Chatbot vs Conversational AI: What Sets Them Apart?

Often used synonymously, chatbots and conversational AI bring two completely different concepts to the table. In very simple terminology, chatbots are computer programs, but conversational AI is a technology that powers them. 

They are related by virtue of having similar fundamental principles and, in a broader sense, chatbots being a part or a subset of conversational AI. 

Despite the similarities, the contrasts between the two have sparked many’s interest. Thus, a new debate among tech enthusiasts and leaders is being started on chatbot vs conversational AI. 

The leaders are interested in knowing what exactly sets the two apart, their unique benefits, and which would fit best in which situation. If you are one of those curious minds interested in the conversational AI vs chatbot debate, you are at the right place. 

But before going into their differences, let us understand what the two of these are: 

What is Chatbot? 

Chatbots, in simpler terms, would mean automated systems that interact with clients to resolve mundane queries. These chatbots were initially developed to free human resources from basic questions and provide round-the-clock customer services. 

Now, however, chatbots have become essential for business. With everything going digital, customers prefer their general questions be answered via chat. These chatbots save long waiting lines for customers and free the customer service agents for more critical issues. 

A basic chatbot can easily handle frequent questions, basic navigation, or informational queries. They can also help book tickets, order food, or assist with online shopping. 

Key Components Of Chatbots 

Chatbot development requires knowledge of some fundamental elements to build a resilient chatbot for businesses; these are: 

• User Interface Component – This is the front end of the chatbot, responsible for the customer’s side representation of the conversation. It can be text or voice assistants integrated into different applications like Facebook Messenger, Slack, Google Teams, etc. 

• Natural Language Understanding Component – It is a subset of NLP(Natural Language Processing) with which chatbots grasp the intent behind the query by understanding the word’s meaning, syntax, and grammatical usage in the sentence. 

• Dialogue Management Component –  This component is responsible for maintaining the conversation flow. It keeps a record of the dialogue in progress to provide effective replies.

• Backend Component – A rule-based chatbot requires a solid knowledge base (KB) and relationship database (RDB) for building connections and providing a diverse range of data for the chatbot to rely on for resolving queries. 

• Response Generation Component – Upon receiving the query, the chatbot understands, analyzes, and formulates the perfect response w.r.t. the question asked and available data. The response is formulated using the predefined rules.  

What is Conversational AI? 

Conversational AI is a technology that powers chatbots and makes them advanced enough to understand human language, the context behind the sentence, and its sentiment. With such advanced features, conversational AI chatbots can converse in natural language. 

It is not a rule-based reply; instead, conversational AI can have dynamic conversations. To achieve this, a large amount of data is fed to the system, which acts as the foundation for all the conversations and information shared by the system. 

Using conversational AI, many advanced features are possible, such as human-like interaction, context-based responses, adept handling of complex queries, or personalized suggestions. With such intriguing features, conversational AI can easily ace the chatbot vs conversational AI debate. 

Key Components of Conversational AI 

• Natural Language Processing – NLP uses large datasets to understand human language. The machine learns everything from idioms to slang, phrases, and sentence formation to converse in the same pattern. 

• Machine Learning – It is a branch of AI that allows computers to understand human patterns using accessible data and mimic the human brain. Along with ML algorithms, this improves the result with continual learning and updating. 

• Text Analysis – This component is about understanding the different parts of a sentence as subjects, objects, and their relation to get the contextual meaning of the text. This helps machines understand the sentiment behind it and send an appropriate reply. 

• Computer Vision – This is similar to NLP but for images. Computer vision is used to understand different components of an image from objects and their relation to their orientation and contexts. This is then used to provide relevant answers or replies.

• Speech Recognition – The tool allows the computer to understand the different tones, expressions, and syntax of the verbal human language. The feature is heavily used in AI virtual assistants for mobile phones or in speech-to-text features, video captions, etc. 

Chatbot vs Conversational AI – Differences 

Even though chatbots are extensions of conversational AI, some key things make them unique. These contrasts are the key factors in settling the conversational AI vs chatbot debate. 

The core differences between chatbots vs conversational AI are:  

Response and Interaction Style 

Chatbot – It follows the “if-then” rule. If the input is A, then respond B is how it engages with the user. The interaction is predictable, and the response is predetermined based on specific keywords. 

Conversational AI – NLP and ML make it possible to establish the connection between different parts of the sentence, making the interaction context-driven. The responses are also more personalized, thanks to their ability to mimic natural language. 

Intent Grasping Abilities 

Chatbot – Limited ability to grasp the context as no previous conversation records exist. Also, the keyword determines the appropriate response, which again doesn’t help much with understanding the intent. 

Conversational AI – Powered by NLP and ML, conversational AI makes the conversation more intentional. Since the machine is now capable of understanding and mimicking the different nuances of natural language, like phrases, idioms, slang, and more. 

Customer Service

Chatbots – The customer service is limited to basic rule-based queries. These chatbots can easily handle general inquiries, schedule appointments, and provide updates. However, chatbots can’t handle the inputs unique to their system. 

Conversational AI – It can manage almost everything, from simple queries to complicated problems, thus leaving only the highly technical ones for agents. Additionally, it can provide personalized customer service by giving empathetic and relatable solutions. 

Creative Capabilities  

Chatbots – It is rule-based and has a limited understanding of languages. Since it operates mostly on the trained data, it cannot provide any unique or latest ideas. This is why most of its responses are either unrelated or lack creativity.  

Conversational AI – It can analyze texts, images, and speech to provide unique, personalized, and creative responses. Since it uses ML to learn and improve continually, conversational AI is best for deriving results unique to the query.  

Communication Channels 

Chatbots –  It supports only one communication channel,i.e., texts or chat based systems. Customers can only communicate their requirements through texts, which will be forwarded to human agents if unresolved. 

Conversational AI – It supports all forms of customer input, whether written, vocal, or file attachments. Thus, customers can share their concerns conveniently through the available multi-channel communication option. 

(Human) Language Support 

Chatbots – It has limited (human) language capabilities and can support only a few languages. Generally, these chatbots have one or two language(s) compatibility and follow a set pathway, which can get complicated.

Conversational AI –  It is advanced and can support multiple languages simultaneously. This gives users an range of options to choose from, possibly giving them a choice to communicate in their native languages and easing the whole process. 

Integrations 

Chatbots – These are used to make appointments, get updates, or answer general questions using a structured question system. Chatbots allow integrations to make these tasks happen, but the scope is limited. 

Conversational AI – It allows extensive integrations for dynamic problem-solving and handling multiple tasks simultaneously. It can easily integrate with CRM platforms, aligns with DevOps software development methodologies, and can be used for AI-app development. 

Criteria	Chatbots	Conversational AI
Response and Interaction Style	Robotic and predictive	Natural and personalized
Intent Grasping Abilities	Limited to keyword identification	Excellent intent understanding with NLP & ML
Customer Service	Can help with FAQs and simple inquiries	Efficiently handles complex queries and tasks
Creative Capabilities	Not adept at providing creative solutions	Unique solutions delivered based on search intent
Communication Channels	Linear (Single channel)	Non-linear (Multiple channels)
(Human) Language Support	Restricted to a few languages	Extensive language understanding
Integrations	Available but limited	Extensive integration options

There are many similarities between chatbots and conversational AI. Both use some components to understand natural language and respond based on the effective analysis of the input. 

They even share similar use cases, like in customer service, automating specific tasks, etc. However, despite these shared similarities, when it comes to conversational AI vs chatbot, the differences between them determine the final judgment. 

Conversational AI has advanced integrations, while chatbots are somewhat primitive. This is primarily a key factor for those looking for an AI-based app development solution. 

As seen in the table above, the contemporary aspect of conversational AI does make it a preferable solution. However, chatbots are also quite relevant, if not equally, compared to conversational AI. 

Also, for all the tech enthusiasts like yourself who are enjoying these chatbots vs conversational AI arguments, here’s another topic of interest for you – it’s about an AI tool that took the world by storm, ChatGPT. 

Different Varieties of Chatbots And Conversational AI

Both chatbots and conversational AI can be easily integrated into the current system. This is because these are easily available in different forms with many use cases. The different kinds of chatbots and conversational AI are:

Types Of Chatbots 

• Rule-Based Chatbots 

It resolves the queries after identifying certain keywords in the question. These keywords trigger a set response that is pre-fed to the system. However, they cannot handle a unique query, which disadvantages them in the chatbots vs conversational AI since there is no learning from human interaction. 

• AI Chatbots 

These AI-based chatbots learn from human interactions to improve their responses with every conversation. These chatbots respond to the needs based on the chat. Though these chatbots can understand human language, they cannot reply in a human tone. Making it relatively weaker in the chatbot vs conversational AI debate. 

Types Of Conversational AIs 

• Conversational AI Chatbots 

Chatbots powered by conversational AI use Natural Language Processing (NLP) to provide a more refined and natural-feeling reply. This makes the user comfortable and improves the interaction. 

• Voice Assistants 

These assistants use voice commands to fulfill the task, using voice recognition, synthesis, and understanding of the command to provide the required input—for example, Amazon’s Alexa, Apple’s Siri, etc.  

• Virtual Assistants 

These assistants are mainly built using NLP and ML to assist humans with specific tasks. NLP helps design the appropriate answer in natural language. Meanwhile, ML allows continual learning and improved suggestions based on previous actions.

Chatbot vs Conversational AI – Advantages 

Benefits Of Chatbots

• 24/7 Customer Support – A considerable margin of wait time can be reduced by integrating chatbots for mundane queries. This is beneficial for customers as they can get instant replies. As for the employees, they don’t have to sit and tackle the basic questions. 

• Collecting Feedback – Once the query is resolved, an automated pop-up can be included for users to write feedback. This will help implement the required changes and improve the customer experience. 

• Reduce Customer Tickets – With all basic issues being handled by the chatbots. The agents can focus on more complicated queries and get done with them faster than those without chatbots.

• Cost Reduction And Scalability – Chatbots reduce the need to hire a human agent when upscaling because they can handle a significant chunk of the total workload. This can be especially useful in case of festivities and high market demands. 

• Lead Generation – Chatbots can quickly identify customers’ needs and automate their interaction with potential customers. This will increase the customer rates without relying too much on the team. 

Benefits Of Conversational AI 

• Context-based Understanding – With the help of NLP and ML, the system becomes advanced enough to grasp the data input better and give an appropriate reply. This makes it dynamic, not a rule-based system dependent on some keywords. 

• Enhanced Customer Service –  One of the many advantages of conversational AI is that it can manage multiple requests simultaneously. It can have an active conversation round-the-clock, making customer service faster and improving customer experience. 

• Personalized Suggestions – Today, no one has time to go through everything. They prefer to get suggestions and advertisements that are in their interest. With conversational AI, this is possible. Users can get personalized ads based on their activities and interests. 

• Improved Workflow – Since AI would resolve multiple simple and complex tickets, there would be very few left for the human agent to take care of. Additionally, the tool can allocate the remaining tickets based on the agent’s skills. 

• Multilingual Channel –  Since conversational AI learns from the datasets it is exposed to, it can automatically learn multiple languages and resolve queries in the customer’s preferred language. 

Whether you use rule-based or conversational AI chatbots, both are equally beneficial. This is why partnering with a reliable service provider is advised; they know the field best. 

A professional and ethical AI consulting service provider will guide you through the process in such a streamlined manner that integrating AI chatbots will come off the “things to worry about” list.  

Use Cases Across Industries For Chatbot vs Conversational AI 

Chatbots In Different Areas And Sectors

When people think of chatbot relevancy, they generally talk about customer services, like resolving customer complaints or answering questions. But that’s not all chatbots are about. They still have plenty of use cases across varied sectors and industries. 

Here are some chatbot use cases that are NOT about resolving customer queries: 

• For Ecommerce Websites

Chatbots can help users navigate through the website. Think of them as shopping personnel but digital. They can guide them through to help find precisely what they want. 

The chatbots can be programmed to announce offers that might interest customers based on their activities or remind them about their cart. Personalized suggestions can also be provided based on their product reviews and past purchases. 

• Chatbots In Classrooms

Well, not to teach, but to help instructors share some of their workload and utilize the time for other important tasks like lesson planning, identifying and mitigating gaps, etc. A separate space for attendance & leave tracking, fee reminders, etc., can be created using chatbots. 

When trained well, chatbots can alert students when their attendance falls short or send alerts about upcoming deadlines. For the instructors, it can help to schedule lesson reminders or identify students’ leave patterns. Many other unique features can be added, like providing feedback, one-on-one tutoring, etc. 

• Automated Announcements 

Key to a successful business is effective management throughout the business operations, both internal and external. While using chatbots on the customers’ end is not new to anyone, it can also streamline internal processes. 

Chatbots can be programmed to generate and share important announcements across the organization, followed by automated reminders at regular intervals. Even emails for work anniversaries, birthdays, and events can be generated as employee appreciation. 

• Online Reservations 

Restaurants can streamline the process for existing or potential customers with a chatbot. They can show their menu, set a questionnaire, and suggest meal options based on customer preferences. 

They can even show the table availability during busy hours so customers can reserve beforehand. Chatbots can also help with event reservations by asking primary questions like date, number of guests, occasion, etc., and sharing them with the restaurant manager. 

Conversational AI Across Different Businesses

Chatbots automate the workflow, help streamline it and whatnot, but conversational AI takes it up a notch with its forward-looking solutions. These integrations, compatibility options, etc., make it a preferred choice for those who aim to streamline operations by simplifying complex requirements. 

Again, while conversational AI chatbots clearly understand customer services, they can also be integrated into other operations. Some possible areas of transformation are: 

•  IoT Devices 

Conversational AI opened up opportunities for smart homes to come into existence. Voice assistant technologies like Amazon Echo and Google Home, as well as intelligent mobile solutions like Google Assistant and Apple Siri, further increased demand. 

These devices help users manage and control devices and use voice commands to order food or groceries online. For instance, tunring the AC on a few minutes before reaching home via mobile assistants. Many such benefits can be unlocked using IoT. 

• Conversational AI In Healthcare 

AI has multiple use cases in healthcare. These conversational AI can be used for primary diagnosis and recording data like medical history, symptoms, age, weight, etc. This would help give patients early treatment and schedule appointments as and when needed. 

It can also be used as a (virtual) assistant for practitioners and patients, helping the former keep an organized patient record, including prescriptions, test results, etc. At the same time, the latter benefits from medicine reminders, re-stock alerts, etc. 

• Improved Finance Management 

From tracking spending habits to detecting fraud messages, conversational AI can benefit the finance sector in many ways. It can detect spending patterns, auto-debits, and income to improve finance handling, which users find extremely helpful. 

It pinpoints any irregularities to warn users about potential fraud. Additionally, it can provide personalized offers, plans, and banking services based on user interests. These things contribute to improving user experience. 

• Conversational AI In Real Estate

There are solutions for digitalizing real estate as well.  Conversational AI can be used for lead generation, conversation, and process initiation. The system can connect with customers and get information like area, budget, property features, etc. The information mentioned above can be used for personalized recommendations.   

Further, these AI tools can hold multiple conversations simultaneously, thus improving the conversion rate manifold. It can also filter potential customers, sharing their data with the human agent for improved marketing. 

To Sum Up The Chatbot vs Conversational AI Debate

Do you know? In the 1950s, Alan Turing sent his paper “Computing Machinery and Intelligence” for publication, and John McCarthy coined the term “Artificial Intelligence.” These two are considered the most crucial AI contributions till date. They literally started whatever we have built today in AI. 

Sometimes, looking back to the history of AI, we wonder if Alan and John ever thought that one publication and one term could disrupt the whole business landscape. Today, AI, in its varied forms, is an integral part of every business, whether it is a startup or an established enterprise. 

It will continue to dominate the market, one way or another, but what about conversational AI vs chatbot? Well, the same goes for these two AI models as well. 

Whether we look at it as chatbot vs conversational AI or chatbots AND conversational AI, the two will continue to evolve, both as separate entities and as an integrated solution. The tech experts are experimenting, and so are business owners. 

Today, businesses go for either chatbot or conversational AI based on their requirements, budget, organization size and automation scope. There’s a lot to think about when choosing the proper integration. 

But who knows, coming years might witness complete automation through an integrated chatbot and conversational AI solution. There’s a possibility that conversational AI will take over the chatbots, thus putting an end to this whole chatbots vs conversational AI debate. 

Regardless, one thing is sure: both are equally capable of revolutionizing the business scenario today and in the future.