Conversational AI: Function and practical applications

Jan Schäfer

For a long time, chatbots were unusable for many companies. Communication with them felt too much like "robot". However, new AI models such as Generative AI and LLM have changed this dramatically. Used correctly, conversational AI can help companies automate important processes. The technology enables machines to talk to humans in a natural way. To do this, it uses voice-based interfaces such as chatbots, virtual assistants and voice assistants.

We explain which components Conversational AI consists of, how you can use it in practice, and how you can benefit from it. And we show step by step how you can properly feed conversational AI with data for use.

Conversational AI in numbers

Conversational AI technology has had an impressive development that will not stop in the near future:

  • The number of interactions handled by conversational commerce agents has increased by up to 250 percent in various industries since the beginning of 2020.
  • While only 29 percent of all companies used AI in digital marketing in 2018, 84 percent did so in 2020.
  • In February 2022 gave 53 percent of all adults in the U.S. report having communicated with a customer service chatbot in the past year.
  • In 2022, 3.5 billion chatbot apps were used.
  • By 2030, Conversational AI will have a global market value of just over $32 billion.
conversational ai

Conversational AI Definition

Conversational AI is an advanced technology area that leverages a compendium of techniques and methodologies, including but not limited to machine learning, Big Data analytics, natural language processing, speech recognition and synthesis, and dialogue management, to facilitate interactive and meaningful communication with chatbots, virtual agents, and voice assistants.

Conversational AI involves complex processes such as intent recognition, entity extraction, and context management in a variety of languages.

This makes it possible to understand the user's input and generate differentiated responses. Despite these capabilities, it should be noted that understanding and generating certain subtleties of human language, such as sarcasm or culturally specific allusions, can be challenging.

Conversational AI is finding its way into various industries such as the public health and e-commerce wide application. Their potential lies in their ability to learn and improve their performance over time, increasing the quality of interaction and enhancing the user experience. 

Conversational AI vs. Chatbot

Conversational AI is an umbrella term for algorithms and models that enable machines to have human-like conversations. Chatbots are specialized applications or systems that use conversational AI to communicate with users.

It is important to distinguish between a rule-based chatbot and an intelligent chatbot. A rule-based chatbot does not work with artificial intelligence. It is based on predefined rules. In practice, this means that with this type of chatbot, users are generally not able to enter text freely, but are limited to certain questions and answers. Rule-based chatbots are thus not a manifestation of conversational AI. Intelligent chatbots, on the other hand, are based on AI. Users can enter text freely and receive a suitable response. The bots automatically learn and develop through every communication.

conversational ai components

Components of Conversational AI

Conversational AI consists of several components that work together to enable effective, interactive communication between humans and AI systems. These components include:

Machine Learning

The ability to learn and improve from experience is an important component of Conversational AI. With Machine Learning conversational AI can continuously optimize its responses and actions to achieve better results and adapt to user preferences. 

A decisive role is played by natural language processing (NLP) one.

NLP is responsible for understanding and processing human language. It analyzes user input, extracts relevant information, and translates it into a form that can be processed by the AI. NLP uses algorithms and models to understand syntactic, semantic, and pragmatic aspects of language.

Speech generation

Speech generation produces understandable, natural-sounding, and appropriate responses. It can use prefabricated text modules that are dynamically assembled or create machine-generated texts based on context and user specifications.

User interface

The user interface is the channel through which interaction between users and Conversational AI Technology takes place. It can be a voice or text interface that allows users to make requests or give commands. The user interface can be integrated into various environments such as websites, mobile apps, chatbots, or voice assistants.

conversational ai use cases

Conversational AI Examples

The current rapid development of AI has led to companies being able to use conversational AI in more and more areas. Here are 6 Conversational AI examples from the field:

1. Customer Support

Conversational AI is increasingly being used in customer support to enable automated, personalized and efficient interactions with customers. By using chatbots or digital assistants, companies can answer questions, provide support and solve problems around the clock. This leads to improved customer satisfaction while reducing the workload of the support team.

2. Internet of Things (IoT)

Conversational AI enables the seamless integration of voice control into IoT devices. This allows users to control their devices and systems by voice command, greatly improving ease of use and user experience. From smart home appliances to industrial applications, AI can make interaction with IoT devices more intuitive and efficient.

3. Search Engines

Thanks to Conversational AI Technology, search engines can provide a more natural and user-friendly interaction. Instead of typing keywords, users can ask their search queries in natural language and get relevant results. This facilitates information retrieval.

4. Human Resources

In human resources, conversational AI can assist in recruiting and onboarding employees. Chatbots can answer applicants' questions, provide information about open positions, and facilitate the hiring process. In addition, chatbots can also assist with internal inquiries, such as vacation requests or employee training, thus increasing efficiency in human resources.

5. Computer Software

Developers can use Conversational AI Models to create more natural and interactive user interfaces. This allows users to interact with the software via voice commands and perform complex tasks more efficiently.

6. Voice Assistants

Voice assistants such as Siri and Alexa are ubiquitous examples of the use of conversational AI. They offer users a wide range of services - from managing their calendar to controlling smart home devices.

conversational ai benefits

3 important Benefits of Conversational AI

Companies benefit from the use of AI:

1. More Efficiency

Conversational AI helps increase efficiency. Automated processing of customer queries reduces the workload of employees and allows them to focus on more complex tasks. Routine tasks such as answering frequently asked questions can be taken over by chatbots, resulting in improved productivity and cost savings.

2. Better Data Analysis

Conversational AI enables better data collection and analysis. By interacting with customers, the technology generates valuable data that helps businesses better understand customer needs and make informed business decisions. Analyzing this data enables companies to identify trends and continuously improve their products or services.

3. Scalability

Chatbots can handle customer queries on a large scale without compromising the quality of the interaction. This enables companies to process simple customer queries automatically and invest the time of experts in valuable customer relationships, allowing them to operate efficiently even as customer demand increases.

3 Challenges of Conversational AI

Conversational AI undoubtedly brings numerous benefits to companies. At the same time, however, the technology also brings these challenges:

1. Voice Input

Regardless of whether it is written or spoken input, Conversational AI repeatedly has problems with speech input. For example, background noise, accents, and dialects are especially troublesome for the AI. In addition, it is difficult for the technology to understand human aspects related to speech input. For example, it can only rarely understand cultural circumstances, tone of voice, emotions or sarcasm and react accordingly.

2. Privacy and Security

Conversational AI uses personal information to provide a personalized experience. This presents a challenge, as misuse of this data can lead to serious consequences. Companies need to ensure that adequate data protection measures are in place to protect user privacy and avoid potential security breaches.

3. User Concerns

Users may have concerns about the use of conversational AI. Some worry that the technology could replace their jobs. This concern is particularly relevant in areas such as customer service, where conversational AI tools can replace human interactions. Companies therefore need to ensure that they position technology as a complement to human employees, augmenting their capabilities rather than replacing them.

Another user concern is the lack of transparency of conversational AI tools.

Many people want to understand how the tools make decisions and what data they use.

They said they were concerned that the technology could deliver erroneous results due to biases or insufficient information. Companies therefore face the challenge of developing transparent systems that can be explained in order to gain the trust of users. To this end, the findings of the latest research offer various ways to ensure the quality of the conversation even with very large AI systems.

Instructions: Prepare Conversational AI in 4 steps

For conversational AI to deliver high value to users and businesses, it must be thoughtfully trained with the right data. We show how companies can prepare an intelligent chatbot to effectively communicate with users and meet their needs. A conversational AI tutorial in 4 steps:

  1. Find list of frequently asked questions (FAQs)

    Start with a thorough analysis of your (potential) customers' needs and requirements. Identify the most frequently asked questions. This can be done through user surveys, customer service records or feedback mechanisms. Capture these FAQs in a structured list.

  2. Develop goals for Conversational AI

    Analyze FAQs and support tickets and identify the most important goals your users want to achieve. Identify the intentions behind the questions and formulate clear goals for your Conversational AI. For example, a goal might be to provide information, offer support, or help users perform specific tasks.

  3. Understand and develop relevant keywords

    Based on customer goals, identify the keywords that appear in your users' questions and goals. This will help you better understand the vocabulary and context. Create a list of these words. This will serve as the basis for building the Conversational AI Model.

  4. Create dialog flow

    Based on the captured FAQs, goals, and relevant keywords, you can now create the dialog flow of your Conversational AI. Develop a structure that enables your AI to recognize user intent, generate the right answer, and respond to the user in a natural and understandable tone.

Continuously improve Conversational AI

With this Conversational AI tutorial, you'll lay a critical foundation for automated customer communication. You should then continuously monitor and improve the AI. Collect feedback from users and analyze their interactions to identify weaknesses of the AI and optimize the dialog flow. In this way, you develop Conversational AI that is able to communicate effectively with your users and meet their needs.


What is Conversational AI?

Conversational AI is a technology that understands and responds to natural language to enable human-like conversations with users. Through the use of algorithms and machine learning, it enables computers to interact with users in real time and answer queries by analyzing speech and generating contextual responses.

What is the difference between a conversational AI and an intelligent chatbot?

A conversational AI is an advanced system that can have human-like conversations and understands natural language. An intelligent chatbot is a manifestation of AI in practice. It uses AI algorithms and systems to interact with users.

What Conversational AI examples are there?

Real-world examples of Conversational AI include chatbots for customer support, virtual assistants for scheduling appointments, voice-controlled smart home systems, and personalized shopping experiences in e-commerce platforms. Conversational AI revolutionizes communication between humans and machines to improve efficiency and user experience.

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