What Is Conversational AI? + How It Works

From voice assistants like Google Home or Amazon’s Alexa to customer service chatbots, you’ve most likely experienced conversational artificial intelligence (AI) in action at some point in your life. While it’s fun and convenient for personal use, conversational AI has significant potential across industries.

Using machine learning, conversational AI mimics human-like conversations to interact with users in a natural and personalized way. This technology improves business performance and capabilities and transforms customer experiences (CX).

In this post, we dive into how conversational AI works, its benefits, and practical strategies for businesses to use this technology to enhance CX and streamline operations.

What Is Conversational AI?

Conversational AI is a technology that enables computers to understand, process, and conversationally respond to human language. This type of technology uses natural language processing (NLP) and machine learning (ML) to process text and voice inputs and learn from every interaction.

How Conversational AI Works

Using NLP and ML, conversational AI solutions can engage in human-like conversations about simple and complex topics. 

• NLP enables computers to naturally understand and generate human language, including context, intent, and sentiment. It converts unstructured human language data into a structured format for machines to understand.
• Machine learning analyzes large user interaction datasets, allowing algorithms to adapt and evolve to improve conversational AI’s accuracy and effectiveness over time.

NLP also uses additional methods, like part-of-speech tagging, to understand the relationship between words in a sentence and ensure the input text is understood and processed correctly.

Conversational AI also includes additional components including:

• Natural language understanding (NLU): Deciphers user input, extracting meaning and intent
• Natural language generation (NLG): Formulates responses in human speech
• Dialogue management: Orchestrates the flow of conversation, ensuring coherence and relevance
• Context awareness: Maintains context across interactions for a seamless user experience

Benefits of Conversational AI

AI is the future for many industries, including customer service. Check out these benefits of conversational AI to understand how this technology can advance business outcomes.

Enhanced customer experiences

Whether customers seek product information, troubleshooting guidance, or order status updates, conversational AI can redefine CX success through timely, unique interactions. Conversational AI helps bridge the gap between channels with seamless omnichannel customer experiences across:

• Phone calls
• Live chat
• SMS
• Mobile apps
• Social media 
• Messaging platforms

By integrating across various touchpoints, conversational AI follows CX best practices and ensures consistent and cohesive interactions, regardless of a customer’s preferred channel or time of need.

Increased agent efficiency and productivity  

By incorporating advanced conversational AI applications, businesses can significantly improve work performance across teams. Unlike traditional systems limited to handling basic yes or no inquiries, conversational AI-powered tools excel in managing a wider range of complex user queries. 

By handling more customer interactions, NLP in customer service enables human agents to focus on resolving intricate inquiries and tackling tasks that demand specialized attention, enhancing team productivity without sacrificing response quality or quantity. 

Accessible communication 

Conversational AI technology breaks down communication barriers, making information and assistance easily available to all users. Intuitive interfaces and language understanding enable effortless interaction regardless of language skills or technical know-how. By providing inclusive digital customer service channels like text-based chat, businesses ensure everyone can engage effectively, promoting inclusivity and equal access to support.

Scalable operations 

Implementing conversational AI platforms allows teams to expand customer service and experience opportunities without incurring large overhead costs. This is particularly handy during demand spikes, like the holiday season.

Conversational AI tools can also help businesses quickly adapt to customer needs without sacrificing efficiency or overspending. With large datasets of industry- and customer-specific data, these solutions automatically learn from and adapt to customer preferences and expectations, keeping businesses relevant and accurate.

Improved data insights and analytics 

Since conversational AI uses machine learning at its core, it’s always listening and learning. Conversational AI-powered tools and solutions use customer insights to provide data-based analyses and forecasts about customer needs and preferences.

Conversational AI also uses:

• Business intelligence to analyze past interactions
• Speech analytics to discern positive or negative customer tones
• Conversation analytics to gauge customer behavior and sentiment
• CX analytics to interpret interactions and feedback

With this customer data at hand, businesses can optimize customer experiences and boost satisfaction levels.

24/7 customer service 

Conversational AI can keep the customer-facing side of a business live at all times. Whether it’s a simple inquiry or an easy task automation, this AI technology provides 24/7 customer support, even when your team is out of the office or simply resting through the night. Conversational AI also lets you handle frequently asked questions (FAQs) without needing an agent to contribute their expertise. 

Boosted personalization 

Conversational AI uses past customer data to deliver personalized customer experiences and proactive support based on historical interactions, logged preferences and expectations, demographic information, and more. This collected data helps the AI solution create context to improve customer engagement.

With this context, AI tools can provide unique responses to customer queries, offering a faster experience that’s more tailored to their needs. 

Related: How to Use Conversational Analytics Software Effectively

5 Types of Conversational AI

Each unique type of conversational AI is designed to cater to different needs, from simple inquiries to complex problem-solving. Let’s explore five key conversational AI types:

1. Generative AI agents: AI systems that use vast datasets to deliver dynamic customer interactions and personalized or contextual responses to creative, open-ended queries.  
2. Chatbots: Conversational AI chatbots can respond to keyword-specific inputs, provide automated responses to FAQs, and solve simple requests using real-time, text-based outputs.
3. Virtual or voice assistants: Intelligent virtual agents (IVA) and assistants are AI-powered tools, like Apple’s Siri, that provide hands-free assistance for various tasks.
4. Interactive voice response (IVR): IVR systems use voice prompts to guide users through automated phone menus, route calls, or handle simple queries without human intervention.
5. AI copilot: AI assistants that provide real-time suggestions and automate repetitive tasks for industry professionals.

Common Conversational AI Use Cases

The versatility of conversational AI allows businesses of all sizes to improve experiences and enhance customer satisfaction.

1. Customer support automation: AI-powered tools handle routine inquiries and other interactions automatically.
2. Data collection: Conversational AI systems gather valuable insights from interactions to help businesses understand user behavior, preferences, and pain points.
3. Speech recognition: AI-powered speech recognition tools can understand and process spoken language, providing hands-free, accessible customer support.
4. Omnichannel chat: Modern customers expect conversational AI tools to provide consistent experiences on multiple channels while maintaining context from previous interactions.
5. Text-to-speech: This technology converts written text into natural-sounding speech for accessibility purposes.
6. Appointment scheduling: Conversational AI tools can handle appointment booking, reminder scheduling, and rescheduling processes.

Lead qualification: AI-powered lead generation chatbots can engage with leads, ask qualifying questions, and assess quality before sales teams interact with potential customers.

How To Create Conversational AI

Creating and implementing conversational AI requires a structured approach. Follow these steps to build a robust NLP- and ML-powered solution.

Set goals and objectives 

Define what you want to achieve with your conversational AI solution. Set attainable customer service goals like:

• Lowered first response times
• Reduced cost per contact (CPC)
• Increased automation across support services

These goals should also be measurable and bound to a specific timeframe.

Determine automation opportunities 

Research and identify your business’s tasks and processes that you can automate with conversational AI. This can include:

• Using CX automation to create and manage self-service portals
• Using customer service automation to automate emails and follow-ups
• Using contact center automation to manage call routing and callbacks

Develop intents and keywords 

Define the core intents, user goals, and key phrases your conversational AI must recognize. These elements will form the foundation of your system’s understanding, enabling it to respond accurately to different user inputs. Focus on common customer questions, product names, and action verbs relevant to your business.

Choose your software 

Make a conscious effort to choose a conversational AI platform that aligns with your business needs. Consider factors:

• Integration capabilities
• Scalability
• Customization options

Your platform should support omnichannel deployment and features like advanced security, reporting, and analytics.

Build intent-based conversation flows 

Create structured conversation flows your conversational AI can deploy using your identified user intents and keywords. Map out possible user journeys, ensuring the AI can handle various scenarios while maintaining a natural, engaging dialogue. Test and refine these flows to improve accuracy and user satisfaction over time.

A Conversational AI Example

Conversational AI simplifies requests to understand what people want and acts accordingly. Here’s how it breaks down a request like booking a flight from Los Angeles to Hawaii for under $300:

1. The technology uses NLP and ML to interpret user input, understanding phrases like “How much does this cost?”, “What’s the price?” and similar questions.
2. NLU digs deeper to grasp subtler meanings implied by human language, including requests for specific dates or times.
3. The AI uses dialogue management to decide how to respond based on the meaning it extracts. 

With natural language generation (NLG), the AI turns its response into human-like speech, facilitating a conversation and the actions necessary to book the trip.

Challenges of Conversational AI Technology

While conversational AI and AI in CX offer significant benefits, there are several challenges businesses must address to ensure successful implementation:

• Bias: Conversational AI models can inherit biases from their training data, leading to unfair or inappropriate responses. These biases may stem from language patterns, cultural nuances, or historical data inaccuracies, meaning solutions need rigorous testing and to be continuously monitored to detect and mitigate bias.
• Security: Maintaining data security best practices, privacy, and relevant industry standards, like HIPAA, is crucial when dealing with sensitive information. Even when conversational AI solutions comply with these standards, some people still speculate and have second thoughts about using AI solutions.

Understanding these challenges is crucial for developing a reliable, effective system. With the right software, like Nextiva, you can address these challenges head-on while reaping the benefits of conversational AI.

Conversational AI Best Practices

When using conversational AI in your business, here are a few best practices you should follow:

• Clearly define goals: Consider what you want to achieve with conversational AI and develop conversation flows or choose systems that help you reach these goals.
• Train on quality data: Train your AI systems on a comprehensive dataset to understand user intent accurately. Give it industry- and product-specific data to help it comprehend and convey the nitty-gritty of your sector and answer questions accordingly.
• Prioritize user experience: To make a chatbot more engaging, it’s advisable to design its conversation flow to match a human’s and to train your systems to detect customer sentiment.
• Be transparent: Keep customers informed that they’re interacting with a chatbot, and be open about how your bots reach certain responses.
• Make escalation easy: To avoid adding to customer frustration, always give them the option to connect with a human agent for urgent or complex queries.
• Prioritize compliance: Companies expect conversational AI solutions to comply with industry regulations and maintain a level of security and data privacy when handling customer data.

Get More from Customer Conversations

If you’re eager to embark on the journey with conversational AI, now is the perfect time. With advancing technology, we’re bound to see even greater possibilities soon. The sooner you develop a strategy for implementing conversational AI and improving customer interactions, the sooner you’ll witness its benefits. 

To start reaping the rewards, select the right platform to kickstart your conversational AI journey. Numerous custom options, such as AI-powered conversational chat windows and embedded interfaces, are available for your website or tailored integration. Plus, software like Nextiva’s conversational AI platform keeps you available around the clock, supporting customers whenever they need it.

Conversational AI FAQs

Below, we answer a few frequently asked questions about the future of conversational AI and human-like interactions across industries.