Conversational AI

What is Conversational AI?

Conversational AI is an artificial intelligence technology that enables systems to engage in natural language dialogues and understand written or spoken requests. It identifies the intent behind a request, manages the dialogue, and thus automates customer communication, for example in chatbots, voicebots, or voice portals.

How does Conversational AI work?

In customer service, Conversational AI is part of AI automation, which is the automation of customer interactions using artificial intelligence. When a customer calls, Automatic Speech Recognition (ASR) first converts the spoken words into text. With a chatbot, this step is unnecessary because the request is already provided in writing.

In the next step, Conversational AI identifies the intent behind the request. This task is handled by Natural Language Understanding (NLU), which also captures relevant details such as contract numbers, dates, or locations. Based on this, the dialogue management system decides on the next step: a follow-up question, an answer, or a transfer to a human agent.

The system formulates the response using pre-prepared text modules or a large language model. To ensure it is based on verified content, many systems link the language model to approved company data. This process is called Retrieval Augmented Generation (RAG). On the phone, a text-to-speech system finally reads the answer aloud.

Conversational AI and chatbots: the differences

Chatbots and Conversational AI are often used interchangeably, but they refer to different things. A chatbot is an application, while Conversational AI is a technology that a bot can use.

  • ‍Foundation: A rule-based chatbot follows fixed decision trees and reacts to keywords. Conversational AI recognizes the intent behind freely formulated requests, even when they include paraphrasing or typos.
  • ‍Channels: A chatbot works in writing via chat on a website, in an app, or in a messenger. Conversational AI also works over the phone, for example as a voicebot.
  • ‍Context: A rule-based chatbot evaluates each input individually. Conversational AI takes the conversation history into account and draws on previous information provided during the dialogue.

In practice, the line is blurring because many chatbots now use Conversational AI. However, for a few clearly defined questions, a rule-based chatbot is often sufficient and requires less effort to implement and maintain.

Conversational AI, Generative AI, and Agentic AI: the differences

These three terms describe different capabilities of AI systems. In practice, they often overlap.

  • ‍Conversational AI: It conducts dialogues in natural language and is designed for communication with humans.
  • ‍Generative AI: It creates new content such as text, images, or audio, usually based on large language models. Conversational AI uses generative AI to formulate responses freely.
  • ‍Agentic AI: It determines the path to a solution itself at runtime and executes tasks across multiple steps and systems to achieve this, even for unforeseen requests.

A voicebot that records a change of address following a fixed procedure is therefore Conversational AI. The system only becomes Agentic AI when it decides for itself which steps are necessary to resolve an unforeseen request.

What types of Conversational AI are there?

Conversational AI can be distinguished by the channel through which customers communicate with the system. A chatbot answers inquiries in writing, for example on a website, in an app, or via a messenger. On the phone, a voicebot takes on this role: it conducts conversations in natural language and can resolve simple requests completely.

Another form is the voice portal. It replaces the keypad menu of a classic interactive voice response (IVR) system, allowing callers to state their request in their own words. The system recognizes the request and forwards the call to the appropriate department.

Agent Assist should be distinguished from this. This AI support works in the background, providing employees with summaries, knowledge, and recommended actions during a call. Some providers count Agent Assist as Conversational AI, but the application itself does not conduct a dialogue with customers.

Where is Conversational AI used in customer service?

In customer service, Conversational AI primarily handles recurring, clearly structured inquiries. The technology is used in insurance companies, government agencies, and retail, often as the first point of contact in the contact center.

A common area of application is request recognition: the system asks for the reason for the call and forwards the inquiry to the responsible department. It also identifies and authenticates customers using customer or contract numbers before an employee takes over. Conversational AI provides simple information regarding contract details, claim status, appointments, or documents via self-service around the clock.

If all channels run on a common foundation, customers can switch channels without having to explain their request again. This is the core of omnichannel.

What are the benefits of Conversational AI?

With Conversational AI, you shift recurring inquiries into an automated dialogue. As a result, your customers receive answers to standard questions even in the evenings and on weekends. Because the system handles many dialogues simultaneously, you can also manage peak loads without having to increase staff on short notice.

Your employees will also benefit from a reduced workload. Routine inquiries are handled automatically, allowing your team to focus on cases that require experience and judgment. If the issue and identity are already clarified before the handoff, the employee can start working on the case immediately, shortening the duration of the call.

For international organizations, there is the added benefit of multilingualism: Conversational AI answers inquiries in many languages without the need for you to build multilingual teams. Decisions in complex cases remain in the hands of your employees.

What to look for when choosing Conversational AI software

Whether Conversational AI is worth it depends first on your inquiry volume. For low volumes or predominantly individual requests, a well-structured contact form is often the more economical solution. If it is worth implementing, these criteria will help you choose:

  • ‍Data storage: Clarify in which country voice and text data are processed and stored, and who has access to them.
  • ‍Handoff to employees: The software should fully transfer the conversation history and captured data so that customers do not have to repeat themselves.
  • ‍Integration with specialized systems: Only interfaces with CRM, inventory management, or telephony systems enable definitive answers during a dialogue.
  • ‍Response control: Responses should be based on approved company data to reduce the risk of fabricated content.
  • ‍Evaluation: The solution should transparently report recognition rates, self-service quotas, and handoffs to employees.

For starters, it is best to focus on frequent requests with little room for interpretation and clear data sources. This allows you to measure the benefits before adding further use cases.

Conversational AI in practice

Tchibo's customer service receives 2,400 calls every day. In August 2025, together with IP Dynamics, Tchibo replaced a rigid IVR system with the AI voicebot "Voicy." The voicebot recognizes the caller's intent and automatically identifies and authenticates them before an employee takes over. The result is customer calls that are up to 30 seconds shorter and a less burdened staff, as the Tchibo success story shows.

In Dynamic Workload, Conversational AI is part of the AI automation suite. The solution conducts dialogues around the clock across all channels and in 120 languages. Its responses are based on the respective company's data. It transfers complex requests to an employee with full context, whom the Workload Management selects based on skills, availability, and workload. Dynamic Workload is operated in the IPD Sovereign Cloud, exclusively in German data centers.

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Conversational AI FAQ

Häufig gestellte Fragen

AI stands for Artificial Intelligence. It refers to systems that perform tasks typically requiring human intelligence, such as understanding language. In German, the term is often translated as "dialogorientierte KI" or "konversationelle KI," though the English term is commonly used in professional contexts.

ChatGPT is an AI assistant based on a large language model that conducts dialogues in natural language, making it a form of Conversational AI. However, for customer service purposes, a general-purpose AI assistant lacks integration with customer data, specialized systems, and verified knowledge bases. Specialized Conversational AI systems provide these necessary integrations.

Conversational AI is particularly beneficial for high volumes of recurring, standard inquiries that can be clearly structured. Typical examples include contract information, claim status, appointment booking, and document requests. A key requirement is a seamless handover of complex cases to human agents, ensuring the conversation context is preserved.

Conversational AI reaches its limits when a request requires significant judgment or empathy, such as with complaints or complex insurance claims. Furthermore, language models can generate incorrect answers if not connected to verified knowledge sources. A clear handover process to human agents and restricting the AI to approved content mitigate these risks.

The success of Conversational AI is primarily measured by the self-service rate and the escalation rate. The self-service rate indicates the proportion of inquiries resolved automatically, while the escalation rate tracks the number of handovers to human agents. Additionally, first-contact resolution, handling time, and customer satisfaction are measured in a before-and-after comparison. The recognition rate indicates how reliably the system understands user requests.

Generally, yes. According to Article 50(1) of the European AI Act, AI systems that interact directly with humans must be designed so that users are aware they are interacting with an AI, unless it is obvious. This requirement has been in effect since August 2, 2026, and the disclosure must be clear no later than the first interaction.