Chatbot Evolution: On the Way to the Modern AI Agent

Chatbots have undergone a remarkable transformation. What once began as a simple answering system has evolved into highly skilled AI agents capable of conducting complex conversations and providing contextual responses. One area that particularly benefits from this is customer service. More and more consumers are accepting the chatbot in communication, especially if it provides them with benefits such as faster responses.

It’s particularly important as the volume of interactions in customer service is set to increase fivefold by 2027. This makes chatbots all the more important in order to meet the increasing demands of consumers and relieve the burden on service teams. Chatbots are also becoming increasingly essential in the wake of the skills shortage. They are easy to scale and can “grow” with the increasing volume of inquiries without the need to hire new staff.

Chatbots understand the emotional state of customers

Before the breakthrough of chatbots, they primarily took on repetitive, easy-to-solve queries and referred to helpful articles in the help center, thus supporting the service teams. Chatbots still perform these functions today, but are now also able to solve more complex strategic queries and understand customer intent and sentiment. This puts them in a position to provide proactive assistance without customers expressing their problem. They are also perceived as increasingly human, making interactions feel relaxed and natural.

This is also confirmed by the CX Trends Report: 68% of CX decision-makers believe that chatbots are increasingly developing into “AI agents”. Thanks to developments in the realm of generative AI, AI agents could act intuitively, draw specific information from the knowledge base and solve problems three times faster. In addition, an innovative chatbot is now also able to process input such as text, images and sounds.

And that’s not all: the AI agent now knows how to respond individually and personally to the needs of customers and react empathetically – regardless of whether they have a query about a product they have already purchased or are still in the decision-making process, for example. Along the entire customer journey, it adapts responses based on needs, preferences or behavior and proactively recommends new products or services accordingly. These services facilitate cross-selling within a company. In addition, the AI agent is able to convey a company’s branding by adapting its communication to the brand of the respective company.

AI takes the pressure off service teams

Another advantage of using AI in customer service lies in the improvement of processes through automation. Complex, multi-layered processes in multiple languages can be easily mapped through automation; a chatbot can help the human agent to select the appropriate process or recognise exceptions.

A practical example: Eurail, provider of Eurail and Interrail passes for train travelers, works with AI to speed up processes and make them more efficient. In this case, the AI recognizes written customer requests for refunds that do not normally comply with company policy. Certain terms such as “emergency” or “bereavement” are flagged and the requests are sent directly to senior customer service representatives who are authorized to grant refunds in exceptional circumstances. A great help for customers with a sensitive request and at the same time a relief for service teams. The digital agent acts as a proactive advisor that continuously learns from past interactions. It supports human employees in optimizing their workflows, anticipating customer needs and improving future interactions.

Train chatbots in a targeted manner

It is important to remember that training a chatbot requires a structured approach. This ensures that the bot can respond efficiently and accurately to user queries. Before starting the training, it is essential to clearly define the specific goals and use cases.

In order to optimally train a chatbot, extensive data sets from real conversations and text-based interactions are also required, which must of course be anonymized before use. This data must be selected specifically for the chatbot’s area of application. The AI-supported chatbot is trained with annotated data in a supervised learning process in order to master specific tasks such as recognizing intentions. Continuous feedback from real interactions constantly improves the chatbot and optimizes the quality of interactions.

Positive ROI through the use of AI agents

Even if some companies are hesitant for various reasons such as data protection concerns or a lack of knowledge, AI agents offer significant benefits for both customers and customer service teams. This can also be proven by figures: The results of the CX Trends Report show that 83 percent of CX leaders surveyed worldwide who use generative AI in CX report a positive ROI – including faster response times and an increased CSAT score. The study also shows that 70 percent of CX managers are already redesigning their customer journey with the help of generative AI tools. Over the next few years, the trend could even see 80 percent of interactions being fully automated.

Matthias Göhler

is Chief Technology Officer, EMEA, at Zendesk.