Article • 15 min read

What is conversational AI? How it works, examples, and more

Learn how conversational AI is transforming CX and how you can use it to benefit your business, agents, and customers.

作者: Contributing Writer David Galic

上次更新日期: July 24, 2024

A woman with red hair walks on the beach while using her phone.

What is conversational AI?

Conversational artificial intelligence (AI) is intelligent software that uses natural language processing (NLP), machine learning (ML), and other AI technologies to understand, process, and respond to human language. The term often describes the technology behind chatbot software or AI agents that interact with customers in a human-like way.

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 2024, 51 percent of consumers prefer interacting with bots when they want immediate service.

As artificial intelligence improves and becomes more common in our daily lives, businesses must 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 optimize it for customer experience (CX).

More in this guide:

Key components of conversational AI
Benefits of conversational AI
Types of conversational AI technology
Conversational AI examples and use cases
How to implement a conversational AI strategy
Conversational AI best practices
Frequently asked questions
Improve your CX with conversational AI for customer service

Key components of conversational AI

Conversational AI uses natural language processing, machine learning, and other AI 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 or conversation flow. Conversational AI software learns from each interaction to offer even smarter experiences over time.

However, it’s crucial to recognize that not all conversational AI is created equal. Only AI trained on billions of customer interactions can instantly discern customer needs. For instance, Zendesk’s proprietary, industry-leading AI models are specifically designed for CX to enhance human connections, delivering accurate, personalized support without any coding or engineering expertise needed.

How does conversational AI work?

Conversational AI works using a combination of a few principle technologies:

Machine learning, which is a type of technology that collects information from its interactions to learn and grow as time goes on
Natural Language Processing, which is artificial intelligence that can understand and respond to human language

NLP has two sub-components, Natural Language Understanding, which makes sense of a text and its intent, and Natural Language Generation (NLG), which converts text into a format humans can understand.

Essentially, this is the way an AI agent would work: a text input is fed into the conversational AI software, then the NLP deciphers what the user’s intent is and generates an appropriate response. Over time, the machine learning capabilities automatically improve the quality of the responses and make them more accurate.

Benefits of conversational AI

Conversational AI has many benefits for your business, like powering AI agents. Here are a few to consider.

Conversational AI benefits include increasing satisfaction, personalization, scalability, upselling and cross-selling, and lowering costs.

Increases customer satisfaction and engagement

Conversational AI can significantly boost customer satisfaction and engagement by offering 24/7 support for quick answers and problem-solving, which reduces frustration from wait times. Conversational bots can also use rich messaging types—like carousels, quick replies, and embedded apps—to enhance customer interactions.

For instance, since Accor Plus implemented Zendesk AI agents, the hospitality network has seen a 20 percent increase in customer satisfaction, a 352 percent increase in response time, and a 220 percent increase in resolution time. The bot provides around-the-clock support and offers customers self-service options outside regular business hours.

Helps support teams manage increasing support volumes

By deflecting simple and complex inquiries to AI agents, businesses can efficiently handle a high volume of customer requests without overwhelming human resources. Automating routine tasks and providing self-service support through conversational AI empowers support teams to focus on resolving more complex and engaging issues.

Take TaskRabbit. When the business expanded into more countries, its customer service volume surged by 60 percent, reaching 158,000 tickets per month. However, the support team effectively managed this increased demand by launching an AI agent with Zendesk. Now, the team is automating 100 percent of first contacts and deflecting 28 percent of tickets with their AI agent.

Provides more personalized experiences

Conversational AI can tailor interactions based on each customer’s account information, actions, behavior, and more. The more tools you connect to your AI agent, the more data it has for personalization.

Here are a few ways AI can improve the customer experience through personalization:

Recommending products based on order history
Tracking orders and providing personalized status updates
Providing personalized, immediate support

For example, Fútbol Emotion implemented a Zendesk AI agent that uses customer data to personalize the customer experience. Customer metadata is stored in the system, so the AI agent already knows who the customer is and can tailor responses accordingly.

Improves agent efficiency and reduces operating costs

In addition to handling routine tasks—like password resets and order tracking—chatbots can help agents improve customer support. Conversational AI will change the role of your human agents, but it won’t replace them. By automating routine and mundane tasks, AI agents enable your human staff to concentrate on higher-value projects, thereby enhancing their efficiency.

AI agents can also collect crucial customer context for agents and share those details before the interaction begins. This shift allows your team to focus on more strategic work and reduces support costs by allocating human agents to tasks that yield a higher return on investment. Implementing AI to deflect tickets helped the 3D development platform Unity save $1.3 million and improve its first response time by 83 percent.

Drives upsells and cross-sells

Conversational AI can increase revenue by leveraging customer data to personalize recommendations during interactions. These AI tools can analyze purchase history, browsing behavior, and keywords used in conversation to suggest relevant add-on products or upgrades that enhance the customer’s experience and increase the average order value. This targeted approach feels less like a sales pitch and more like a helpful suggestion, leading to a win-win for businesses and customers.

Types of conversational AI technology

Understanding the types of conversational AI technology can help companies pick the best conversational interface for their business.

Traditional chatbots

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 inquiries. Traditional chatbots are rules-based, using flowcharts that map out possible prompts and replies that can come up in interactions.

Generative AI bots

Generative AI enhances chatbots by enabling them to provide personalized responses based on user context, handle a wider range of queries, and offer more accurate and relevant information. Additionally, generative AI can continuously learn from interactions, improving its performance over time. This leads to a more efficient, responsive, and adaptive chatbot experience.

For example, Zendesk generative AI leverages OpenAI’s GPT-4o model. Generative replies make our bots more intuitive and 3 times faster at resolving issues by generating human-like answers from a business’ knowledge base. These improvements make every customer engagement more accurate and satisfying.

AI agents

Zendesk AI agents are the next generation of AI-powered bots. They’re trained on the highest quality CX data set, supported by data from over 18 billion CX-specific interactions. As a result, they have the intelligence to navigate the unpredictable twists and turns of customer conversations, answering more complex queries all on their own.

Voice assistants

These systems respond to voice commands and are embedded in various devices like smartphones, smart speakers, and cars. Popular examples include Amazon Alexa, Google Assistant, and Siri.

AI copilots

“Copilot” in AI refers to tools or systems that assist users by enhancing their capabilities, similar to how a copilot supports a pilot. These AI-driven copilots can help with tasks like generating content and providing suggestions based on contextual understanding. They leverage advanced AI technologies, including natural language processing and machine learning, to offer real-time support and improve efficiency and accuracy in various applications.

For instance, the Zendesk agent copilot suggests contextually relevant responses and actions based on customer interactions, which agents can then modify or execute, streamlining the ticket resolution process. As it learns from agent interactions, it gradually transitions to an autonomous mode, where it can independently resolve specific types of high-volume tickets, such as order cancellations or status updates, without the need for agent intervention.

Conversational AI examples and use cases

Businesses and consumers can use conversational AI for many purposes. Here are a few examples.

Customer service automation

Customer service teams can use AI agents in various ways. From greeting customers, providing around-the-clock support and 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.

For example, Upwork’s team of 300 support agents handles over 600,000 tickets each year with the help of Zendesk AI agents. AI agents provide proactive support and deflect tickets by offering customers self-service options, resulting in a 58 percent resolution rate for AI agents. This implementation has enabled Upwork to elevate both the customer and agent experience and improve its customer service overall.

HR and IT support automation

Conversational AI has numerous applications for employee support. For instance, a new hire can contact the HR help desk for information about health insurance options. The AI agent, equipped with access to your company’s benefits plan, explains the different plans and even provides personalized recommendations based on their situation.

You can also incorporate conversational AI into your IT help desk. If an employee faces an issue with their computer, the AI agent can walk them through troubleshooting steps to resolve the issue without involving the IT team. If the problem persists, the bot can route them to an available IT agent, informing the agent of what has already been tested.

Conversational commerce

Conversational AI in e-commerce can also improve the shopping experience and increase sales. For example, a customer browsing an online shoe store can interact with an AI agent through the page’s chat widget.

The AI can answer their questions about sizing and material, recommend similar styles based on their browsing history, and even offer applicable discount codes. All through a casual conversation, the AI helps them find the perfect pair of shoes, streamlining the shopping experience and potentially leading to a sale without involving a human agent.

How to implement a conversational AI strategy

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

A woman walks up steps alongside seven conversational AI strategy steps.

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, like automating customer experiences or deflecting employee service requests. 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. An AI agent that focuses on CX would be the best type of conversational AI to implement.

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

2. Use data to determine what to automate

Analyzing data allows you to make informed decisions about where conversational AI can offer the most value. Look for high-volume, repetitive questions or tasks that dominate support channels.

Tasks with high occurrence and clear answers are prime candidates for automation with conversational AI, ensuring a smooth transition and a positive experience for both customers and human agents. Some solutions, like Zendesk AI agents, can remove the guesswork by using your data to tell you what to automate.

3. Get support from stakeholders

The next step is securing support for the initiative. When pitching your idea to stakeholders, 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 AI’s positive impact on your agents. Spending less time on repetitive tasks increases agent productivity and employee satisfaction.
Getting a good return on investment (ROI): Decision-makers 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.

4. Determine your budget and resources

After deciding how you want to use conversational AI, consider how much money and resources your business can allocate. For businesses with a small dev team, no-code software is a great fit because it works right out of the box. Software requiring extensive development to match your business needs will demand additional budget and resources.

5. 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 software and other systems where customer data lives.

Additionally, ensure your AI agent integrates with all your digital support channels so it can seamlessly resolve customer requests across their preferred platforms and provide an excellent omnichannel customer experience.

6. Choose the right software

Not all conversational AI software is created equal. Be sure to consider how the AI is trained. For example, Zendesk AI is pre-trained on billions of real customer interactions, so it automatically understands what customers want from day one. You should also investigate implementation timelines to understand how quickly the AI can be deployed and any additional development costs involved.

Finally, make sure the software seamlessly integrates with all your existing systems. For instance, if the AI can’t access your knowledge base or other key business systems, its effectiveness will be severely limited.

Businesses can choose a conversational AI solution that delivers long-term value by prioritizing these factors.

7. Look at data to measure performance

Collect data and customer feedback to evaluate how your conversational AI is performing. For example, quality assurance tools can evaluate interactions between AI agents and customers and monitor for negative sentiment. AI agents can also automatically send CSAT surveys after each interaction. This will show you what customers like about AI interactions and help you determine how to optimize your conversational AI strategy.

Conversational AI best practices

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

Be transparent with customers: Some people have difficulty telling the difference between human agents and AI agents. Telling consumers from the start when they’re interacting with AI can instill trust in your company.
Create an easy handoff from AI agent to human agent: Make it easy for the customer to connect with a live agent when a conversation requires a human touch. AI agents can also pass along information the customer has already provided, such as their name and issue type.
Meet the customer on their preferred channels: As customers connect with you over their favorite communication channels, it’s important to have an AI agent to meet them where they are, like social platforms and messaging apps.
Match your AI agent’s personality to your brand’s tone: An agent might be the first interaction a customer has with your brand, so you want it to promote a consistent experience.
Partner with a trusted AI provider: Aside from offering accurate and seamless support, the AI tool also needs to safeguard sensitive information and comply with customer data privacy regulations.

AI is constantly evolving, so in addition to the best practices above, you’ll need to stay current on the latest AI advancements to deliver excellent customer service. Set a healthy budget for AI investments to keep up with your competitors.

Frequently asked questions

What is the difference between chatbots and conversational AI?

How is conversational AI trained?

Conversational AI is trained on massive amounts of text and speech data, like real-world customer service interactions or dialogue scripts. This data helps the AI understand the nuances of human language, including slang, sarcasm, and different conversational styles. Exposure to more data enhances the AI’s understanding and ability to respond to natural language effectively.

Zendesk AI agents, for instance, are pre-trained on the world’s largest CX data set, drawing insights from billions of real-life customer interactions. This training helps them understand user intentions and craft responses more effectively than tools that must learn on their own.

What is the difference between conversational AI and generative AI?

Conversational AI focuses on simulating human-like conversations through interactive dialogue, while generative AI creates new content, such as text, images, or music, based on patterns learned from data.

Generative AI can enhance conversational AI. For example, a generative AI bot could find an answer to a customer’s question in your knowledge base and respond with a summary of the article—that hasn’t been pre-scripted.

What are some common challenges of conversational AI?

While conversational AI offers exciting possibilities, businesses can face hurdles in implementation. Limited developer resources can make building and training AI in-house challenging and expensive. Lead times for developing and deploying a robust conversational AI solution can be lengthy. Additionally, generic conversational AI might not be specifically designed for the nuances of CX, potentially inhibiting customer interactions.

Zendesk AI agents, on the other hand, resolve all these issues. They are ready out of the box and purpose-built for CX, so you can start using them immediately. And because the AI tools are easy to use, your team can independently make adjustments without roping in developers.

What are some industry applications for conversational AI?

Conversational AI is finding applications across a wide range of industries, including:

Customer service: AI in customer service can answer frequently asked questions, troubleshoot problems, and handle basic transactions.
Retail: AI agents can recommend products, answer sizing inquiries, and personalize shopping experiences.
Healthcare: Virtual assistants can schedule appointments and answer basic medical questions.
Finance: Conversational AI can handle routine banking tasks like checking balances and transferring funds.

Improve your CX with conversational AI for customer service

Not only does conversational AI technology allow for fast and personalized customer service experiences, it also enables agents to be more productive, and your business to scale intelligently.

Zendesk AI agents are the most autonomous bots in the industry, knowing how to solve all sorts of interactions—even the most complex. Unlike other solutions, our AI agents are experts in customer service and purpose-built to enhance human connection. They also work well with human agents. Our AI agents set up fast with no technical expertise required, so you can get started from day one.

See how Zendesk AI agents can help you impress your customers.