inputs the query into the chat:

1. Natural language processing is used to break down the query, identify the sentiment, and restructure the sentence to make it easier to understand.
2. Then, deep learning, and natural language understanding pick out the intent of the request and take any relevant information from the query. In this stage of the process, the software understands the user’s input. 
3. Last, but not least, in the third step, the conversational intelligence software formulates a response to the user. It uses the conversation flow to respond appropriately to the question or perform the right action. With each inquiry, the conversational application learns and becomes smarter. So, with time, it can answer more and more complex questions.

And what can conversational AI platforms be used for? 

Let’s find out!

Conversational AI use cases and examples

What do you think of when you hear of AI applications? 

Customer service chatbots and voice assistants might come to mind. You’ve seen them and you’ve probably used them. So, let’s discover more about them. 

Here are the most popular examples of conversational AI use cases.

Customer service

Conversational assistants help human agents with online customer service and become virtual shopping assistants for shoppers. They answer FAQs, provide personalized recommendations, and upsell products across multiple channels including your website and Facebook Messenger. 

Sephora’s virtual assistant is an example of conversational AI in retail used well:

Hospitality

Just as in retail, conversational AI hospitality can help restaurants and hotels ease their order processes and increase the efficiency of service. 

Let’s say that your restaurant is offering a takeaway service. Instead of taking orders on the phone, you can add a chatbot to your website and social media that will do it automatically. It can show your menu to the client, take their order, ask for the address, and even give them an estimated time of delivery. 

In fact, our study into the future of chatbots shows that 59% of customers prefer to use chatbots for ordering food. 

Human Resources

You might be wondering how conversational AI can help in the HR department. It’s all about human contact and interactions, isn’t it? 

Well—yes, but AI can help candidates to get all the information they need straight away and update them on the hiring process. Also, it can automate your internal feedback collection, so you know exactly what’s going on in your company. Conversational AI platforms can also help to optimize employee training, onboarding and even provide AI coaching for continuous development.

Healthcare

Conversational AI healthcare apps can be used for checking symptoms, scheduling appointments, and reminding you to take medication. It makes the healthcare system more accessible for all patients. That’s because anyone can get accurate help right when they need it, no matter where they are or how busy their doctor is.

It can also improve the administrative processes and the efficiency of operations. It collects relevant data from the patients throughout their interactions and saves it to the system automatically. This way, the doctor gets a fuller picture of the patient’s health conditions. The power of using generative AI for healthcare advancements is already obvious, and is arguably an area in which the most focus is needed to reap long term rewards for patients and practitioners.

IoT devices

Chances are you have a mobile phone, a smartwatch, or Alexa in your home. These devices use sensors that connect with each other to process and exchange information. 

This conversational AI technology also uses speech recognition that allows your smart home assistant to perform tasks, such as turning off the lights and setting your morning alarm. 

Some of the most popular IoT applications used at home include Siri (Apple), Alexa (Amazon), and Google Home. 

Research shows that the Internet of Things has been getting more and more attention lately. In fact, worldwide spending on the IoT increased by $63 billion between 2019 and 2020. 

Read more: Learn more about the Internet of Things including what it is, how it works, and some examples.

All of these tools can help to free up your time and make your life that little bit easier. 

They’re able to replicate human-like interactions, increase customer satisfaction, and improve user experiences. 

Now—

What other advantages conversational assistants can bring you, business-wise?

Benefits and challenges of conversational AI

Gartner research forecasted that conversational AI will reduce contact center labor costs by $80 billion in 2026.

So, apart from saving money, what are the main benefits of conversational AI solutions?

It saves agents’ time and reduces waiting times

It’s essential for your business to answer customers quickly and efficiently. Especially since more than 55% of retail customers aren’t willing to wait more than 10 minutes for the customer service agent’s answer.

But don’t make your representatives fly through the requests, as they won’t provide a thorough enough customer service experience. To keep your shoppers’ satisfaction levels high and speed up the response time, your business should make use of conversational AI companies. 

Chatbots can take care of simple issues and only involve human agents when the request is too complex for them to handle. This is a great way to decrease your support queues and keep satisfaction levels high. 

It enables 24/7 support

It’s important to be available to your customers around the clock, seven days a week. You never know when they’ll come across trouble while browsing your ecommerce website. But paying overtime and employing more agents would be very costly.

Instead, use conversational AI software when your support team isn’t available. It can resolve common customer issues and let them know when live agents are available to answer more complex queries. It’s a win-win situation as your shoppers feel looked-after, and you can gain more clients in the process.

Read more: Discover what financial services can use conversational AI in banking with its benefits and best platforms.

It provides personalized recommendations

Marketing is another area where AI brings great results. During an artificial intelligence conversation with a client, the software can make personalized recommendations, upsell products, and show off current deals. These suggestions can lead to a boost in sales and increased lifetime value of each customer.

In fact, according to Google, shoppers are 40% more likely to spend more with a company that provides a highly personalized shopping experience. 

Read more: Check out how to use chatbot marketing, what its benefits are, and learn some useful tips.

It serves customers in a variety of languages

An underrated aspect of conversational AI is that it eliminates language barriers. Most chatbots and virtual agents come with language translation software. This allows them to detect, interpret, and generate almost any language proficiently. 

As a result, a multilingual chatbot makes your business more welcoming and accessible to a wider audience of potential customers.

It gathers valuable customer insights

After each chat, the conversational AI integration can ask your website visitors for their feedback, collect their data, and save the chat transcript. On top of that, research shows that about 77% of consumers view brands that ask for and accept feedback more favorably than those that don’t.

Customer feedback helps to identify what you should improve and what your shoppers’ needs are. This data can show you what device clients use to make a purchase, what age group they belong to, what products they’re interested in and much more. Whereas, saving the chat transcripts will enable you to analyze the conversations more closely. 

Okay—

These were the benefits, but let’s not forget that there are always two sides to the same coin. So, even though conversational intelligence has many advantages, it also has some challenges.

Challenges of conversational application

Everything has a less positive side to it and if you’re not careful, it could turn into an issue. But you can avoid troubles and client frustrations if you’re aware of the obstacles and work on them. 

So, let’s have a look at the main challenges of conversational artificial intelligence.

Human touch missing

No matter how advanced the technology is, it’s not able to sympathize with a person. It’s also difficult to keep up with all the changes that influence human communication, such as slang, emojis, and the way of speaking. These two aspects can make artificial intelligence feel a little too artificial, even with personalized chatbots becoming a thing.

Limited functionality

Although conversational AI can perform a variety of functions and tasks, it’s still limited to what it was programmed to do. So, there will come a time when the website visitor will need to be redirected from the chatbot to live chat. But as long as you offer both options, you should be on the safe side. 

AI training takes some time

Before you can make the most out of the system, you’ll need to train it well. This will require a lot of data and time to input into the software’s back-end, before it can even start to communicate with the user. The input includes previous conversations with users, possible scenarios, and more.

Security and privacy issues may arise

A variety of technological devices have been the target of hacking lately. You need to be aware of the dangers on the security level. So, if your application will be processing sensitive personal information, you need to make sure that it has strong security incorporated in the design. This will help you ensure the users’ privacy is respected, and all data is kept confidential. 

Here’s a comparison table for a quick view of both benefits and drawbacks. 

Conversational AI benefits	Conversational AI challenges
– Natural in communicating with clients – Available 24/7 – Provides personalized experiences – Collects customer insights and feedback – Available in multiple languages  – Saves time and money	– Lacks human touch – Limited number of questions it can answer – Training of the AI takes time – Security and privacy issues

As you can see, this technology can bring you many advantages, but you need to be willing to spend time on training and continuously improving it.

And let’s say you’re willing to spend that time on your conversational AI software. How can you make one for your business?

How to create conversational AI?

It all starts with thinking, planning, and analyzing—what do you need the system to do, what questions do you need it to answer, and how do you want it to interact with users? 

To do that, you need to first analyze the current customer interactions, their intents, and commonly asked questions. This will help your AI applications to efficiently route the visitor to the relevant information.

So, you can make conversational AI in four steps:

1. Identify your users’ frequently asked questions (FAQs) 

Start by going through the logs of your conversations and find the most common questions buyers ask. This is an essential foundation for your conversational AI processes. These inquiries determine the main intents and needs of your shoppers, which can then be served on autopilot.

As an example, these FAQs could include What are your opening hours? What are your pricing plans? What’s your return policy? questions, and many more. 

2. Design goals for your tool

Based on your FAQs, outline the main goals and intents of your users. 

You can create a number of conversational AI chatbots and teach them to serve each of the intents. But remember to include a variety of phrases that customers could use when asking for the specific type of information. 

Your support team can help you with that, as they know the phrases used by clients best. 

3. Use keywords that match the intent 

Make a list of nouns and entries matching the user intents that your conversational AI solution can fulfill. These help the software engineer make sense of the inquiry and give the best-suited response. 

4. Write out the responses

The goals, intents, and keywords will help the machine to identify what the visitor is asking about and provide relevant information. But to do that, you need to give your software the right responses. 

So, answer each of the intents in detail with the most relevant reply. Your conversational AI for customer service will use these pre-written answers when speaking to your users. 

But if you want to skip all that, you can use the newest conversational AI, Lyro by Tidio, which trains itself on the data from your website. Here’s a full tutorial on how to set it up:

Read more: Learn how Lyro compares to Zowie AI and pick the best system for your online brand.

OK—

What are some of the best practices you should keep in mind to get the most out of it?

Conversational AI: tips and best practices

First things first, conversational apps are not one of the technologies you can build and leave for them to “do their thing.” You need to continuously work on them and improve them to get the best results. 

And to use your AI tools most efficiently, you should optimize them for a variety of tasks, stay on top of your data, and continuously improve the software. 

Here are some tips on how to use your conversational systems for more than just FAQs. 

Optimize for marketing

The conversational AI platforms can be used for a number of tasks. So, don’t limit yourself to an area of customer service. Instead, use the software to market your products and boost sales. 

You can do this with product recommendations, offering time-sensitive deals, and saving carts by providing discounts. All in a natural and conversational way that your customers will appreciate.

Read more: Check out this case study to learn how a leather wallet company recreated the in-store shopping experience on their online store and increased sales using conversational AI.

Stay on top of results

Always check the results that you’re getting from the software. These include customer satisfaction, average waiting time, and the number of queries answered without involving your reps. 

Checking the data will help you quickly identify when something’s wrong and when you need to make improvements to your platform. This could include your checkout page not working, but also the chatbot’s answers needing improvements. 

Staying on top of your customer support metrics will also help you understand your shoppers’ needs better and act upon any changes right away.

Offer a mixed solution

Ensure that your visitors get an option to contact the live agents as well as your conversational AI. Some people prefer to speak to a human, while others like the automated service that can solve their issues within minutes. 

Keep in mind that AI is a great addition to your customer service reps, not a replacement for them.

Read more: Discover the essential differences between chatbots and live chat to choose the best option for your business. 

Continuously train your AI

Even if you’re using the best conversational AI on the market, you’ll still need to repeatedly train it. That’s because this software is not a set-it-and-forget-it type of tool. It won’t work properly if you don’t update it regularly and keep an eye on it. 

Artificial intelligence learns with experience, but it needs your help to stay relevant and appropriate to the user.

Read more: Learn how to train an AI with tips and a step-by-step guide.

Moving on—

Let’s have a look at the top conversational AI companies, shall we?

Conversational AI platforms

Insert the phrase “conversational AI” into G2, and you’ll get over 200 results. All of these companies claim to have innovative software that will help your business and your personal needs. But going through them all to separate wheat from the chaff would take days. 

Don’t worry, we’ve done it for you and chosen only the best of the best to include in our list. 

So, three of the best conversational AI are:

Lyro

Ratings: 4.7/5 ⭐️(1,400+ ratings)

Before you point fingers at us—yes, this is our software, but we won’t toot our own horn here. 

So, let’s keep it short and sweet.

Lyro is a conversational AI chatbot that helps you improve the customer experience on your site. It uses deep learning and natural language processing technology (NLP) to engage your shoppers better and generate more sales. This platform also trains itself on your FAQs and creates specific bots for a variety of intents.

Check out our reviews and try our system for yourself to decide if we deserve this spot on the list. 

Main features:

• Automatic segmentation of questions
• Feedback collection
• Self-learning mechanism
• Context and intent recognition
• Natural Language Processing (NLP) chatbots

Pricing:

• Free
• Email marketing ($10/mo) 
• Starter ($29/mo)
• Automation ($29/mo)  
• Growth ($59/mo)
• Lyro AI ($39/mo)
• Tidio + (starting from $499/mo)

Read more: Learn how to add Tidio AI chatbots to your website and how to use them.

Watson assistant IBM

Rating: 4.4/5 ⭐️ (255+ reviews)

This open-source conversational AI company enables developers to build chatbots for simple as well as complex interactions. It provides a cloud-based NLP service that combines structured data, like your customer databases, with unstructured data, like messages. 

This platform also takes security and privacy matters seriously with measures, such as visual recognition security and a private cloud for your users’ data. 

Main features:

• Customer sentiment analysis
• Custom categories classification
• Predictive analytics

Pricing:

• Free plan available
• Plus (from $140/mo)
• Enterprise → custom pricing

Zendesk

Rating: 4.3/5 ⭐️ (5,460+ reviews)

This is one of the best conversational AI that enables better organization of your systems with pre-chat surveys, ticket routing, and team collaboration. It’s one of the providers that offers a mobile app for real-time customer support, as well as monitoring and managing your chats on the go. 

Zendesk is also a great platform for scalability of your business with automated self-service available straight on your site, social media, and other channels.

Main features:

• Custom forms
• Ticketing system 
• Visual flow builder 

Pricing:

• Zendesk Sell Team ($19/mo/user)
• Zendesk Sell Growth ($49/mo/user)
• Suite Team ($49/mo/operator)
• Suite Growth ($79/mo/operator)
• Zendesk Sell Professional ($99/mo/user)
• Suite Professional ($99/mo/operator)
• Suite Enterprise ($150/mo/operator)

Conversational AI: summary

That’s all for today. Give yourself a minute to process it all, as we’ve learned quite a bit today. 

Let’s do a quick recap first, though.

Conversational AI is a software which can communicate with people in a natural language using NLP and machine learning. It helps businesses save time, enables multilingual 24/7 support, and offers omnichannel experiences. This technology also provides personalized recommendations to clients, and collects shoppers’ data.

To create a conversational AI, you should first identify your users’ commonly asked questions and design goals for your tool. Then ensure to use keywords that match the intent when training your artificial intelligence. Finally, write the responses to the questions that your software will use to communicate with users.

The best conversation AI platforms on the market are Tidio, IBM Watson Assistant, and Zendesk. 

Now—make the most out of this technology. People are developing it every day, so artificial intelligence can do more and more. Don’t get left behind! Jump on the train and get a competitive advantage over your competitors.

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