Introduction
Chatbots and Conversational AI are some of the most popular buzzwords in the modern ecosystem of automation and artificial intelligence-based business processes. Even though these technologies and products differ head to toe, people often use them interchangeably. Let’s delve into the distinction between chatbots vs conversational AI.
Chatbots are the conventional replacement for the human workforce that eliminates the requirement of human efforts for repetitive tasks in a controlled response manner. Identifying prompts and a range of responses is limited to a pre-defined set, and these rules bind these chatbots’ functionality.
Conversational AI is a relatively new revolutionary tech development that has overtaken the world. These are based and built on artificial intelligence and can draw responses from multiple prompts without having any set of predefined rules to govern how they will respond to a command/prompt.
This is just the tip of the iceberg when it comes to understanding the differences between Chatbots vs Conversational AI solutions. Hang on to this article with us as we uncover the basic differences in a head-to-head comparison of Chatbots vs Conversational AI.
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What is a Chatbot?
A chatbot is a computer program designed to mimic human conversation and automatically respond to user commands or prompts. Built using natural language processing (NLP) and machine learning (ML) algorithms, chatbots can understand and interpret text-based inputs from users and generate relevant responses in real time.
Chatbots are built using a variety of programming languages and frameworks, depending on the specific use case and platform they are intended for. They are typically integrated with messaging platforms such as Facebook Messenger, Slack, or WhatsApp or deployed as customer support tools on websites and mobile apps.
To function, chatbots use a combination of pre-programmed responses and machine learning algorithms to analyze user input and generate relevant responses. They rely on NLP algorithms to understand user intent and extract relevant information from the input, such as keywords or phrases.
They then use this information to generate a response that is relevant to the user’s query. The limitation with chatbots kicks in this part; whenever a user enters a prompt that is not predefined in the data set of the chatbot, it loses its ability to produce a response for that and moves on without answering the user’s command.
For Example, look at these prompts and responses:Â
Chabot:Â How can I help you?Â
User:Â I would like to book a hotel in Navi Mumbai, but I have lost my physical Ids.Â
As you see in the user’s prompt, there are two requests one is about booking a hotel, and the second is about the unavailability of physical Ids. In this case, the first request is very common, and thus it is most likely to be in the predefined dataset of the chatbot.
But, the second prompt being not-so-common among the visitors of that hotel booking site, there will not be a predefined response. Any conventional chatbot will likely ignore this request and answer only the first command.
Even if the users repeat this query, the chatbot will simply express its inability to produce a suitable response resulting in a frustrated customer and moving on to another place offering the same service.
Types of Chatbots
Based on the type of usage and development prerequisites, chatbots can be generally classified into four basic types. Here is a detailed explanation of different types of chatbots, their functioning, and their limitations.
1. Rule-Based Chatbots
These chatbots are programmed with predefined rules and responses to specific user inputs. They are typically used for simple tasks like answering frequently asked questions or providing basic customer support. Rule-based chatbots are easy to implement and require minimal training data, but they cannot learn and adapt to new situations.
2. Conversational Chatbots
These chatbots are designed to simulate natural human conversation and provide a more engaging user experience. They use advanced NLP and ML algorithms to understand user intent and generate contextual responses.
Conversational chatbots can be used for various applications, including customer service, marketing, and sales. We will discuss these conversational Chatbots in detail in the next section.
3. Voice-Activated Chatbots
These chatbots are designed to interact with users through voice commands, typically using platforms like Amazon Alexa or Google Home. They use advanced speech recognition algorithms to understand user input and generate relevant responses.
Voice-activated chatbots can be used for various applications, including home automation, entertainment, and personal assistant services.
What is Conversational AI?
Conversational AI is an advanced technology that uses natural language processing (NLP), machine learning (ML), and other AI techniques to enable computers to interact with humans through natural language conversations.
It allows businesses to build sophisticated chatbots cum virtual assistants to understand human language, interpret user intent, and respond in real time. Conversational AI uses various techniques to analyze user input and generate relevant responses.
NLP algorithms parse user input and extract key information, such as keywords or phrases. ML algorithms are then used to generate a response relevant to the user’s query. Conversational AI also leverages other AI techniques like sentiment analysis and entity recognition to understand user intent better and provide more accurate responses.
How would a conversational AI assistant handle the same customer query as before? Her is an example.Â
Conversational AI Assistant:Â How can I help you?
User:Â I would like to book a hotel in Navi Mumbai, but I have lost my physical Ids.
In this case, the conversational AI will identify both queries, unlike the case with the chatbot, where it could not handle when a query came outside of its predefined instruction range. Here, the conversational AI will identify the stress in the tone.
With the help of advanced NLP and ML algorithms, the virtual assistant will generate a response catering to the user in a more personalized manner. With conversational AI, the answers are not fixed in the backend dataset but are formed to give an individualistic personalized experience.
Understanding the upper hand of Conversational AI over Rule-based Chatbots
One of the key benefits of conversational AI is its ability to improve customer engagement and satisfaction by providing a more natural and intuitive interface for interacting with businesses. Unlike chatbots, personalized responses and customized answers are also an add with conversational AI assistants.
The responses are generic and remain the same every time a user visits your website/ application or social media marketplace. Conversational AI can help increase customer loyalty and retention by allowing businesses to provide 24/7 support and reduce response times, improving overall customer satisfaction and delivering an enhanced customer experience.
Another benefit of Conversational AI is its high scalability, making it perfect for use across various industries and applications. It can be used for customer support, lead generation, sales, and marketing, among other use cases.
It is also adaptable to different languages and dialects, eliminating one more limitation of the language barrier while dealing with a global audience. Much of a talk, right? Let’s look at some numbers to understand conversational AI’s benefits better.
- Experts believe that Conversational AI assistants will save $8 billion for global business by reducing their customer support costs in 2023
- Apart from monetary benefits, these AI assistants are capable of saving upto 2.5 billion hours of human workforce annuallyÂ
- Approximately 91% of total online shoppers find it more comfortable to interact with a conversational AI than conventional chatbotsÂ
- Chatbot Magazine found in a survey that US brands have 64% more chances of converting a millennial lead with AI assistance
Conclusion
Chatbots can be undoubtedly credited as a breakthrough to save human efforts in repetitive tasks for many businesses. However, the pace at which technology has grown in the last decade is unreal. To keep up with the pace of this digitally growing world, it has become essential for businesses to bring in changes that their audience demands.
For instance, let’s take an example of the steep decline in the amount of time an internet user now spends in shopping online, and it continues to deplete as tech grows and enhances the reach of a user.
Conversational AI assistants can increase customer engagement upto 5X and significantly increase the chances of lead conversion with their hassle-free navigation through a series of products and helping the user find the perfect fit.
Connect with Kenyt.AI and get your conversational AI Assistant to help your business grow faster and more efficiently.
