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. 

Read on to find out how AI can benefit customer service, examples of uses, and challenges to look out for. 

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. 

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.  

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. 

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.

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.

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

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.

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. 

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.  

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.  

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:

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. 

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. 

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

The results:

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:

The results:

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. 

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. 

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.

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.