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Chatbots Vs Conversational AI: What’s The Difference?

Published on 13/02/2023 by Ojasvini and Anna Hammond

This article was originally published on 11/08/2020.

Businesses might be aware of the limitations of chatbots when it comes to engaging with customers. Getting to the next stage of customer service automation might involve using conversational AI platforms. This article explains what is conversational AI and will cover the topic of chatbot vs conversational AI.

Conversational AI distinguishes a basic rule-based chatbot from an advanced one

What are chatbots?

Chatbots are typically computer software that mimics or replicates human conversations to conduct some service. As chatbots are automated, they tend to perform tasks faster than humans. NLP (Natural Language Processing) technology is basically a chatbot's crux and helps it understand user requests and respond accordingly. Technology like Siri, Alexa and Google Assistant are typical examples of chatbots used daily.

There might be many different types of chatbots used by businesses per their needs. Some common types include skills chatbots (that follows orders and perform an action directly), support chatbot (that is made to resolve a fixed problem), assistant chatbot (that responds to customers’ queries instantly) and transactional chatbot (created to perform a transaction on behalf of a customer).

How does conversational AI work?

Conversational AI is a technology that can identify and respond to both text and speech inputs. In customer service, this technology is typically used to interact with customers more humanly. This interaction can happen via a conversational AI bot in a messaging platform or a voice assistant over the phone. These conversational AI platforms help deep learning algorithms understand human language and effectively identify user intent.

In addition, conversational AI can be typically referred to as a higher-level chatbot concept. They frequently mix AI technology with other processes and workflows, including machine learning, natural language processing, voice recognition and more. In its true nature, conversational AI as a term can be used to distinguish between a basic rule-based chatbot (one that can only complete a limited number of tasks) and an advanced one (that can potentially solve complex human problems).

Conversational AI vs chatbots: Key differences

Chatbots and conversational AI are often used synonymously. However, there are some key differences to consider, such as:

Basic chatbots only perform limited tasks

Suppose customer types in a simple question, and a basic chatbot responds from its little repository of pre-defined responses. Ideally, a chatbot needs to have an explicit example of every way a customer phrase a question, but a basic chatbot might need help performing complicated tasks.

On the other hand, a conversational AI system uses natural language recognition and is aware that a question may take several different forms, meaning AI bots can take glitches, such as spelling mistakes, in their stride—whereas a basic chatbot might fail to recognise the query.

Conversational AI helps in customer service enrichment

Effective conversational AI systems can mimic emotions, such as empathy or friendliness, to build a relationship with the customer through more personal interaction, whereas a basic chatbot typically enacts more robotic.

For instance, a simple chatbot might only respond to the first user request and ignore the next. An artificial intelligence bot, on the other hand, can respond to both requests and respond to them simultaneously during the conversation.

Initial investment in conversational AI platforms is higher

In case a specific business decides to change its products or services, the chatbots would be required to be retrained. Therefore, opting for basic chatbots might not be a practical proposition for those businesses that constantly change. So, although the initial investment in AI may be higher, maintenance costs are eventually less when it comes to a chatbot vs. conversational AI. The additional costs can be reduced, and the human employees can be shifted from repetitive enquiries to more complex support tasks.

Conversation AI tools create a repository of customer data

Conversational AI helps enable businesses to capture data from calls and customer interactions. It allows a business to link a current transaction to a past interaction or to prompt for further business during an existing call. But this data use comes with responsibilities to keep customer data safe. Therefore, a business might need to include its IT security advisers early in building its conversational AI system. This ensures that transactions are appropriately encrypted and all existing IT governance rules are applied to the new system.

As these systems can draw on multiple data sources, it might also be essential to know who owns which data, who can use it, and for what purpose.

Conversational AI can benefit customers and businesses

Conversational AI might be a game-changing technology for the customer service industry. The field can be seen developing rapidly. Therefore, businesses should ideally opt for a platform or system that can allow them to potentially scale up quickly and take advantage of the latest developments. A growing power of conversational AI or a much-advanced chatbot would lie in its ability to collect and use data for the benefit of both customers and businesses.

Looking for conversational AI software? Check out our catalogue!

This article may refer to products, programs or services that are not available in your country, or that may be restricted under the laws or regulations of your country. We suggest that you consult the software provider directly for information regarding product availability and compliance with local laws.

About the authors

Ojasvini is a content analyst who specializes in finding the best software options. She analyses market trends and uses her diverse background to write for multiple audiences.

Ojasvini is a content analyst who specializes in finding the best software options. She analyses market trends and uses her diverse background to write for multiple audiences.

Anna was a content analyst for GetApp.

Anna was a content analyst for GetApp.