The term Conversational AI is appearing more and more frequently in the media. Conversational AI is becoming increasingly important, especially in connection with chatbots. In the following article, I explain when chatbots need Conversational AI and where the added value of bots with Conversational AI lies.
If you search the Internet for Conversational AI, you will find countless definitions and explanations. I find the following self-formulated definition very apt: “Conversational AI is the term used to describe AI-supported, automated dialog systems. This is a form of artificial intelligence (AI) that enables users to engage in automated, natural-language dialog via chatbots or voice assistants.”
How does Conversational AI work?
Conversational AI uses artificial intelligence and machine learning to enable human-like conversations between computers and users. It is based on advanced speech recognition and processing technologies. By using voice or text input from users, the system analyzes the context, recognizes the intention of the request and generates appropriate responses. It learns from the interactions and continuously improves to have more natural and personalized conversations. Conversational AI can be used in voicebots, chatbots and other applications to enable human-like dialogs.
What is the difference between conversational AI and a “normal” chatbot?
As the name suggests, rule-based chatbots work with fixed rules. With this type of chatbot, the dialogs are defined in advance and users only ever have the option of being guided through the conversation using buttons. Depending on which button a user clicks, the chatbot takes the predefined conversation path.
The AI Revolution platform, for example, uses a rule-based chatbot to guide users through the website and also generate new leads for the newsletter.
It’s different with AI-based chatbots. This type of chatbot works with conversational AI. This makes it possible for users to communicate their query to the chatbot using free text (written or verbal) and for the chatbot to record and categorize the answer itself. As soon as the bot has “understood” the content of the request, it searches for a solution in its own database.
Chatbots with conversational AI can therefore usually cover a wider range of use cases than simple rule-based chatbots. This does not mean that rule-based chatbots no longer make sense(more on this here).
The advantages of conversational AI
Conversational AI offers a number of advantages. Here are the most important advantages of Conversational AI:
Improved user experience
Conversational AI enables natural and interactive dialogs between users and computers. This improves the user experience and creates a feeling of personal interaction.
Increased efficiency
By automating interactions, repetitive tasks can be completed faster and more efficiently. Conversational AI can process requests immediately, provide information or carry out transactions without users having to wait for manual processing.
Round-the-clock availability
Conversational AI systems can be available around the clock to answer queries and provide support. Users can access the services at any time and from anywhere, without having to adhere to specific opening hours or schedules.
Scalability
Conversational AI systems can scale with increasing demand without the need for additional staff. You can process a large number of requests simultaneously, increasing capacity and flexibility.
Personalization
Conversational AI systems can store user profiles and preferences in order to offer personalized interactions. They can provide information and recommendations based on the individual needs and preferences of users.
Customer service improvement
Conversational AI can optimize customer service by providing quick and accurate answers to frequently asked questions and facilitating access to information. This leads to improved customer satisfaction and a reduction in waiting times.
Data analysis and insights
Conversational AI systems can collect and analyze data about user interactions. This data can provide valuable insights into user behavior, preferences and trends that can be used to inform business decisions and customer experience.
The challenges of conversational AI
Natural language processing
The development of Conversational AI requires advanced speech recognition and processing technologies to enable human-like dialogs. Mastering nuances, ambiguities and language variations is a challenge.
Contextual understanding
Conversational AI must understand the context of a conversation in order to generate appropriate responses. The ability to take previous interactions and information into account requires complex algorithms and data models. Models such as GPT-3.5 are usually already able to understand context.
Integration with existing systems
Integrating conversational AI into existing IT infrastructures and systems can be complex. A seamless connection with backend systems, customer databases and other platforms requires careful planning and development.
Quality control and continuity
Conversational AI must be constantly monitored and updated to maintain the quality of interactions. Fixing bugs and ensuring a consistent user experience are challenges to ensure trust and satisfaction.
Areas of application for conversational AI at a glance
Customer service and support
Conversational AI is often used in customer service to answer customer queries, solve problems and offer support. Chatbots and voicebots enable fast and efficient interaction with customers by answering frequently asked questions, forwarding inquiries or even carrying out simple transactions.
E-commerce and distribution
Conversational AI plays a crucial role in e-commerce and sales. Chatbots can help customers select products, make recommendations based on their preferences and facilitate the ordering process. They also offer upselling and cross-selling opportunities to increase sales.
Personal assistants and smart home integration
Conversational AI is used in personal assistants such as Amazon Alexa, Google Assistant and Apple Siri. These voice assistants enable voice-controlled interaction with networked devices in the smart home, such as controlling lighting, thermostats or playing music.
Healthcare and telemedicine
Conversational AI is increasingly being used in the healthcare sector to improve patient communication. Chatbots can answer questions about symptoms, arrange doctor’s appointments or provide information about medication. By using conversational AI, patients can access resources and medical support more quickly.
When is the use of conversational AI worthwhile?
When companies want to implement a new chatbot, the question of whether a rule-based or AI-based chatbot should be developed usually arises right at the start of the project. Both forms have their justification. First and foremost, companies need to be clear about what they want to achieve with the chatbot. The decision for or against the use of Conversational AI then depends on this. Here are a few examples of when it makes sense to use conversational AI.
Classifying user queries, especially in customer service
When chatbots are used in customer service, the requests usually vary greatly. Many chatbot use cases from customer service can therefore not be mapped at all with rule-based chatbots. If the possible questions that a user can ask are too broad, it makes sense to ask the user an open question first. The user’s response is then categorized using Conversational AI and answered accordingly. It is also possible that only the initial question is recorded using Conversational AI and the rest of the conversation is then rule-based again.
Clara, Helvetia Insurance’s chatbot, uses conversational AI primarily to categorize the user’s first query. It first asks the user an open question until it has understood what the query is about. It then offers the user various options that can be selected using buttons. Conversational AI helps to categorize the “problem” first. Using buttons and predefined rules, the conversation then continues and should lead the user to their goal as quickly and directly as possible.
Internal employee chatbots
Chatbots that are developed for your own employees are usually not rule-based. This type of chatbot usually only brings added value when it can analyze and process user requests using conversational AI. The requests that employees have are very broad and depend on the situation. A rule-based chatbot that guides employees through the same conversation over and over again can usually provide little support here.
Situational or contextual conversations
When two people talk to each other, we are used to referring to previous conversations. It is similar with situational conversations. For example, when we talk about the weather, it is logical for both parties that we usually talk about the weather in the place where we are. It would be different if we talked about our past vacations and mentioned the weather.
Rule-based chatbots cannot take context and situation into account. They always behave in the same way or always respond according to the same patterns. If a chatbot uses conversational AI, it is also possible for it to make reference to the context and situation in which the user finds themselves.
Conversational AI in practice: tips for companies
Conversational AI is certainly a technology that will continue to develop rapidly over the next few years. It can be assumed that at some point, companies will no longer be able to avoid using conversational AI. This makes it all the more important to gather initial learning and experience with Conversational AI at an early stage. It is often enough for companies to start with a small use case and then steadily expand it. Tools such as the GPT Canvas or the Virtual Agent Guide help to create a successful concept.
Conclusion: Conversational AI – the future of human interaction with AI
The future of conversational AI promises exciting developments. Advances in speech recognition, machine learning and artificial intelligence will lead to even more natural and context-aware dialogs. Conversational AI is increasingly being used in various industries, from healthcare to education and financial services. Integration with other technologies such as augmented reality and virtual reality opens up new possibilities for immersive and interactive experiences. Personalization will be driven further by Conversational AI being better able to understand individual needs and preferences. Data protection and security will continue to be a priority in order to ensure user confidence. Overall, Conversational AI will play an increasingly important role in our daily lives by enabling efficient, personalized and seamless interactions between humans and machines.
Conversational AI – Frequently asked questions & answers
What is Conversational AI?
Conversational AI refers to technologies that enable machines to understand and respond to natural language in order to have human-like conversations.
How does Conversational AI work?
Conversational AI is based on artificial intelligence and uses algorithms to analyze voice input, understand context and generate appropriate responses.
What areas of application does Conversational AI have?
Conversational AI is used in customer service, virtual assistants, e-commerce, smart homes and many other areas to enable interactive and personalized user experiences.
What are the advantages of Conversational AI?
Conversational AI improves the user experience, increases the efficiency of companies and enables scalability of customer service capacities.
What are the challenges in implementing conversational AI?
Challenges include natural language processing, context sensitivity, personalization and compliance with ethical and data protection standards.
How will Conversational AI develop in the future?
The future of conversational AI lies in technological advances, improved machine learning and seamless integration into our everyday lives.
