Conversational AI for Customer Service : The Complete Guide

Conversational AI for Customer Service : The Complete Guide

As businesses strive to maintain a competitive edge, they increasingly turn to innovative solutions. Among these, Conversational Artificial Intelligence stands out as a particularly promising option. Its ability to mimic human conversation has catapulted it to the forefront of essential technologies, offering significant benefits across various industries.

From natural language understanding (NLU) to personalized services, conversational AI offers companies new ways to interact with customers. How exactly? What limits should you be aware of?

What Is Conversational AI?

Conversational Artificial Intelligence is a technology based on simulating human dialog. It's an intelligent virtual agent capable of conversing with humans using natural language. In addition to understanding complex requests, it analyzes and determines user intention and provides personalized and intelligent responses.

To do this, Conversational AI uses two main technologies:

  • Natural Language Processing (NLP): This allows the technology to understand human language, taking into account the user's request.

  • Machine Learning (ML): The backbone of Conversational AI, empowering it to evolve and enhance its responses over time. This adaptability is key to its continuous improvement, ensuring it remains at the forefront of technological advancements.

How Conversational AI Works

Conversational AI uses sophisticated algorithms to interact effectively with users. Its process can be broken down into four steps.

  1. Handling Inputs

Here, raw and unstructured data is converted into information machines can understand. The user provides this either by text or by voice. If these inputs are via text, the NLU (Natural Language Understanding) is used to discern the user's intention. Automatic voice recognition (AVR) is used with speech. This converts sound into packets of language that the machine can then analyze.

  1. Analyzing Inputs

This step entirely uses NLP (natural language processing). It allows machines to decipher users' requests (whether by voice or text) to understand their intentions.

  1. Output Generation

Using NLG (natural language generation), the system can formulate immediate and understandable responses for the customer.

  1. Learning through Reinforcement

This phase allows the system to improve its responses over time. During this stage, machine learning algorithms and automatic learning come into play. Their role is to enable the AI to provide users with more precise and tailored results, resulting in real conversations with humans.

What’s the Difference Between Conversational AI and Conventional Chatbots for Customer Service ?

While conversational AI is undoubtedly more efficient, classic chatbots are still helpful. However, these two technologies offer distinctly different solutions. While a smart virtual agent using conversational AI can answer other requests, a traditional chatbot can only handle predetermined tasks.

Unlike classic chatbots, which act robotically, conversational AI optimizes the customer experience through human-like interactions.

Another key difference is that conventional chatbots are much better suited to simple and well-defined projects and tasks. At the same time, conversational AI is better suited to complex uses that require more advanced decision-making.

What Are the Advantages of Conversational Artificial Intelligence for Customer Service?

The goal of conversational artificial intelligence isn't to fully replace human agents. Instead, the idea behind the technology is to improve teams' working conditions. Companies benefit from this in several ways:

  • Customer service efficiency: Conversational AI uses chatbots, voicebots, and callbots in customer service. For example, the YeldaAI callbot solution allows up to 50% of tasks to be automated and responses to be provided 24/7. Customer service advisors can focus on more complex demands and call center KPIs to improve considerably.

  • Customer satisfaction: With contextual and adapted responses, conversational AI offers natural and personalized interactions. This improves customer satisfaction, as customers feel better taken care of.

  • Lead Generation: The system makes collecting essential data easy by engaging with prospects after interactions, which can significantly improve lead generation.

  • Process Automation: Conversational AI can automate many different processes in areas like marketing, sales, customer support, after-sales, or even HR.

  • Interaction Personalization: AI can analyze user data to adjust responses and suggestions according to individual preferences, indirectly making exchanges more engaging and relevant.

Here are some statistics to illustrate these advantages:

  • According to the American company 8x8, conversational AI simultaneously manages up to 87% of their regular customer interactions. It shows how much it can reduce the need for human intervention while maintaining natural and fluent interactions.

  • According to a Salesforce study, 69% of customers prefer to address their demands to virtual assistants because of the speed of the service.

  • According to an Atento study, conversational artificial intelligence can easily understand 90% of customer intentions while offering a 95% customer satisfaction rate.

  • According to a study by Capgemini, 57% of users who are satisfied with their conversational AI interactions spend more after using this channel.

Use Cases of Conversational AI for Customer Service

Conversational artificial intelligence comes with many advantages in many different areas. Here are some of them:


Conversational AI in healthcare can optimize access to care and improve the administrative process. Here are some tangible examples:

  • Virtual Medical Assistance: Offering patients quick advice, answering questions about their symptoms, and offering appropriate treatment.

  • Managing Appointments: Helping patients to find available appointments and quickly handling calls before appointments.

  • Healthcare Education: Provide patients with preventative advice, help them understand their health, and show them how to stay in shape.


Conversational AI can help financial services offer customers more personalized and efficient services. For example:

  • Banking Operations Assistance: Helping customers to quickly carry out operations like transferring money, checking balances, paying bills, etc.

  • Managing Complaints: Helping customers to file complaints, providing them with real-time updates on their complaints.

  • Personalized Financial Advice: Helping customers optimize their finances and manage their money, savings, and investments.


In retail, conversational AI can offer customers a better purchasing experience, particularly in terms of:

  • Buying Assistance: With personalized recommendations, chatbot solutions can help customers find the right products, compare prices, and complete orders.

  • Stock Management: Help retailers monitor stock levels, reorder stock, and avoid stock shortages.

  • 24/7 Customer Service: Ensuring customer service at any time of the day, handling returns and refunds, and proactively answering questions.

Beyond these advantages, here are some more statistics that show how companies can benefit from conversational AI:

  • According to YeldaAI, chatbots improve customer satisfaction by 30% in retail.

  • A McKinsey & Company study in 2023 shows that medical chatbots can reduce hospitals' incoming call volume by 40%.

  • A Forrester Research study showed that conversational AI chatbots can reduce recurring bank transaction times by 25%.

  • According to a Salesforce study, around 62% of customers are more likely to buy from a brand with a chatbot.

The Drawbacks and Challenges of Conversational AI

While conversational AI is an appealing prospect, it has its limits. Some risks and challenges must be addressed. Here are some of the main ones:

  • Bias risks: They can alter the quality and equity of AI responses, leading to errors and even unfair or discriminatory decisions.

  • Limited comprehension: AI might have limited comprehension when dealing with ambiguous or complex requests or during non-structured conversations. This can lead to it needing help to respond to specific questions or even offer incomplete or inaccurate solutions.

  • Confidentiality and Data Security: Conversational AI has to deal with sensitive information, such as search history, personal information, and more. There are risks of data leaks, hacking, or even foul play.

Each of these challenges and risks associated with conversational AI shows how important it is to think carefully about its development and use. Regarding user data, for example, the technology needs to show that it can safely and securely store data.

Integrating Conversational AI into your Company Customer Service with YeldaAI

YeldaAI specializes in developing callbots, voicebots, and chatbots, allowing you to integrate conversational artificial intelligence into your business with just a few clicks. If you're looking for AI solutions that meet your company's objectives, don't hesitate to ask us for a demo.