A closer look at conversational AI automation and its impact

Sandra Posavac Content Marketing Specialist

Conversational AI automation changes how businesses interact with customers, enabling natural, human-like conversations between machines and people. With natural language processing (NLP) and machine learning, these systems get what you’re saying, respond smartly, and keep improving every time you use them. 

What is conversational AI automation?  

Conversational AI automation refers to using advanced technologies like machine learning and natural language processing to automate customer interactions. By understanding and generating human-like responses, it streamlines communication, offers instant support, and enhances customer satisfaction. This approach allows businesses to handle high volumes of inquiries efficiently and consistently, ultimately improving overall service quality. 

This transformative conversational AI technology builds stronger customer relationships and accelerates CX maturity

Conversational AI automation vs. traditional support

Boost your CX maturity with conversational AI automation

Our research shows that businesses implementing conversational AI automation significantly improve customer satisfaction and experience (CX). Lower-maturity companies often rely on basic tools like FAQ automation, while more advanced organizations with higher maturity use conversational AI systems to deliver personalized support and solve complex issues. 

To better understand, here is a clearer picture of conversational AI chatbots in customer interactions: 

  • 89% of brands automate customer interactions with a focus on customer satisfaction.
  • 70% of brands use AI chatbots, yet 73% rely heavily on human agents. 
  • Only 16% of brands use conversational artificial intelligence, and 17% have AI chatbots that handle most customer queries.

But let’s be honest; while artificial intelligence is ready to work 24/7, it’s not replacing human conversations any time soon. The solution lies in combining conversational AI speed with human empathy and intuition. 

Now that you’ve seen how conversational AI can automate and improve your CX, let’s explore how it can keep your customers coming back for more.

Key components of conversational automation

To understand why conversational AI automation works so well, let’s break down the key components: 

Natural language understanding (NLU)

NLU enables conversational AI systems to interpret and process human language intuitively and naturally. Its goal is to analyze user input and identify key details, such as: 

  • Intents: Understanding the user’s goal or purpose behind the message (e.g., “What’s my account balance?”). 
  • Entities: Identifying specific details within the message, such as dates, locations, or product names (e.g., “balance for January 2025”). 
  • Context: Retaining the flow and background of the conversation automation for accurate responses, especially during multi-step customer interactions. 

With advanced machine learning techniques, NLU helps conversational AI tools understand slang, misspellings, and variations in phrasing, ensuring accurate responses. 

For instance, if a user types, “What’s my balance?” or “Can you check how much is in my account?” the system can accurately interpret the intent and respond appropriately. 

Understanding and processing human language at this level allows conversational AI to create meaningful and efficient customer interactions, enhancing user experience while reducing the workload on human agents. 

Dialog management

Dialog management is the conversational AI module responsible for determining the most appropriate response or action based on user input and the conversation context. Think of dialog management as the brain of the operation. Structured dialogs ensure smooth and logical interactions throughout a chat.

This component works by: 

  • Interpreting context: Retaining and analyzing the flow of conversation to respond appropriately. 
  • Selecting the best response: Dialog management chooses the most contextually relevant reply or action based on user input, predefined rules, or AI-driven logic. 
  • Managing workflows: It involves managing complex interactions that may involve multiple steps, such as verifying user details or completing a transaction. 

For example, if a customer asks, “Can I reset my password?” dialog management identifies the intent. It guides the user through a secure password reset process. If additional input is required, like verifying account details, the system dynamically adjusts its responses to keep the interaction coherent.

Natural language generation (NLG) 

NLG is the process by which conversational AI creates human language responses based on received data by: 

  • Transforming data into text: It takes structured data, such as user query or customer information, and converts it into a well-formed sentence or response. 
  • Maintaining context: NLG ensures responses align with the ongoing conversation, adjusting based on previous exchanges and user history. 
  • Delivering personalization: Using data such as a customer’s name or preferences generates tailored responses that feel individualized and engaging. 

Instead of a robotic “Order shipped,” AI can say: “Hi Sarah, your order #12345 is on its way and should arrive by Wednesday!” 

Knowledge base

A knowledge base is the system’s central hub for accurate, up-to-date information. It ensures that conversational AI chatbots can provide relevant and reliable responses to user queries. 

The knowledge base works by: 

  • Storing information: It holds structured data like FAQs, product details, company policies, and troubleshooting guides, which the AI can access when needed. 
  • Ensuring accuracy: Regular updates ensure that the conversational artificial intelligence system provides the most current and accurate information, minimizing the risk of errors in customer interactions. 
  • Enabling quick responses: A well-organized knowledge base allows conversational AI to quickly access information and respond to inquiries in real-time, enhancing operational efficiency. 

For example, when a customer asks about a return policy, conversational AI systems quickly retrieve and share the latest guidelines. 

Benefits of conversational AI automation

Conversational AI automation aims to make interactions between people and machines smarter and smoother. Here’s why we suggest considering it: 

  • Enhanced customer experiences 
    Artificial intelligence is available 24/7. That means customers get prompt, personalized help anytime they need it. Faster responses lead to happier customers. And happy customers stick around. 
  • Increased efficiency and cost savings 
    By handling repetitive tasks such as answering routine questions, AI allows human agents to focus on complex problems, improving efficiency and reducing costs. 
  • Consistent and accurate responses 
    AI doesn’t forget or get tired. It provides reliable, up-to-date answers every time, so customers always get accurate info. Consistency builds trust. 
  • Personalized Interactions 
    AI learns from past interactions and customer data, making every conversation custom-made. It is like having virtual assistants
  • Scalability 
    AI handles multiple chats at once. Whether it’s a holiday rush or a new market, it scales up without breaking a sweat, ensuring fast, quality service every time. 
  • Multilingual support 
    Going global? No problem. Many AI conversational platforms speak multiple languages so that you can connect with customers worldwide without awkward translations. 
  • Continuous improvement 
    Deep learning enables conversational AI systems to refine their responses over time. AI gets smarter with every interaction. It learns from customer feedback and improves its answering skills. The more it chats, the better it gets. 

Examples of successful conversational AI automation in businesses 

Conversational AI automation is already driving customer satisfaction and operational efficiency across industries. Here are some real-world examples that highlight its impact: 

Telco: Doubling engagement and increasing conversions with RCS campaign at Telekom Deutschland

Telekom Deutschland boosted its Spotify Premium trial offer with an engaging RCS-based campaign that saw impressive results. Using Infobip’s solution, they sent branded RCS messages with interactive carousels and CTA buttons, doubling engagement over SMS. 

With 26% higher open rates and 66% increased engagement, the campaign achieved 120% higher conversions. The use of RCS, with its rich media and interactive features, helped build customer trust, leading to better conversions.  

Example of RCS business messaging

Banking: 30% of customer queries resolved automatically at LAQO 

LAQO, Croatia’s first digital insurer, uses a GPT-powered assistant named Pavle to handle 30% of customer inquiries 24/7 via WhatsApp Business Platform

With 90% of queries resolved in just 3-5 messages, artificial intelligence provides fast, consistent, and accurate support. Automation cut contact center effort by 10%, freeing human agents to handle complex cases and focus on customer acquisition. The system processes claims within 24 hours, boosting efficiency and customer satisfaction. 

LAQO Infobip

Healthcare: 65% faster data collection with healthcare AI assistant 

Magdalena Clinic uses the Megi Health platform, a chatbot integrated with WhatsApp, to streamline diagnosis and offer 24/7 support. The chatbot helps patients track blood pressure, submit readings, and connect with doctors. 

Automation has cut data collection time for diagnoses by 65%. The chatbot’s friendly, human-like interaction earned 86% customer satisfaction score. The platform improves results by providing better data and education while reducing stress for both patients and healthcare professionals.

Medical AI chatbot example

Retail & eCommerce: 207% of the campaign target reached at NIVEA

NIVEA’s #MyCocoaShade campaign saw outstanding results when it used a WhatsApp chatbot to engage consumers in Nigeria and Kenya. The campaign encouraged participants to send photos and receive a stylized version with their skin shade. WhatsApp platform was perfect for handling image submissions and smooth automation. 

With 207% of their target reached, NIVEA received over 500 photo submissions and added 2,772 new consumer profiles. This digital engagement and traditional marketing reached 250,396 people and boosted brand presence. 

WhatsApp chatbot

10 steps for implementing conversational AI automation in customer service

If you are thinking of transforming your customer service with conversational AI, follow this simple guide to get started, improve efficiency, and keep your customers happy. 

  1. Define goals and objectives 
    Before integrating conversational AI into your customer service, define your goals and objectives. Ask yourself: Do you want to improve response times? Offer 24/7 support? Or personalize service to create a more engaging customer experience?  
    Setting clear goals will direct the implementation process and ensure you’re focused on what matters most. 
     
  2. Analyze customer data 
    To train an effective AI system, you must gather relevant customer data, such as past interactions, feedback, preferences, and behavioral patterns. This data helps you understand customer pain points, common queries, and expectations, enabling the AI to offer more accurate, context-aware responses.  
    Additionally, analyzing customer emotions and categorizing inquiries based on urgency or complexity will make the system more adaptable. Data-driven insights will allow your AI to personalize interactions and evolve, making it more responsive to real customer needs. 
     
  3. Choose the right platform 
    Pick AI-powered conversational experience platform that fits your goals. It should offer strong natural language processing, integrate easily with existing tools, and scale with your business needs. Focus on features like easy integration with existing CRM systems, analytics capabilities to track performance, and a user-friendly interface that makes management easier. 
    Additionally, consider whether the platform can handle multi-channel support, whether it’s through live chat, social media, or messaging apps.  
     
  4. Build a knowledge base 
    Your AI system needs access to up-to-date, reliable information to answer customer queries effectively. Continuously update the knowledge base to incorporate changes in products, services or customer expectations.    
    Make sure your AI can quickly access the necessary information from the knowledge base when required, minimizing response time and improving the conversation quality. 
     
  5. Train and optimize the system 
    Training your AI is an ongoing process. Start by providing the system with real customer interaction data to teach it how to respond effectively. As it learns from this data, regularly adjust the AI using feedback and performance reviews to ensure it adapts to customer needs. Incorporate real-world scenarios, language variations, and specific queries to ensure your AI understands and responds accurately.  
    Over time, continually optimize the system based on performance metrics and user feedback, making sure that it gets smarter with each interaction. 
     
  6. Integrate with existing systems 
    Make sure your AI works smoothly with your CRM and other customer support tools. This allows the AI to access customer data, service history, and personalized information, making interactions more informed and efficient. Ensure that your AI can escalate complex issues to human agents and capture valuable data for reporting and analysis.  
    By integrating with other business systems, you create a unified, smooth workflow that enhances the AI’s capabilities and your team’s performance.  
     
  7. Promote and educate 
    Once your AI is live, promote it to customers and educate them on how to interact with it, whether it’s through chat, voice, or other communication channels. Educational resources such as guides, tutorials, and FAQ sections can help customers feel confident in using AI and ensure smoother adoption.  
    The more your customers understand how to use the system, the higher the adoption rates and the more effective your AI will handle customer inquiries. 
     
  8. Monitor and analyze performance 
    Keep an eye on key metrics like response times, resolution rates, and customer satisfaction. Use these metrics to evaluate the AI’s effectiveness and identify areas that require improvement.  
    Use customer feedback to understand any challenges or frustrations with the system. Ongoing analysis will help you improve your AI. 
     
  9. Provide human escalation paths 
    Make sure your AI system can quickly connect customers to human agents for complex or sensitive issues. Clearly define escalation paths within your AI system, ensuring customers know when and how to transition from artificial intelligence to human support. This can be triggered automatically based on keywords or after a certain number of unsuccessful attempts.  
    Providing easy access to human agents ensures that customers don’t feel frustrated when the AI is unable to resolve their issues, maintaining a positive customer experience. 
     
  10. Continuously improve and adapt 
    Artificial intelligence isn’t static, and neither should your system be. Check its performance regularly, add new features, and adjust as customer expectations change. You keep the system fresh and relevant by adding new features, improving natural language processing capabilities, and expanding the AI’s knowledge base.   

Overcoming challenges in conversational AI automation

Implementing conversational AI automation comes with its own set of challenges. However, with the right strategies, you can turn these issues into opportunities for improvement. Here’s how to address the most common obstacles: 

Natural language understanding limitations

NLU can sometimes misinterpret complex or unclear language. To fix this, train your models with diverse, real-world data. Regular updates and intelligent algorithms will help your AI understand how people talk. 

Data quality and availability

Good data is the heart of practical AI. However, collecting enough accurate and diverse data can be tricky, particularly when the data is incomplete or inaccurate. Gather a wide variety of data that reflects real customer interactions. Use data augmentation and feedback loops to make your AI more thoughtful and relevant. 

Privacy and security concerns

Privacy matters. Protect customer data with encryption, anonymization, and compliance with laws like GDPR. Be transparent about how you use data, so customers trust your AI to keep their information safe. 

User adoption and trust

To get users on board, show them the benefits of AI and how it works. Make sure it provides helpful, human-like responses and clarifies what it can and can’t do. Trust doesn’t happen overnight, but it’s always worth the effort. 

Handling complex or sensitive queries 

AI has limitations and cannot address every issue. Make sure customers can easily talk to a human for complex or sensitive issues. Regularly update language models to identify better and manage such situations. 

Scalability and performance

As customer demand grows, your AI needs to keep up. Use cloud-based solutions to scale quickly and ensure high-quality service, even during busy times. 

Integration with existing systems

Integrating AI with your existing CRM and support systems can be challenging. Still, it can be a smooth transition with the right APIs and customization. Make sure the AI enhances your operations rather than complicating them. 

Conversational AI automation example

Must-know tips for effective conversational AI automation

Want to get the most from your conversational AI after implementation? Learn how to maintain a system that works well and keeps everyone satisfied. And of course, don’t just set it and forget it. Continuously monitor your AI’s performance, adapt to customer needs, and make regular updates to keep it effective. 

Prioritize natural language understanding 

Smarter AI means happier customers. Invest in powerful NLU and keep training your language models. The better your AI understands what people are saying, the better it can respond. 

Personalize the experience

Use customer data to make interactions feel personal. When your AI knows your customers, it can connect with them better and keep them returning for more. 

Maintain transparency and trust 

Tell your customers what your AI can and can’t do. A little transparency goes a long way toward building trust and smoother interactions. 

Prioritize data privacy and security

Guard your customers’ data like a treasure. Use advanced security and comply with laws to show you value their trust. 

Provide comprehensive training for customer service teams

Train your customer service teams to interact effectively with the AI system. Proper training ensures efficient collaboration, smooth operations, and quick problem-solving. 

What to expect next? – The future of conversational AI automation 

Conversational AI will transform how we interact with technology. Explore some exciting trends shaping its future: 

Multimodal interactions

The future of conversational AI will involve more than just text or voice. It will incorporate multiple input forms. Imagine interacting with your AI through voice, text, visual cues, or even gestures. This will make the interaction feel more natural and intuitive, seamlessly fitting into our lives and devices. 

Advanced language models

Advanced language models will understand you better than your best friend. With strong learning improvements, next-gen conversational AI will better interpret language and context, delivering more accurate responses. 

Emotional intelligence and empathy 

Future AI systems will go beyond logical responses and begin detecting emotions, allowing them to offer empathetic support. AI will respond in more emotionally aware and appropriate ways by analyzing tone, word choice, and even facial expressions (in multimodal systems). 

Personalization and adaptive learning 

Machine learning will allow conversational AI to adapt and learn from every interaction, continuously refining responses and becoming more personalized. As they learn more about individual preferences, these systems will deliver more relevant, customized experiences for each user. You will feel like they were made just for you. 

Integration with emerging technologies 

We are already seeing the integration of conversational AI with Augmented Reality (AR), Virtual Reality (VR), and connected IoT devices. From smart homes to virtual worlds, conversational AI will blend into your tech landscape like it was always meant to be there. 

Conversational commerce and transactions 

Conversational AI is transforming how we do business by enabling natural language-based transactions. Customers can complete purchases, check account details, and even receive personalized offers through chatbots or voice assistants without leaving the conversation. 

Improved scalability and performance

Advancements in cloud computing and distributed systems will allow conversational AI to scale effortlessly, handling millions of simultaneous interactions without sacrificing performance. Thanks to that, businesses can provide consistent, high-quality support even during busy times. 

Ethical and responsible AI 

As AI becomes a more integral part of daily life, the focus on transparency, fairness, and privacy will increase. This will ensure that AI systems prioritize user trust and operate ethically in all contexts. 

The perfect AI-human partnership 

The future of conversational AI automation is about enhancing the human experience through powerful AI tools, rather than replacing humans. By combining AI’s speed and efficiency with human empathy and intuition, businesses can deliver exceptional customer experience. 

Conversational approach to customer engagement

See how conversational AI automation saves time, enhances customer satisfaction, and drives business growth.

Share
Jan 30th, 2025
13 min read
Sandra Posavac Content Marketing Specialist