Customer expectations are on the rise. As they face more options than ever, they’re looking for fast, personalized experiences. In fact, studies show that 79 percent of customers want businesses to understand their individual needs and preferences. And 74 percent of customers get frustrated when they can’t talk to someone who understands their history. Conversational AI examples show how different businesses are using this technology to enhance their customer service operations. This article will highlight some of the best examples of conversational AI for customer service and introduce top conversational AI companies that are helping businesses transform how they connect with customers.
One tool that can help you reach your objectives is Voice AI’s text-to-speech tool. This easy-to-use solution can produce humanlike voice outputs for your conversational AI system, enhancing its performance and improving your customers’ satisfaction.
What Is Conversational AI and How Does It Work?
Conversational AI consists of intelligent software that understands human language using natural language processing, machine learning, and other AI technologies. This technology is behind applications like chatbots and virtual assistants that simulate human conversation.
At a high level, conversational AI systems take a user’s input, text or speech, process and interpret it using NLP and machine learning, and then generate an appropriate response based on intent and context. In short, they allow humans to have automated conversations with machines.
How Does Conversational AI Work?
Conversational AI involves several key steps and components.
Here is how it works:
- Speech recognition: Converts speech to text.
- Natural language understanding (NLU): Parses the meaning of the text.
- Dialogue management: Tracks the context and flow of conversation.
- Response generation: Formulates a reply.
- Text-to-speech synthesis: (for voice applications) Vocalizes the response.
Machine learning techniques, intense learning methods like transformers, allow conversational AI to improve over time. Training on more data and interactions helps the systems to:
- Expand their knowledge
- Better understand and remember context
- Engage in more human-like exchanges
What Are the Different Types of Conversational AI?
Understanding the types of conversational AI can help companies pick the best conversational interface for their business. The most common are:
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.
AI-Powered Chatbots
Yes, chatbots are the first (and perhaps most common) form of conversational AI. You may have had bad user experiences with chatbots through social media channels like:
- Facebook Messenger
- Google Assistant
Typically, these bad experiences would’ve had no conversational flow at all, mainly because they’re just very basic chatbots that react to different messaging prompts and your selection of predetermined answers. Maybe they just gave you a link to the Help Center and said “our agents are unavailable right now.” (And yes, that example comes from personal experience.)
How NLU and NLG Make Chatbots More Human, and More Useful
But chatbot technology has grown past that point, and they can be good, helpful tools that use natural language understanding (NLU) and natural language generation (NLG) to interact with people using more human language.
AI chatbots make it easy to:
- Answer basic questions.
- Look up information.
- Assist customers without them even needing to speak to an agent.
Generative AI bots
Generative AI enhances chatbots by enabling them to provide personalized responses based on:
- User context
- Handle a wider range of queries
- Offer more accurate and relevant information
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-4 model. Generative replies make our bots more intuitive and 3 times faster at resolving issues by generating human-like answers from a business’s knowledge base. These improvements make every customer engagement more accurate and satisfying.
AI Agents
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
Is there a phone equivalent of an AI bot? Yes, and it’s called a voice assistant. Voice assistants recognize voice commands and turn them into text entries for the AI, so that they can perform similar functions to AI chatbots.
Think of them like Google Home, Amazon’s Alexa, or Apple’s Siri—they:
- Understand your speech
- Recognize your request
- Perform an action based on it
Think of voice assistants as convenient voice interfaces. They can carry out commands and reply to queries, making them helpful tools for looking up information or performing basic tasks.
Interactive voice assistants (IVA)
Going one step beyond voice assistants, we have interactive voice assistants (IVA) or virtual assistants. They take the convenience and functionality of voice assistants, but add a level of conversational interactivity. Interactive voice assistants are there when your contact center agents are busy, answering each call immediately to help customers as soon as they call in.
Why Voice Assistants Are the New First Line of Customer Support
They use natural language processing (NLP) and natural language understanding (NLU) to provide a proper conversation or identify a caller’s concern and direct them to the right agent. As with AI chatbots, interactive voice assistants are great for helping customers resolve issues without even needing to speak with an agent.
They can:
- Answer questions
- Look up information
- Assist customers
It saves callers time and reduces agents’ workloads.
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.
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20+ Innovative Conversational AI Examples and Use Cases in Different Industries
Conversational AI is transforming customer service experiences across industries. Here are the top examples:
10 Example of Conversational Artificial Intelligence In Multiple Industries
These examples highlight the diverse applications of conversational AI in enhancing customer service and engagement across various industries. From virtual assistants in smartphones to chatbots in customer support, companies are leveraging this technology to create more efficient, personalized, and interactive experiences for their customers.
1. Apple: Revolutionizing customer support with Siri
Apple has incorporated conversational AI into its customer service through Siri, the virtual assistant on iPhones. Customers can use voice commands to ask questions, troubleshoot issues, and receive personalized assistance, creating a seamless and efficient support experience.
2. Amazon: Enhancing customer engagement with Alexa
Amazon’s Alexa is a prime example of conversational AI in action. By integrating Alexa into their Echo devices and other smart products, Amazon has transformed the way customers interact with their services.
Users can, through voice commands, do the following:
- Order products
- Get recommendations
- Control home devices
3. Bank of America: Simplifying banking with Erica
Bank of America’s Erica is an AI-powered virtual assistant designed to enhance customer service in the banking industry.
Customers can ask Erica for:
- Account information
- Make transactions
- Receive personalized financial advice
It is making it easier and more convenient to manage their finances.
4. Uber: Streamlining customer support with chatbots
Uber utilizes chatbots powered by conversational AI to handle customer support queries. By automating responses and providing instant solutions to common problems, Uber ensures a faster and more efficient support system, improving overall customer satisfaction.
5. Sephora: Enhancing the beauty shopping experience with chatbots
Sephora’s Virtual Artist is a chatbot that uses conversational AI to provide customers with personalized beauty recommendations. By understanding customer preferences and offering tailored advice, Sephora creates a more engaging and personalized shopping experience.
6. Duolingo: Making language learning interactive with chatbots
Duolingo employs conversational AI in its language learning app by using chatbots to simulate conversations with native speakers. This interactive approach allows users to practice their language skills realistically and engagingly, enhancing their learning experience.
7. Domino’s: Simplifying the pizza ordering process with chatbots
Domino’s Pizza has integrated conversational AI into its ordering system through the use of chatbots. Customers can place their orders and track deliveries through messaging platforms, creating a convenient and hassle-free experience.
8. Autodesk: Enhancing customer support with virtual assistants
Autodesk, a software company, uses virtual assistants powered by conversational AI to provide customer support. These assistants can assist customers with troubleshooting, offer training resources, and provide guidance, improving their overall experience with the software.
9. KLM Royal Dutch Airlines: Improving customer service with AI-powered chatbots
KLM utilizes conversational AI to provide real-time customer service through its chatbot, BlueBot.
Customers can:
- Ask questions
- Get flight updates
- Book tickets
It simplifies the travel experience and increases customer satisfaction.
10. BMW: Personalizing customer interactions with conversational AI
BMW has incorporated conversational AI into its customer service by using virtual assistants in its vehicles. These assistants can control various car features, provide information about destinations, and even engage in casual conversations, creating a more personalized and engaging driving experience.
13 Conversational AI Use Cases Across Industries
1. Self-Service Customer Support
Businesses can use conversational AI to help customers resolve issues without connecting with a human agent. This includes options like chatbots and voice assistants.
Customer support automation can help customers follow steps to:
- Resolve issues
- Access information
- Perform basic tasks like:
- Transactions
- Upgrading subscriptions
For instance, a retail brand can integrate a chatbot on its app/website to help customers track orders or return them.
2. Customer Engagement Automation
AI can also help personalize customer communications in their preferred language and tailor messages based on customer sentiment. It ensures seamless interactions across multiple channels, making customer engagement more meaningful.
For instance, a hospitality company can use conversational AI to engage customers in their preferred language through their:
- Website
- Messaging platforms
- Offer contextually relevant responses
3. Personalized Marketing Campaigns
With conversational AI, you can personalize marketing messages based on customer preferences and their actions on the website.
For instance, an e-commerce website can use chatbots to offer personalized recommendations based on the pages the customer visits. It can also recommend similar products to customers when they’re shopping and help them make purchase decisions.
4. HR Automation
An innovative example of conversational AI in the HR industry is the automation of tasks like:
- Interview scheduling
- Responding to employees
- Screening candidates
For example, an HR manager can screen candidates, send emails, and schedule interviews, saving time and effort during the recruitment process.
5. Scheduling Appointments
The AI allows customers to conveniently book, reschedule, or cancel an appointment without human intervention. A chatbot can help a customer find available appointment slots and guide them through the steps to confirm it.
For example, healthcare providers allow patients to book appointments through voice assistance or chatbots. This helps patients find available doctors and times based on their preferred location and book the appointment.
How Exotel Delivers Smarter Conversations with AI Voice Technology
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6. Intelligent Call Routing
Another powerful example of conversational AI is routing, where AI analyzes the caller’s intent for inbound calls and directs the call to the correct department or agent.
For example, a customer may have specific requests for investment or retirement plans in financial services. For such complex issues, AI can identify the caller’s intent and forward the call to the agent with the right skill so the customer can get personalized financial advice.
7. Lead Generation
Conversational AI tools can be efficiently used for lead generation. They can engage with potential customers in real time, sparking their interest in the company or its offerings. Gathering insightful information during every interaction helps identify potential leads and qualify them for the sales team to follow up on, making the lead generation process more efficient.
For example, a retail company can use the website chatbot to engage visitors, ask questions to understand their needs, and convert the browser into a potential buyer.
8. Agent Assistance & Training
It offers real-time support to contact center agents during customer interactions, guiding them through complex conversations and delivering better service. Analyzing customer interaction enables conversational AI systems to:
- Suggest responses
- Provide relevant information
- Alert agents to any compliance issues
This allows both agents and supervisors to monitor performance and identify areas for improvement.
For example, a financial service contact center can deploy agent assistance to guide agents through compliance protocols and provide responses to sensitive customer queries.
9. Automated Customer Surveys
AI tools like chatbots or smart IVRas also help you to automate customer data collection naturally and conversationally.
This ensures that you:
- Follow up at the right time
- Increasing the response rate
- Gathering valuable customer feedback
For example, a healthcare provider can use AI-powered IVR surveys to gather patient feedback post-treatment, ensuring meaningful insights to improve service quality.
10. Virtual Assistance for Telemedicine
A good example of conversational AI is making healthcare accessible through smart IVR and chatbots. These tools allow patients to receive virtual consultations from anywhere at their convenience.
For example, a telemedicine platform can deploy AI-powered chatbots to guide patients through systems and connect them with appropriate doctors for virtual consultations, ensuring timely, convenient, and efficient care.
11. Fraud Detection & Prevention
Conversational AI can monitor customer interactions and automate calls or messages to verify users using OTP.
It helps flag:
- Suspicious activities
- Identify potential fraud
- Take immediate action
For example, a retail brand can use AI to monitor transactions and unusual purchase patterns. If the brand detects a bulk order from a new location for an old customer, the system can trigger an OTP via SMS to confirm the customer’s identity.
12. Customer Onboarding
One example of conversational AI is the customer onboarding process, where AI can guide new users through the necessary steps and provide real-time support on its usability. This can reduce user drop-offs and ensure a smooth onboarding experience.
For example, a banking service can use AI chatbots to assist new customers in setting up their accounts. The bots can also help users identify and learn useful features and enhance their experience.
More innovative Routing and Seamless Support: The Exotel Advantage
You can optimize your contact center operations with AI-powered communication platforms like Exotel. It offers solutions that help you route inquiries, engage customers across channels, and provide uninterrupted customer service.
13. Voice-based Customer Service
Companies deploy AI-powered voice agents to handle incoming calls, which are transcribed in real time using advanced speech-to-text (STT) capabilities.
These agents can immediately:
- Interpret customer queries
- Determine the urgency or complexity of the issue
- Decide whether to provide automated troubleshooting
- Escalate the call to a human agent
This process reduces customer wait times and improves first-call resolution rates.
Seamless Escalation: When Voice AI Knows When to Hand Off
For example, when a caller states, “I’m having trouble with my internet,” the AI quickly confirms details, perhaps verifying the account, and either troubleshoots the issue or hands off the call to a specialized team. This seamless handoff ensures efficiency and enhances the overall quality of customer support.
Example:
Caller: “I’m having trouble with my internet.”
AI: “I can help. Could you please confirm your account number so I can run a diagnostic?”
Real-World Use Cases of Voice AI Across Key Industries
Voice AI In Healthcare
In healthcare settings, voice agents serve multiple roles, from managing appointment scheduling and sending reminders to updating patient records. These systems are designed to handle multi-turn interactions that require understanding context over several dialogue exchanges.
Real-time transcription is critical, enabling agents to interpret and respond to patient requests instantly.
Secure, Compliant Conversations: Voice AI in Patient Communications
Consider a patient saying, “I need to reschedule my appointment.” The system can verify existing appointment details, cross-check available time slots, and offer suitable alternatives, all while ensuring the conversation remains private and compliant with regulatory standards.
This application streamlines administrative processes and contributes to a personalized patient care experience.
Example:
Caller: “I need to reschedule my appointment.”
AI: “Sure, I can help with that. Are you looking for another weekday or a weekend appointment?”
Voice AI in Financial Services
Financial institutions leverage voice-enabled IVR systems to streamline a wide range of customer inquiries and actions. By combining real-time STT, context retention, and secure API integration, voice AI applications can:
- Authenticate users
- Handle balance inquiries
- Process urgent security tasks
From Verification to Action: The Role of Voice AI in Financial Security
For instance, when a caller reports a stolen card, the AI can initiate identity verification by requesting a PIN or another form of authentication. Once verified, the system immediately triggers security protocols to lock the affected account and alerts the appropriate teams.
This rapid response minimizes potential financial losses and ensures the customer’s security, all without a lengthy waiting period.
Example:
Caller: “I need to report a stolen card.”
AI: “Understood. Please verify your identity with your PIN, and I will lock your card immediately.”
Voice AI in E-Commerce
Retail businesses can integrate voice-enabled assistants to manage order inquiries and handle routine customer service tasks. These AI solutions connect directly to real-time order management systems, allowing for accurate and immediate updates.
When a customer says, “My jacket order is delayed,” the voice assistant can access shipment data, determine the current status, and then offer to send a detailed update via text message.
Timely Updates, Happier Customers: AI Support in E-Commerce
This approach improves customer satisfaction by providing timely information and allowing human agents to address more complex issues, ultimately streamlining the shopping experience.
Example:
Caller: “My jacket order is delayed.”
AI: “Let me check that for you. Would you like me to send you a text with the updated delivery estimate?”
Voice AI in Travel And Logistics
Travel agencies, airlines, and logistics companies are adopting voicebots to handle dynamic and time-sensitive tasks such as reservations, rebookings, and shipment tracking. With voice AI, callers receive instant feedback during multi-turn conversations, where the system continuously updates based on real-time data.
Personalised Travel Support: Voice AI for Time-Critical Situations
For instance, if a traveler states, “I missed my flight and need to rebook,” the system can quickly verify the traveler’s location, access available flight options, and propose next steps while offering alternatives, such as rebooking immediately if the caller is still at the airport or suggesting flights for the following day.
This can reduce the stress associated with travel disruptions and enhance customer loyalty by offering an efficient and personalized service.
Example:
Caller: “I missed my flight and need to rebook.”
AI: “I can help with that. Are you still at the airport, or would you like to explore flights for tomorrow?”
Meeting Modern Expectations With Intelligent Voice Interactions
These examples demonstrate how AI-powered voice interactions enhance operational efficiency by reducing human workload and response times, while also elevating the customer experience through personalized and responsive service.
These use cases reflect the growing demand for voice-based solutions that integrate seamlessly with real-time systems to meet the modern expectations of:
- Speed
- Accuracy
- Security
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How to Implement a Conversational AI Strategy
1. Set Goals and Identify Use Cases
The first step in a successful conversational AI implementation is defining your goals and identifying your use cases. 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.
Why Clear Objectives Drive Better Conversational AI Outcomes
Be specific about your objectives and the problems you want to solve to determine 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. Analyze Data to Reveal Opportunities for Automation
The next step is to analyze your data to determine 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 AI agents, can remove the guesswork by using your data to tell you what to automate.
3. Get Stakeholder Buy-In
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 Budget and Resources for Implementation
After deciding how you want to use conversational AI, consider how much money and resources your business can allocate. For companies 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. Examine Current Infrastructure and Communication Channels
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. 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 Conversational AI 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.
Why Integration and Speed Matter in Conversational AI Implementation
You should also investigate implementation timelines to understand how quickly the AI can be deployed and any additional development costs involved 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. Measure Performance and Optimize
Once your conversational AI is deployed, look at the 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.
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