COVID-19 has acted as an unexpected catalyst in the acceleration of digitization in our everyday lives. The digitization is not limited to a particular area but digitization has gained unprecedented momentum in each and every aspect of our lives. Since the past few years, the digitization wave has been focused on automation. Automation is focused on using technology to automate repetitive tasks and processes. New milestones have been achieved in technologies like IOT (Internet of Things), AI (Artificial Intelligence), RPA (Robotic Process Automation) and Machine Learning.
Chatbots can be considered as the biggest example of automation, AI and Machine Learning. Chatbots have gained massive popularity in the past few years and its adoption has been successful across various verticals and industries. Organizations and businesses are facing increasing pressure to improve communication and quality of customer service. Chatbots have the ability to solve these challenges as well as help these organizations to save money and man-hours.
A Chatbot is a software, which can stimulate a chat (or conversation) with consumers. Chatbots can also help organizations to automate the frequently asked questions by the consumers providing instant response 24*7*365.
Chatbot is mainly of mainly two types-
Rule-Based Chatbots — Rule-based Chatbots are the Chatbots that work or perform on a series of predefined rules. Also known as Decision Tree Chatbots, Rule-based Chatbots cannot take decisions on their own and they do not learn or improve through consumer interactions.
Artificial Intelligence (AI) based Chatbots — Artificial Intelligence (AI) based Chatbots are the Chatbots that work on the basis of Artificial Intelligence to understand the intention and context of the consumer input. AI-based Chatbots use Natural Language Processing (NLP) in order to respond to the consumer’s input.
When it comes to the comparison of both the above given Chatbots, both (AI and Rule-based) have their own features but the key differences between them are as follows-
1. WORKING ALGORITHM –
Both Rule-based and AI-based Chatbots work on completely different algorithms.
Rule-based Chatbots operate on a previously defined set of rules for the user and accordingly reply to the question asked/ options selected by the user. The consumers simply need to feed in their input and the Chatbot delivers the appropriate response as defined in the rules.
On the other hand, AI-based Chatbots works on machine learning algorithms in which the Chatbot is trained in order to perform the tasks based on the intention and context of the consumer input. The algorithm learns from the gathered information as well as conversations that happen with the consumers. It can be improved with continuous training, more data and pattern recognition in consumer conversations.
2. INVOLVEMENT WITH THE CONSUMERS-
Rule-based Chatbots and AI Chatbots have different ways of involving consumers.
Rule-based Chatbots do not show active involvement with the consumers. The Chatbot can only answer the predefined set of questions and any other input cannot be understood by the Chatbot. It is expected that the consumer will interact in a defined way and any input has to select the predefined options and cannot go for any other options.
AI-based Chatbots show more active involvement with their consumers. They are trained in such a way that they can talk and interact with the consumers.
3. TIME TAKEN IN TRAINING-
Rule-based Chatbots and AI Chatbots take different time in the training of Data.
Rule-based Chatbots take less time in training and are faster to train. The data structure algorithm of Rule-based Chatbots is simple to form and train.
AI-based Chatbots includes Machine learning and they take a lot of time and data in training. The data in this Chabot needs to be trained several times because this Chatbot is needed to interact with the consumers naturally. Along with it the AI Chatbot also has the ability of decision making.
4. DECISION-MAKING SKILLS-
Rule-based Chatbots and AI Chatbots have different decision-making skills.
Rule-Based Chatbots do not have the ability of decision making, they only work based on pre-defined rules. The user has to select the pre-defined options and the Chatbot gives responses to the consumer input.
AI-based Chatbots have a strong decision-making ability as these Chatbots are trained in such a way that the Chatbot can communicate with the consumers naturally and take decisions accordingly. The Chatbots have the ability to act like a human being by understanding the context as well as the intention of the consumer input.
5. BEHAVING LIKE A HUMAN-
Although no technology can replace humans but AI chatbots can simulate a human-like conversation in a specific context with appropriate training.
Rule-Based Chatbots have zero interaction power with the consumers as they are predefined on a certain path. They cannot deviate from the predefined path and fail if the conversations go out of the pre-defined path.
The idea behind AI-enabled Chatbots is to enact a human-like behaviour using Machine Learning. AI-based Chatbots use machine learning and training in such a way that they can work, interact, decide similar to human beings. The consumer talking to such a Chatbot can be expected to have a better experience in comparison to Rule-Based Chatbots.
6. PATTERNS OF BEHAVIOUR-
Rule-based Chatbots and AI-based Chatbots have different pattern recognition techniques.
On one hand, Rule-Based Chatbots can only take up the input from the consumers and save it in its database but cannot use that data or consumer input to enhance the conversation with the consumers.
AI-enabled Chatbots, on the other hand, recognize patterns from historical conversational data. Along with it, this Chatbot can improve itself from the previous conversations in order to provide unique, personalized conversations to the consumers.
In conclusion to the above differences, we can say that both types of Chatbots have their own set of advantages and disadvantages. Either way, Chatbots are bringing automation to support the customer service and marketing teams of organizations to achieve more with less.