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Article 15 min read

What is conversational AI? The ultimate guide on how it works

Conversational AI is a set of technologies that can recognize and intelligently respond to speech and text inputs. Learn why conversational AI is the future of customer service.

By David Galic, Contributing Writer

Last updated March 23, 2024

We asked a conversational AI bot to write an intro to this blog post. Here’s what it wrote:

“If you think consumers are bot-resistant, think again. Customer interactions with automated chatbots are steadily increasing—and people are embracing it. According to the Zendesk Customer Experience Trends Report, 74 percent of consumers say that AI improves customer service efficiency. If your customers are satisfied with your service, your business’ bottom line will reflect it.

As artificial intelligence improves and becomes more common in our daily lives, businesses need to learn how to leverage conversational AI for customer service. Our guide will detail how conversational AI works, how it benefits customers and agents, when (and when not) to use it, and how to best optimize it for CX.”

Because of the strides conversational AI has made in recent years, you probably believed, without question, that a bot wrote that intro. It was, however, written by a human. That’s where we are with conversational AI technology, and it will only get better from here.

What is conversational AI?

Conversational artificial intelligence (AI) is a set of technologies that can recognize and respond to speech and text inputs. In customer service, the term describes using AI-based tools—like chatbot software or voice-based assistants—to interact with customers.

Messaging continues to grow as a preferred communication channel for customers, with social messaging apps like Facebook Messenger and WhatsApp Business accounts experiencing huge spikes in support requests. Messaging and conversational AI work hand-in-hand, and with the global conversational AI market expected to grow from $8.24 billion in 2022 to $32.51 billion by 2028, it’s no wonder that more businesses are implementing this technology.

What is a key differentiator of conversational AI?

The key differentiator of conversational AI is that it implements natural language understanding (NLU) and machine learning (ML) to hold human-like conversations with users.

  1. Natural language processing (NLP): Sometimes referred to as natural language understanding, NLP allows computers to comprehend speech and text so they can communicate with humans.

    NLP analyzes speech and writing patterns and tries to determine what a customer is saying to interpret their intent. It learns to account for incorrect grammar, typos, differences in intonation and syllable emphasis, accents, and so on.

    Once customer intent is clear, machine learning conversational AI technology forms a response.
  2. Machine learning: Machine learning is the process of machines (computers) using algorithms to parse data, learn from that data, and then apply what they’ve learned to deliver relevant answers.

    Every time these workflows occur, conversational AI technology becomes more sophisticated by identifying which responses provide customers with the best results.

Chatbots vs. conversational AI

The main difference between chatbots and conversational AI is conversational AI can recognize speech and text inputs and engage in human-like conversations. Chatbots are conversational AI, but their ability to be “conversational” varies depending on how they’re programmed. As mentioned above, conversational AI is a broader category encompassing all AI-driven communication technology.

For a chatbot to be considered conversational, it should:

  • Work seamlessly across a variety of channels, including web, mobile, and social apps
  • Foster smooth bot-to-agent handoffs, so customers don’t have to repeat themselves when the conversation transfers to a live agent
  • Ensure each interaction becomes part of a larger conversation that carries over a lifetime of interactions with the company

This is in contrast to siloed chats that start and stop each time a customer reaches out (or switches channels). Eliminating siloed chats results in a seamless experience for customers and agents alike.

How does conversational AI work?

Conversational AI uses natural language processing and machine learning technology to translate human conversations into a language that machines can understand and then form a reply based on information they take from a given knowledge base.

In an organization, the knowledge base is unique to the company, and the business’ conversational AI software learns from each interaction and adds the new information collected to the knowledge base. As a result, the technology continuously improves.

Challenges of conversational AI

Conversational AI still has limits in its ability to replicate a real human conversation and isn’t meant to fool someone into thinking they’re talking to a person. Your company must be upfront with customers about when they’re conversing with artificial intelligence versus a human. If the customer wants to talk to a human agent at any point, your business should make the handoff an easy transition.

Conversational AI should also use language that customers are comfortable with. The bot should create a natural and friendly experience and be programmed to speak in the same terminology as your customers.

Benefits of conversational AI

Why is conversational AI important? These five benefits top the list of what conversational AI can do for your business.

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Types of conversational AI technology

Understanding the types of conversational AI technology can help companies pick the best fit for their business and optimize it for success.

Types of conversational AI technology


Chatbots are computer programs designed to simulate human conversations. They help customers find quick answers around-the-clock or effectively route them to the best department to handle their inquiry. Chatbots are usually implemented through social media messengers or chat applications built into a website or mobile app. Here are some different types of chatbots:

  • Rule-based chatbots work like a flowchart, with humans mapping out conversations based on predefined rules. They’re dependable, they’re easy to program, and they integrate into your preferred customer support channel.

    However, rule-based chatbots lack AI, so they don’t allow for as much personalization or flexibility. They are typically used to automate processes, like answering simple FAQs.
  • Deep learning conversational AI chatbots can independently lead a conversation. NLP enables an AI bot to generate relevant answers by analyzing the conversation and trying to understand customer intent. Based on the intent, machine learning then formulates a response.

    AI chatbots are more challenging to set up than rule-based chatbots but are much more versatile and able to answer more complex queries. Ecommerce websites often use AI chatbots to understand the shopper’s intent when providing recommendations.
  • Hybrid chatbots—like Zendesk chatbots—are rule-based and AI-equipped. For example, a hybrid chatbot on a healthcare site might use AI technology to understand a patient’s issue and rule-based technology to offer medical instructions.

    Customer service providers can use hybrid chatbots to answer common questions and identify when to bring in an agent to help resolve an issue.

Voice assistants

Voice assistants are AI applications programmed to understand voice commands and complete tasks for the user based on those commands. Starting with speech recognition, human speech converts into machine-readable text, which voice assistants can process in the same way chatbots process data.

You will often find voice assistants in search engines, smart speakers, and operating systems. Alexa and Siri are common examples of this technology. The main advantages of voice assistants are that:

  • Customers can use them hands-free, which makes them popular options for people with disabilities
  • They can recognize a wide variety of languages, just like chatbots

Interactive voice response (IVR)

Interactive voice response (IVR) is an automated phone system tool that greets callers and provides them with menu options via voice or keypad inputs. Based on the caller’s responses, the IVR can:

  • Guide the caller through a series of prompts, ultimately routing them to the department or agent they’re trying to reach
  • Provide immediate answers to FAQs
  • Offer self-service options

IVR functions as a hybrid of chatbots and standard voice assistants, combining mapped-out conversations with a verbal interface.

Conversational AI examples and use cases

Businesses can use conversational AI for their chatbots, voice assistants, and IVRs. But what is an example of using conversational AI in a business setting? Here are a few use cases to consider, with additional info below:

  • Customer service
  • Sales and marketing
  • Data collection
  • Internet of Things (IoT)

Customer service

Customer service teams use chatbots in various ways. From greeting customers, offering around-the-clock support, providing self-service options, and offering personalized recommendations during the shopping experience, conversational AI software has proven to be a versatile tool for creating great customer experiences.

Upwork’s mighty team of 300 support agents handles over 600,000 tickets each year. How do they do it? With help from Zendesk, the company utilizes chatbots to offer proactive support and deflect tickets by offering customers self-service options—resulting in a 58 percent chatbot resolution rate. These implementations have taken both the customer and agent experience to the next level and improved Upwork’s overall customer service.

Sales and marketing

Businesses can use conversational AI software in their sales and marketing strategy to convert leads and drive sales. They can use it to provide a shopping experience for the customer that allows them to have a “virtual sales agent” that answers questions or provides recommendations. Zendesk chatbots can surface help center articles or answer FAQs about products in a customer’s cart to nudge the conversion, too.

Through data collected during interactions, chatbots can provide valuable information to help market products and services and identify customer trends and behaviors.

Data collection

Conversational AI bots can capture key customer information like their name, email address, order numbers, and previous questions or issues. They can even pass all this data to an agent during the handoff by automatically adding it to the open ticket. This provides the agent with the context of the inquiry, so the customer doesn’t need to repeat information.

Personalization stat

Agents can use this information to personalize interactions as well. According to our CX Trends Report, 59 percent of consumers believe businesses should use the data they collect about them to personalize their experiences.

Internet of Things (IoT) devices

Internet of Things (IoT) devices are the everyday devices people use that connect to the internet. Mobile phones, tablets, and smartwatches are all examples of IoT devices. They contain sensors that send real-time data to the agent when a customer reaches out about an issue.

IoT sensors can even be placed inside industrial equipment, machinery, or vehicles to collect performance data. AI then analyzes the information to find patterns and predict when a device might need maintenance. AI can then proactively send alerts to the user or customer support.

How to implement conversational AI

Learn how to implement conversational AI so you can start reaping the rewards:

  1. Establish your goals and use case
  2. Get support from stakeholders
  3. Determine your budget and resources
  4. Consider your existing infrastructure
  5. Choose and connect your CRM
  6. Look at data to measure performance

1. Establish your goals and use case

You won’t know if your conversational AI initiative is paying off unless you know what you want to gain by using the technology.

Do you want conversational AI for support, sales, or marketing? Be specific about your objectives and the problems you want to solve so you can gauge which conversational AI technology is best for your company.

For example, say your primary pain point is that your support agents are wasting time answering basic questions, and you want them available to handle complex customer inquiries. What types of conversational AI would best solve this issue? Perhaps it’s a combination of voice assistants that deliver automated answers to common questions and rule-based chatbots that can address FAQs.

Specify what customer service goals and key performance indicators (KPIs) you want to achieve before moving forward with implementation. That way, you can measure the success of your conversational AI strategy once it’s in place.

2. Get support from stakeholders

The next step is securing support for the initiative. When pitching your idea to stakeholders, make sure you closely align your arguments with top business objectives. Focus on the importance of:

  • Understanding customer needs: Demonstrate how conversational AI tools learn about customer needs, behaviors, and preferences—and explain how that will improve CX.

  • Improving agent satisfaction: Emphasize the positive impact AI can have on your agents. Spending less time on repetitive tasks increases both productivity and employee satisfaction.

  • Getting a good return on investment: Decision-makers will want clear ROI projections. Use resources like Dataiku and Nexocode to learn how to calculate, frame, and pitch the ROI metrics of AI projects.

The success of your conversational AI initiative hinges on the support it receives across your organization. According to Deloitte’s State of AI report, AI projects cannot succeed if company leaders aren’t setting core, overarching business strategies to achieve the vision.

3. Determine your budget and resources

After deciding how you’d like to use your chatbot, consider how much money and resources your business can allocate. For businesses with a small dev team, a no-code option would be a great fit because it works right out of the box. More complex use cases require additional budget and resources.

4. Consider your existing infrastructure

Next, investigate your current communication channels and existing infrastructure. Pick a conversational AI tool that can easily integrate with your current customer support or sales CRM. You’ll want the bot to work with the channels you already have and seamlessly step into current conversations for a great omnichannel experience.

5. Choose and connect your customer support or sales CRM

Determine any additional tools you may require. What conversational AI platform investments have you already made (if any)? Leverage any existing architecture to deliver value and reduce costs. Does it integrate with your current systems?

For example, if you already have a messenger app on your site, you can build a chatbot that can integrate with it instead of developing a similar tool from scratch. Remember to think ahead and consider the scalability of your infrastructure as you develop your strategy.

6. Look at data to measure performance

Collect data and customer feedback to evaluate how the bot is performing. The bot itself can capture customer information and analyze how individual responses perform across the entire conversation. This will show you what customers like about AI interactions, help you identify areas of improvement, or allow you to determine if the bot isn’t a good fit.

Conversational AI best practices

Follow these best practices to get the most out of your conversational AI.

Conversational AI best practices GIF

Create an easy handoff from bot to agent

When a conversation requires a human touch or the customer no longer wants to interact with AI, make it easy for the customer to connect with a live agent. The bot will also pass along information the customer already provided, such as their name and issue type.

“Agents get all that context right away, so they never have to ask customers to repeat themselves,” Lalonde says. “Everybody hates having to repeat themselves, but that goes away in a world where you have conversational AI embedded into the customer experience.”

Meet the customer on their preferred channels

As customers connect with you over their favorite communication channels, it’s important to have an AI chatbot to meet them where they are. Channels like social platforms, messaging apps, and ecommerce apps help welcome the customer and provide 24/7 service for a great customer experience.

Match your AI’s personality to your brand’s tone

Chatbots might be the first interaction a customer has with your brand. Tailor their persona to sync with your brand’s tone and to stay consistent across the board. Customers don’t need a comedy routine during their interaction, but they don’t want to talk to a toaster oven, either. As AI and bots become more natural and human-like, businesses can embrace these advances to create better conversational experiences.

Keep up with AI advances

Adaptability is a crucial element when incorporating technology into your business strategy. AI is constantly evolving—so the flexibility to pivot and quickly adapt must be built into your plans. Set a healthy budget for AI investments to keep up with your competitors. In our CX Trends Report, we found that 68 percent of business leaders already have plans to increase their investments in AI.

68% of business leaders already have plans to increase their investments in AI.

The future of customer experience is conversational AI

Toy robot

Seven out of 10 consumers now strongly agree that AI is good for society, while 66 percent give AI a thumbs up for making their lives easier. And 69 percent of customers say they’re willing to interact with a bot on simple issues—a 23 percent increase from the previous year.

Companies often view bots as a cost-saving measure, and they certainly can be. But ideally, conversational AI will enhance the capabilities of your support staff, not replace them. “Don’t think of chatbots as a substitute for humans,” Lalonde cautions. “One of the great things about using conversational AI for customer service is the beauty of having both bots and humans working together towards solving customer problems.”

Conversational AI makes it easier and faster for customers to get answers to simple questions. At the same time, support agents have fewer tickets to resolve, freeing them up to address the complex questions that chatbots and virtual assistants can’t handle. When companies combine the strengths of AI tools and humans, it leads to a better customer experience—and a better bottom line.

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