Conversational AI: How It Works, Why You Need It in Customer Service

• What is conversational AI?
• How conversational AI improves customer service
• What are the benefits of conversational AI?
• How does conversational AI work?
• Types of conversational AI
• How to create conversational AI
• Conversational Al use cases & examples
• Choosing the best conversational AI chatbot software for customer service
• Enhance your customer service operations with conversational AI

Conversational AI is a tool that uses natural language processing (NLP) and machine learning (ML) to make sense of human language and respond in a way we can understand. Customer service teams use it to enhance communications and provide around-the-clock support with a human touch.

Contact centers use conversational AI to analyze customer messages and figure out why people seek support. This information, called intent data, helps service teams strengthen their relationships with customers by anticipating needs and providing the right answers quickly. Conversational AI's learning algorithms teach themselves to automate elements of customer interactions, boosting productivity and creating better customer experiences .

Conversational AI improves customer service by providing instant, round-the-clock support — even at 3:00 a.m. on a major holiday. This technology, which includes chatbots, AI agents, and more, can handle a large volume of inquiries simultaneously, ensuring that routine customer queries are addressed promptly without delay. This immediacy and efficiency helps boost customer satisfaction (CSAT) as users receive quick responses and resolutions.

Conversational AI also can access vast amounts of data in real time, offering personalized interactions and solutions based on the customer's history and preferences. This level of personalization makes customers feel valued, further enhancing their experience.

In addition, conversational AI can free up live agents to handle more complex issues by taking care of routine inquiries and tasks. This not only optimizes the workflow within contact centers, but also improves the quality of service provided. Human agents can focus on issues that require critical thinking and empathy.

These improvements collectively enhance the customer experience, leading to higher customer satisfaction and loyalty.

Conversational AI vs. chatbots: The biggest differences

A conversational AI chatbot is much smarter than a conventional chatbot using predefined questions and answers. Legacy rules-based chatbots work fine with basic questions, with straightforward answers, like "what time do you close?" or "where are you located?" But rules-based chatbots can't handle the nuances of human conversation.

For example, 50 people might ask the same question 50 ways. This could stump a chatbot programmed to answer it with limited responses.

Conversational AI, however, uses machine learning models trained on massive databases of human conversations. The technology learns to interpret the intent behind questions and comments and provide highly customized responses.

Because conversational AI can handle a broad spectrum of queries, its ability to support customer service teams — and help customers — is much greater.

Generative AI vs. conversational AI

Generative AI (GenAI) is a subset of conversational AI that uses large language models (LLMs) to create text, images, sounds and other content automatically. The most popular generative AI applications are trained on vast language datasets that let users enter prompts into a simple chat interface. The app then delivers the prompt to LLMs that interprets the user's intent and generates a response.

Because generative AI can almost instantly answer complicated questions in a user's preferred language, it's a potential game changer for customer service operations. GenAI apps trained on a company's product database can help customers find exactly what they're looking for. And contact center agents can use GenAI to find in-depth information for common customer problems to resolve issues faster. Simply put, generative AI enhances conversational AI.

Besides 24/7 support and speed of service, here’s what else conversational AI can deliver:

• Feedback collection. AI-driven conversations provide service leaders with real-time data insights into what’s top of mind for their customers.
• Multilingual support: It can be programmed to support multiple languages, making it easier to assist customers from different linguistic backgrounds without language barriers.
• Personalization. Conversational AI can recognize customers, track their previous engagements and purchases, and tailor messages to their habits and preferences.
• Consistency: AI-driven conversations maintain a consistent tone and quality of service, ensuring that all customers receive the same level of attention and accurate information.
• Scalability: It can manage many conversations simultaneously, ensuring that customer service capacity doesn’t become a bottleneck during peak times.
• Omnichannel. Customer conversations can be integrated across multiple channels like social media and SMS, and touchpoints like smartphones, PCs, and in-store digital kiosks. This extends the technology's reach across a company's service, sales, and marketing channels.

Further, conversational AI technology isn't just for text chats. It also enables automated services that can help people navigate through interactive voice response systems. With conversational AI, interactive voice response (IVR) systems can ask callers more precise questions and do a much better job of answering their questions automatically or forwarding calls to expert support staff.

Conversational AI analyzes written or spoken statements and delivers replies designed to give users what they're looking for. Its two core phases are training and interpretation.

In the training phase, machine learning applications teach themselves by scanning billions of words, phrases, sentences, and even complete documents. This training data establishes what accurate or correct language looks like.

In the interpretation phase, the app's machine learning algorithms analyze users' statements or questions. By comparing the user's language to the correct language in the training data, the conversational AI app interprets what users want and tries to satisfy them.

A typical conversational AI interaction unfolds like this:

1. A user enters a conversational interface (app). They might compose a statement or question or respond to cues from the interface.
2. When they compose a statement, the app delivers it to a large language model trained to interpret the statement's meaning.
3. The large language model compares the statement to relevant training data. Pattern-matching algorithms use statistical models to create predictions of the user's intent.
4. The model delivers a response to the user.
5. Feedback mechanisms in the app help the model create more precise predictions.
6. Over time, conversational AI learns to improve its performance and accuracy.

The best conversational AI interfaces are carefully designed to understand how people will use the application and anticipate what they expect at key points in their conversations.

Most communication methods can be digitized and used to create conversational AI. Key variants include:

• AI chatbots: Natural language processing expands the capabilities of rules-based chatbots. While many AI chatbots are stand-alone, generative AI applications, top customer service software vendors are also embedding AI chatbots into their applications. This makes the software simpler for customers to use and easier for companies to support.
• Voice assistants: Language processing also allows devices and software to speak with users. This is especially helpful for hands-free access when driving a car or as an alternative to typing complex statements into tiny smartphone keypads.
• Virtual agents: AI software can memorize habits, predict future activities, and automate everyday tasks like setting up meetings or turning on a home security system. Conversational AI produces questions or statements that simplify users' lives.
• Interactive voice response systems: Conversational AI helps to transform interactive voice response systems by identifying why somebody is calling a help desk or customer service operation and solving their specific challenges.
• Multilingual translators: Learning algorithms translate interactions into a user's native or preferred language.
• Nonverbal translation: Innovations in image-recognition AI have led to new developments for camera apps. This technology can capture sign language and other nonverbal cues and match them with data from language models.

Customer service is among the most productive use cases for conversational AI. Let's say you sell audio equipment online. A customer comes to your website with questions about a new desktop stereo system.

Conversational AI could:

• Deploy a chatbot on your homepage or product pages to resolve the customer's issue
• Identify the customer as a native Spanish speaker and automatically translate the interaction
• Interpret the nature of her question and either answer it instantly or escalate it to a customer service rep
• Give your customer support staff in-depth information about the customer's previous purchases and likely questions
• Use generative AI to quickly narrow down the nature of the problem and provide a fast, accurate answer
• Use analytics data to identify interaction holdups and recommend solutions
• Compare the quality of this experience to similar ones. Managers can use this data to identify their top-performing customer service staff and find ways to improve the work of lower performers.

Creating conversational AI from scratch typically requires massive investments in computing hardware, software, security, and data science expertise. So, most companies choose a vendor specializing in conversational AI software. A vendor will help you:

• Create goals. You have many options for adding conversational AI to your customer service mix. So, you must start by defining your priorities and creating a plan for accomplishing them.
• Implement the right solution: You'll need a conversational AI interface that walks users through the experience and leaves them satisfied. An easy-to-use, point-and-click interface can let your team set up chatbots or other AI tools without any programming knowledge. This user-friendly option gives you the ability to adapt to customer data and business needs.
• Create workflows: AI conversations should be built in stages with a clear introduction, intermediate interactions, and conclusion. Each step must be implemented carefully to ensure everything flows together. This can prevent an early misstep from causing problems in the later stages.
• Test thoroughly: Analyze your workflows in a testing environment and try to work out as many bugs as you can before going live. You can't anticipate every possible bug, but you can build a feedback form into your chatbot workflow, so users can help you correct errors or omissions.

Customer service chatbot software should allow you to make the most of conversational AI. When shopping for a customer service chatbot with conversational AI capabilities, look for a solution with these features:

• Support for multiple languages across all your communication channels, so you can resolve a wider range of customer needs
• Templates that let your staff build their own AI chatbots. This means they don't have to wait weeks or months for expert technicians to build or update bots.
• Sophisticated knowledge management tools for training your staff and documenting processes
• Application programming interface (API) integrations that let you share data from software for marketing, customer relationship management, and other common business tools. This dramatically reduces your need to create software applications in-house.
• Secure cloud platforms that handle the high computing demands of AI processing, saving your staff from this cost and complexity.

Conversational AI uses smart technology that lets people and computers talk to each other. It's no substitute for genuine human interaction, but it can drive value for businesses and customers by:

• Accelerating customer service operations, easing the pressures on contact center staff
• Personalizing sales, service and support, for better customer experiences
• Explaining the motivations that drive consumer behavior
• Generating analytics data that helps you make better service decisions
• Integrating into your technology stack, adding insights across your operations

Decades ago, conversational AI was confined to fictional characters talking to robots and computer screens. But it's not science fiction anymore. It's here today — and it's helping to enhance service operations for companies and customers.