Chatbot vs conversational AI: Differences, types, and examples

Chatbots vs conversational AI – you’ve probably heard these terms used interchangeably before. But in fact they refer to related yet distinct technologies. 

When we take a closer look, there are important differences for you to understand before using them for your customer service needs. Chatbots are computer programs designed to engage in conversations with human users as naturally as possible and automate simple interactions, like answering frequently asked questions. 

Some follow scripts and defined rules to match keywords, while others apply artificial intelligence to understand human language and respond to customers in real-time.

In a broader sense, conversational AI is a concept that relates to AI-powered communication technologies, like AI chatbots and virtual assistants.

By combining data, machine learning, and natural language processing, it understands nuances and context for more natural and human interactions.

Both chatbots and conversational AI are on the rise in today’s business ecosystem as a way to deliver a prime service for clients and customers.

Based on Grand View Research, the global market size for chatbots in 2022 was estimated to be over $5 billion. Further, it’s projected to experience an annual growth rate (CAGR) of 23.3% from 2023 to 2030.

At the same time that chatbots are growing at such impressive rates, conversational AI is continuing to expand the potential for these applications. The AI impact on the chatbot landscape is fostering a new era of intelligent, efficient, and personalized interactions between users and machines.

In this article, you'll learn about the principles that differentiate chatbots vs conversational AI, explore their main differences, and gain insights into how artificial intelligence is influencing customer service.

What is a chatbot?

A chatbot is a conversational technology designed to engage with users through text or voice interactions. In essence, chatbots operate on a straightforward input-output framework: users send a message, and the chatbot processes them to return appropriate responses.

Chatbots are frequently used for a handful of different tasks in customer service, where they can efficiently handle inquiries, provide information, and even assist with problem-solving.

There are two main types of chatbots: rule-based and AI-powered. Let's delve deeper into each of these variations.

Rule-based chatbots

Rule-based chatbots rely entirely on predefined scripts to communicate. You can map out every possible conversational path and input acceptable responses to narrow down the customer’s intention.

This mapping gives the chatbot access to a limited set of appropriate replies for specific user queries and comments. Think of these presets as flowcharts that strictly dictate responses: "If a user says X, the bot replies Y."

With rule-based chatbots, there's little flexibility or capacity to handle unexpected inputs. Nevertheless, they can still be useful for narrow purposes like handling basic questions.

In short, rule-based chatbots can:

• Guide users through predefined conversation flows and decision trees.

• Offer menu-based navigation for users to get information.

• Answer simple questions, like checking an order status.

• Provide basic information via FAQs.

AI chatbots

On the more advanced end, AI chatbots (also known as contextual chatbots or virtual agents) incorporate machine learning, natural language processing, and generative AI to understand language and handle a more expansive range of conversations.

Instead of being given fully programmed dialogues, these bots are trained on conversational data that allow them to parse sentences, understand intent and context, and generate relevant responses even when users say something new or unusual.

AI chatbots can:

• Provide personalized and contextual responses.

• Tailor conversations through user information.

• Maintain natural, free-flowing interactions.

• Learn and improve conversations over time.

• Route users to a support rep when necessary.

In a nutshell, rule-based chatbots follow rigid "if-then" conversational logic, while AI chatbots use machine learning to create more free-flowing, natural dialogues with each user. As a result, AI chatbots can mimic conversations much more convincingly than their rule-based counterparts. 

How can you make sure you choose the right chatbot for your support needs? Download The AI Chatbot Buyer’s Checklist and check the key questions to ask when you’re choosing an AI chatbot.

What is conversational AI?

Conversational AI is a technology that simulates the experience of real person-to-person communication through text or voice inputs and outputs. It enables users to engage in fluid dialogues resembling human-like interactions.

It incorporates machine learning, natural language processing, and generative AI to converse in a more flexible way than technologies bound to rigid scripts.

The purpose of conversational AI is to reproduce the experience of nuanced and contextually aware communication. These systems are developed on massive volumes of conversational data to learn language comprehension and generation.

Essentially, conversational AI strives to make interactions with machines more natural, intuitive, and human-like through the power of modern artificial intelligence.

Types of conversational AI applications

Chatbots and voice assistants are both examples of conversational AI applications, but they differ in terms of user interface.

Chatbots are designed for text-based conversations, allowing users to communicate with them through messaging platforms. The user composes a message, which is sent to the chatbot, and the platform responds with a text. 

This allows for asynchronous dialogues where users can converse with the chatbot at their own pace. Conversational AI chatbots are commonly used for customer service on websites and apps.

In contrast, voice assistants are optimized for voice-based interactions. Users can speak requests and questions freely using natural language, without having to type or select from options. 

The voice assistant responds verbally through synthesized speech, providing real-time and immersive conversational experience that feels similar to speaking with another person.

Popular examples are virtual assistants like Siri, Alexa, and Google Assistant.

On a side note, some conversational AI enable both text and voice-based interactions within the same interface. For example, ChatGPT is rolling out a new, more intuitive type of interface. The feature allows users to engage in a back-and-forth conversation in a voice chat while still keeping the text as an option.

Rule-based vs conversational AI chatbots: how can they join forces?

AI chatbots don’t invalidate the features of a rule-based one, which can serve as the first line of interaction with quick resolutions for basic needs.

A rule-based chatbot can, for example, collect basic customer information such as name, email, or phone number. Later on, the AI bot uses this information to deliver personalized, context-sensitive experiences.

Another scenario would be for authentication purposes, such as verifying a customer's identity or checking whether they are eligible for a specific service or not. The rule-based bot completes the authentication process, and then hands it over to the conversational AI for more complex queries.

These are just a few practical examples of how traditional chatbots can collaborate with more advanced AI-based solutions, resulting in a customer service journey that leverages the best of what each technology has to offer.

What lies ahead for chatbots and conversational AI? 

Upon witnessing the capabilities of recent AI advancements, forward-thinking leaders have already started planning on how to integrate AI-powered technology into their team's workflow: 69% are ready to increase their investments in AI in the coming year, according to The State of AI in Customer Service 2023 report.

As mentioned earlier, traditional chatbots, such as rule-based ones, will still have a vital role in customer support. However, the trend suggests that we will see a prominent presence of conversational AI solutions, which we can easily understand when looking closely at some of their foremost benefits: 

• Requires no training – simply connect it to your knowledge base or help cCenter.

• Generates reliable, accurate responses based on your existing content.

• Smoothly escalates complex questions to customer service representatives.

Several companies, like Zapiet, a store pickup and local delivery plug-in for Shopify, are already leveraging these benefits.

As reported by Sam Forde, Merchant Support Manager from Zapiet, “The results we have seen with [Intercom’s AI-powered chatbot] Fin are groundbreaking, double-digit gains in engagement and resolution rates. Never have I seen a piece of technology so seamless to integrate, just a few clicks and you suddenly have a 24/7 new teammate!”

Fortunately, stories like Zapiet’s have become more and more common. As our research revealed, 61% of support leaders who have incorporated AI and automation into their operations have seen better results in their customer experience over the past year.

Moreover, 58% have noticed improvements in their CSAT scores, while 66% successfully achieved their KPIs and met their SLAs, as a result of using the AI solution.

Are you ready to embrace the future and drive greater results? Meet our groundbreaking AI-powered chatbot Fin and start your free trial now.