The ultimate guide to generative AI chatbots for customer service

Learn how generative AI can improve customer service and elevate both customer and agent experiences to drive better results.

Content Marketing Specialist

Monika Karlović

Content Marketing Specialist

Conversational experiences and generative AI are all the rave these days, and they have proven to be a game-changer for many businesses.  

Customers are looking for fast, human-like responses from chatbots, and generative AI can help brands elevate their customer support, if trained and integrated in the right way. 

Generative AI definition

Generative AI (GenAI) is a type of artificial intelligence that can create new and unique content like text, videos, images, audio, etc., resembling human created content. The AI models learn patterns and structures from input data to create a totally new piece of content with similar characteristics.  

14 AI chatbot use cases for customer service

1. Support in onboarding process 

Onboarding can bring about tons of questions from users and create a backlog of work for agents. By creating a messaging flow with an AI chatbot that guides customers through the entire process, you can elevate their experience with onboarding on their favorite channel while easing the workload for customer support agents. 

2. Connect with an expert

An AI chatbot can be helpful for a wide range of queries, but sometimes customers just need to speak with an expert. From medical professionals to technical support, your AI chatbot can instantly detect the intent of the user and direct them to a professional if they cannot assist with the query. 

3. Assist in search for business locations

By using location services and training your AI chatbot accordingly, you can offer customers support on finding local stores, bank branches, pharmacies, etc. Your chatbot can summarize a list of local locations, working hours, time to travel, and other important information all in one conversation.

4. Educational videos for support 

With rich media features on your customers’ favorite messaging channels, you can use AI to send relevant educational videos for customer queries. From product registration and set up, to technical issue support, or onboarding, educational videos can help speed up time to resolution and offer customers a dynamic and entertaining way to get support.

5. Guided product set up or registration

You can train your AI to thoughtfully guide your customers through their product registration and setup process. With the ability to answer FAQs, and offer step-by-step help on their journey, you can lighten the load for live agents and improve this experience for end-users with a self-paced process. 

6. Usage and account information 

AI can be incredibly helpful in getting customers up to date information they need. For example, if a customer wants to know how much data is left on their phone plan, they can message your AI chatbot, which scraps your databases for the right information and quickly updates the customer with little to no wait times.  

7. Manage delivery queries

Delivery queries are one of the most popular types of queries customers have. By training your AI to manage anything from delivery FAQs, changing delivery address or time, and all other delivery related questions, you can ensure customers get the answers they need quickly and at any time of day (or night).  

8. Support during purchase 

Sometimes customers need fast support during purchase, and if they can’t get it, you run the risk of them abandoning their order. By utilizing an AI chatbot for customer service you can provide 24/7 instant support for any purchase related needs and questions. 

9. Booking appointments

Appointment booking and management is one of the more popular ways businesses use chatbots for support. Customers can choose their appointment times, cancel, and reschedule as needed without having to wait for an agent.  

10. Manage refunds and returns 

Ensuring your refund and return process is smooth is critical to customers repurchasing with you in the future, even if they didn’t keep the product the first time. With an AI chatbot, you can guide customers through the return process, offer updates, and ensure they are satisfied with your services overall. 

11. Automate new account creation

Account creation or profile registration can be done with an AI chatbot over any messaging channel of your choice. Imagine a lead is interacting with your chatbot, asking some FAQs and is ready to create an account with you. Instead of sending them off to a website or app, keep them in the conversation and have your AI chatbot collect answers you need to build their profile. 

generative ai for customer service open a new account

12. Manage FAQs

AI is a great tool to manage and answer FAQs for customers. Ensure your customers can get round the clock, relevant, and fast support when they need it without waiting for an agent or searching your website for answers.

13. Launch customer satisfaction surveys

Determining customer satisfaction is crucial to improving your support services. Launch regular customer satisfaction surveys with an AI chatbot that can collect responses and feedback directly in chat.  

14. Provide updates for claims, delivery, order status 

Customers don’t like to be kept waiting. It’s one of the biggest pain points for customer service. Providing updates for insurance claims, delivery and order statuses can elevate your customer service and ensure your customers aren’t waiting for answers to their queries.  

Rule-based vs. AI chatbots: When to use which?

Chatbots have become a staple for many businesses in their customer support arsenal. There are a few ways you can use AI to improve chatbots. Let’s deep dive into AI chatbots for customer service, and how they compare to the standard rule-based chatbot.

Rule-based chatbots

A key word driven chatbot with defined rules to guide customers through a series of menu options. 


  • Fast integration and go-to-market 
  • Predictable and reliable behavior 
  • Full control in set responses
  • Templates available


  • Harder to navigate and manage with multiple FAQs and use cases 
  • Low conversational experience 
  • High maintenance costs 

Use cases:

Customer support

Offers and services

Appointment booking

Who’s a good fit?
Brands that have a small number of use cases (up to 5) and are not focused on conversational experience, but are wanting to go to market quickly.

AI chatbot

These are intent based chatbots that use natural language processing to interact with users. They recognize keywords and use machine learning to recognize why the end user is starting a conversation and understand patterns of behavior.


  • Full control of responses 
  • Predictable behavior 
  • Higher quality conversational experiences 
  • Easy to navigate high number of use cases


  • Accumulating and defining what data to train the AI model with 
  • Hard to maintain a large volume of FAQ use cases

Use cases:

Product recommendations

FAQs and queries

Appointment booking

Who’s a good fit?
Brands that have a larger number of use cases or FAQ questions and focus more on the quality of conversational experiences 

AI assistant

An AI assistant is powered by generative AI, and can create various types of content like text, images, audio etc. It allows for a greater volume of FAQ responses and more human-like interactions with users.


  • Generates human-like responses 
  • Minimize the risk of hallucinations by defining the context in which the chatbot can respond
  • Easy to integrate into existing chatbot 


  • Additional costs 
  • Integration to external services 
  • Response time can be slightly longer 

Use cases:

Customer support

FAQs and queries

Lead generation

Who’s a good fit?
Brands that need a chatbot to handle FAQ use cases on a large scale and offer human-like responses. 

AI chatbots are an ideal way to enable faster customer support, while keeping that human-touch to the conversation. With generative AI, you can widen the breadth of use cases and FAQ questions that the chatbot can handle, making customer support faster and more convenient than before.  

What are the benefits of using generative AI in customer service?

1. Improve agent experience

One of the major reasons why AI is being used for customer service is to improve agent experience. Call centers are known for being over-loaded with mundane and repetitive questions that can often be resolved with a chatbot. Offloading these queries to an AI chatbot or AI assistant can help improve agent experience by allowing them to focus on more complex queries and lighten their workload, which gives them more time to offer personalized experiences to users. 

2. Be available for 24/7 support

Using an AI chatbot can enable you to offer customers support around the clock. At any time, when it’s most convenient for them, customers can access support, and get answers to their questions through a chatbot.  

These chatbots enable self-service use cases and allow customers to get answers to FAQs and simple queries without having to interact with a human agent. But, when a chatbot is no longer able to assist a customer, the chatbot can transfer them to a human agent and they get the support they need.  

3. Speed up time to resolution

We covered how GenAI can lower the number of mundane queries to agents and enable self-service query resolution which improves overall customer support. As a result of this, time to resolution is sped up by a long shot.

Since customers can quickly access answers to their queries, and the wait times for call centers are generally reduced, time to resolution drops, making customer support a much more pleasant experience. 

4. Offer multi-language support

Generative AI can make communicating with customers around the world easier than ever. It can be trained on multilingual data to provide fast translations for customer queries and responses. That means that brands can provide 24/7 multilingual support to customers anywhere in the world, in an instant.

How to implement a generative AI chatbot for customer service

// Planning and design

// Development

// Content creation

// Testing

// Deployment

// Maintenance and improvements

Seems like a big job? That’s because it is. But there’s no need to be overwhelmed.

To ensure your AI chatbot is ethical, effective, and meets the needs of your customers, consider teaming up with CX and AI experts at Infobip who are experienced in creating, launching, and maintaining successful customer support chatbots with AI. 

Talk to us about setting up your AI chatbot for support!

Examples of generative AI chatbots for customer support


Croatia’s first fully digital insurance provider, LAQO, wanted to improve customer service with 24/7 availability, accessibility, and personalized support. Together with Infobip’s chatbot building platform Answers and Azure OpenAI Serivce, LAQO was able to launch a generative AI-powered assistant that:

  • reflects LAQOs brand voice and humor
  • offers around the clock support for FAQs and repetitive queries
  • is available in Croatian and English

The results:


of queries are handled by AI assistant


of queries are resolved in 3-5 messages

Megi Health Platform

Megi Health created an AI chatbot with Infobip’s chatbot building platform to help support patients in their medical journey. The chatbot is not meant to replace medical professionals but to provide round the clock support when needed, focusing on four use cases:

  • Record and control blood pressure
  • Track symptoms
  • Patient education
  • Connect with a doctor

The results:


CSAT score


reduction in time to collect data for diagnosis

3 alternative ways to use generative AI in customer service

1. Simplify the configuration of your chatbot and cloud contact center 

Generative AI can help you simplify the configuration of your cloud contact center and chatbot solution. AI technology can help you build parts of your customer support chatbot by making suggestions and responses and message flows, simplifying the entire process. GenAI can also help with the configuration of your contact center and streamlining processes to make agent experience smoother.  

2. Intent analysis

Understanding what your customer is asking for in a support use case is critical. It’s where many customers become frustrated with some simple chatbots. You can train your AI chatbot to understand the intent behind a question, so they can better address and answer the query.

For example, these questions might prompt the same response from a simple, rule-based chatbot:

  1. What time is my flight? 
  2. When does boarding for my flight start? 
  3. How much earlier should I come before my flight? 

With a well-trained AI chatbot, you can avoid any inconvenience and frustration because the intelligent chatbot can understand the intent behind a message and offer a conversational response to improve overall customer support experiences. 

3. Text summarization

Generative AI can also help streamline business processes to make customer support agents more efficient at their job. For example, a customer has been interacting with a chatbot but must be transferred to an agent for further support. AI can help summarize the customer’s conversation with the chatbot so the agent can quickly get contextualized information and avoid asking the customer repetitive questions. This makes their job easier and improves customer satisfaction with your support service. 

What are the challenges of using GenAI in customer service?

Data privacy

There have been concerns around data privacy and generative AI models. Since these algorithms are trained on mass amounts of data, it is critical to ensure none of the data contains sensitive information. You then run a risk of the AI revealing this information in responses or making it easier for hackers to gain access to private data. 

AI hallucinations

Generative AI carries a lot of potential when it comes to providing information fast and accurately. But unfortunately, there is a risk of the algorithm generating false responses and presenting them as facts aka AI hallucinations. This can be countered by limiting the scope of the AI model and giving it a specific role so to avoid it generating false responses. The way you train your AI model will impact how accurate the information it generates is, so ensure you invest the needed time and effort to make sure it is as accurate as possible. 

Addressing hallucinations such as adjusting temperature is the usual first step—i.e. the parameter in the algorithm itself that controls the randomness or uncertainty of the outcomes. Infobip used advanced prompting techniques and carefully prepared content.

Danijel Temraz

Principal Engineer at Infobip

Finding a balance between AI and human interactions (human-in-the-loop)

Since AI can only manage queries it has been specifically trained for, it’s critical for there to still be a human-in-the-loop. An AI chatbot, for example, can easily transfer a customer to an agent when it knows it can no longer help. The challenge is finding the balance of when the right moment is for this transfer to ensure accuracy and maintain customer satisfaction.

What’s the future of GenAI in customer support? 

This technology is continuing to evolve – and quickly. In the blink of an eye we could start to see the capabilities of AI assistants powered by GenAI change from FAQ and query support, to perhaps one day assisting in more complex query resolution.

Will GenAI ever replace human agents for customer support? Probably not. Humans still and will always likely play a major role in training, assisting customers, and ensuring that AI responses are accurate, relevant, and reliable for customer service.

But one thing is for sure, generative AI helps speed up customer service and improves customer satisfaction with brands. Exploring how to implement, train, and launch an AI assistant is beneficial for any brand that is overloaded with simple queries and low CSAT scores.

Discover how GenAI can help predict user churn

and allows businesses to proactively intervene.

How GenAI reduces churn

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May 22nd, 2024
14 min read
Content Marketing Specialist

Monika Karlović

Content Marketing Specialist