What is Conversational AI ?

What Is Conversational AI?

Conversational AI technology enables communication between humans and computers in a far more natural way than was hitherto possible. Not only can interaction be carried out by typing messages, but also by spoken words and by a combination of both.

In addition to chatbots and virtual agents, there are many more applications of Conversational AI. They can not only interpret the user’s intent, identify relevant information and maintain context, but also retrieve and generate appropriate data.

Together, several technologies enable Conversational AI to go beyond simple chatbots. Natural language processing (NLP) enables conversational systems to handle human language, and machine learning (ML) enables systems to learn from interaction and become increasingly accurate over time.

Conversational AI supports scenarios where users are asking questions, searching for information, getting help or even completing tasks with the support of the AI system.

How Does Conversational AI Work?

A Conversational AI system starts with a user’s input. That can be a written text message or even a spoken request for example.

Before a system can come up with a suitable answer, it must first have understood the user’s input. For messages typed in by a user, this involves Natural Language Understanding (NLU). The input from a voice-based dialog would first have to go through a Speech Recognition process and be changed into text that can be processed by the system.

This response can contain information retrieval, generative responses, and even more interaction through clarification requests, or even the integration with other applications and services.

The Dialogue Management component manages the conversation by determining the correct response or action based on the input from the user. The Natural Language Generation component generates human language-based responses.

In summary, ensuring quality interactions beyond the AI model itself such as the data, knowledge sources, conversation design, integrations and context management in a large number of applications is crucial to deliver good Conversational AI experiences.

What Are the Main Components of Conversational AI?

Several technologies are typically used to implement a Conversational AI system.

Not every application needs every of these components. As an example, a text-based assistant does not need any of the speech recognition / text-to-speech components. On the other hand, a voice assistant would need both of these.

Where Is Conversational AI Used?

Conversational AI is used in a wide variety of applications including customer and patient facing services and internal business operations as well as digital applications. As conversational systems have become capable of working with business data and with connected applications, the role of Conversational AI has expanded.

Customer Service

Customer Service – most common application of Conversational AI.

AI assistants can respond to customers’ queries and carry out tasks, such as providing information and guiding customers through processes, as well as managing support cases and gathering information prior to passing the customer over to a human for more complex issues.

In scenarios where support teams handle lots of identical questions and cases, routine interactions with customers are handled by Conversational AI. Human support agents deal with more complex issues, requiring human judgment and other skills, in individual conversations with customers.

Virtual Assistants

Virtual assistants are also Conversational AI-based applications which can support users searching for information and even completing tasks with support where needed.

The main functions of a conversational interface for a virtual assistant are to respond to user queries and/or complete tasks on behalf of the user, by interacting with applications, services etc. that the virtual assistant is set up to work with.

An internal assistant could return information from internal company resources that would otherwise require a person to search through various systems one by one.

Healthcare

Healthcare organizations are using Conversational AI for patient communication as well as for administrative tasks within the organization.

Examples of applications in the healthcare sector include making appointments, answering frequent questions, guiding patients to the appropriate department or clinic, and distributing approved information to patients.

The functionality to handle sensitive health-related data must include appropriate measures of privacy, security as well as human oversight to ensure adequate decision making.

Banking and Financial Services

Banks and financial services can use conversational systems to manage customer requests and interactions that occur on a regular basis.

Customers can use conversational systems to get account information, ask about products, learn about processes and get assistance with common banking questions.

Activities of a more sensitive nature require greater controls. When conversational systems are used to manage financial accounts online, for example, for authentication, authorization, for data protection and for confirmation.

E-commerce

Conversational AI can support several stages of online shopping. 

Customers can ask about products, availability, delivery information, orders, returns, or product differences. When the system is connected to current business information, responses can be based on actual product and order data rather than generic answers. The same interface can potentially support actions as well, depending on the systems it is connected to.

In addition to inquiries, the same interface can also support actions.

Human Resources

HR departments can also use the conversational assistant to reply to employees who have recurrent questions about leave, benefits, on boarding, payroll and so on.

A conversational assistant can read out approved sources of information to employees to help them deal with their queries and direct them to where they can find the information that they require.

Reducing the amount of time spent on repeated employee questions while enabling them to search for information faster.

Education

The use of Conversational AI in Educational Institutions is for student support and administrative communication.

Students can use conversational systems to find information on matters like courses, timetables, admission, assignments and on-campus services.

The same interface can be used to support learning too, explaining for example a concept and answering subsequent questions on the matter.

Accessibility

Conversational interfaces for digital services.

Speech recognition, voice interaction, text-to-speech, and language capabilities can make some services easier to use for people who find conventional interfaces difficult to navigate.

What Are the Benefits of Conversational AI?

Benefits of using Conversational AI.

Faster Access to Information

The main benefit for using Conversational AI is that users can get the information they need faster.

Information can exist across a large number of knowledge bases or even across digital resources. This makes it difficult for users to find the information they are searching for. However, a conversational interface can make it easier for users to find the information they are searching for.

24/7 Availability

Conversational systems can handle supported requests outside normal working hours.

A round-the-clock support solution for customer services. Here, Conversational AI can assist users with routine queries and problems at times when human customer support agents are not available to assist, notes IBM in its customer service application of Conversational AI.

Handling High Volumes of Requests

A conversational system can handle interactions with many users, at the same time.

This allows the AI system to handle a large amount of user interactions at the same time. This can also be useful during peak hours of activity, such as when a store has a sale or when a company’s website is experiencing a high volume of activity.

Reduced Repetitive Work

Teams often spend significant time answering the same basic questions.

A conversational AI can handle routine repetitive conversations and allow staff to concentrate on more complex work.

More Convenient Interactions

Users can describe what they need in natural language instead of learning to use another application.

This can make digital services easier to use, for example when users first of all do not know the right terms and only after that the right places where to look for the information that they are looking for.

Consistent Responses

An example of how a conversational system connected to reliable, maintained information sources can provide consistent answers to recurring questions.

For example, Conversational AI could be used to give consistent answers to customer queries about products or internal policies and procedures.

Better Access to Business Knowledge

For Enterprise use of Conversational AI, it can act as an interface to information in documents, knowledge bases, databases, and even on internal Business Applications that employees use.

Of course, employees don’t have to search for information themselves. They describe what they need and the system retrieves the relevant content.

What Are the Challenges of Conversational AI?

Conversational AI has many limitations to date. In part, because human language is extremely ambiguous, and, furthermore, human communication is often unpredictable and irregular.

Every user types, speaks or writes differently. Some use slang and abbreviations, other users write complete sentences. Moreover, every user is different and can have a different accent. Also regional expressions and words with multiple meanings can be a problem. Voice systems even have problems with background noise, audio quality and pronunciation of words.

Longer conversations are also problematic as the system must maintain context. Even if individual messages are correctly understood, they can be incorrectly connected in longer conversations.

The Conversational AI solution also treats privacy and security with the utmost care. Appropriate access rights and data protection are guaranteed at all times.

Accuracy. It’s easy to generate convincing, yet incorrect information. This requires appropriate testing, monitoring and escalation procedures, especially if the AI is to be used for high-impact applications.

Conversational AI vs. Traditional Chatbots

A traditional chatbot follows fixed paths through a conversation and will typically rely on predefined responses to customer inquiries and statements. This type of system is based on a set of predefined rules, keywords, and statements that are linked to certain actions or replies, which in turn form the basis of the entire conversation.

Many are blurring as more advanced AI capabilities are being incorporated into chatbot technology. What really matters to assess the quality of conversational AI applications is the actual functionality provided by a given system rather than the label of ‘Chatbot’ or ‘Conversational AI application’.

What Is the Future of Conversational AI?

Conversational AI is becoming increasingly intertwined with Generative AI, large language models, multimodal systems, enterprise data, and a multitude of AI agents. Instead of answering a question in a conversational AI application to stop, a number of systems are working step by step, including interaction with other applications.

In the near future, conversational interfaces will not be limited to answering simple questions and will be able to retrieve information, to reason over given context, and to perform given tasks, either completely or with support.

Rather than assessing the ability of the AI to interact with human beings in terms of natural conversation, the quality of the Conversational AI can be assessed in terms of the quality of the tasks that it is able to complete on behalf of a human.