Traditional Chatbots vs. Conversational AI



conversational chatbot

Automating customer interactions with conversational chatbots offers a range of benefits. Chatbots can increase employee productivity, enabling service to more customers in a shorter time window. Automated customer support solutions also decrease human error and provide consistent, accurate information to customers. Automating interactions can enhance customer experience (CX) with quicker first response time, average resolution time, and first contact resolution.

However, the gains you can expect from automating customer service depend on the type of chatbot technology you choose to deploy for your business.

Traditional Chatbots vs. Conversational AI: What is the Difference?

Although the term “chatbot” is applied to any computer program that simulates human conversation, how chatbots are designed and work can vary widely.

A Brief History of Chatbots

Reviewing the timeline of how chatbots evolved provides insights into how different chatbots work. Did you know Joseph Weizenbaum created the first chatbot, Eliza, in the 1960s? The goal was to simulate conversations with a psychoanalyst who would ask questions to encourage patients to explore their thoughts. Eliza was simple by today’s standards, recognizing keywords or phrases and using them to ask those questions. Although simple, users often forgot they were talking to a machine.

Innovators continued to pursue creating systems that mimicked the conversational flow of human communications. A.L.I.C.E., developed in the 1990s, uses behavioral pattern matching to converse with people, and SmarterChild, a conversational instant messaging bot.

Artificial intelligence (AI) enhanced chatbots as early as the 1990s, and advancements make their capabilities more and more impressive. AI now powers virtual assistants with entertaining personalities that help people complete day-to-day tasks.

What is a Conversational Chatbot?

When the goal is to allow a person to speak to a machine naturally and respond the way another human would, a conversational chatbot platform can help you get there. Human back-and-forth interactions aren’t limited to a set number of “inputs” and corresponding “outputs.” Therefore, conversational chatbots need to be designed to interact in the same way. Different forms of AI, from machine learning (ML) and deep learning (DL) to natural language processing (NLP), give the most advanced types of conversational chatbot systems that ability.

Chatbots vs. AI: What is the Difference?

Although conversational chatbots leverage AI to work, they are different from each other. AI is the branch of computer science that enables machines to think like humans and, in some cases, process information much faster than humans.

Chatbots use AI to understand human communication via voice or text and use data to formulate answers, but AI has other use cases. It can speed search engines to provide relevant results, help e-commerce websites personalize recommendations, monitor manufacturing equipment to take action for worker safety, and more. AI can do all this without necessarily having conversational interfaces.

Are All Chatbots the Same as Conversational AI?

Although chatbot technology has evolved to include conversational AI capabilities, not all chatbots leverage that advanced technology. Conversational chatbots are a type of chatbot, but there are also chatbots that use different techniques to carry on a machine-to-human conversation.

What are Rules-Based Chatbots?

Often, when people refer to just a “chatbot,” they’re talking about a rules-based platform. These solutions rely on an if-this-then-that (IFTTT) strategy to simulate conversations. For example, if the customer’s “input” is “large pepperoni with extra cheese,” “large pizza with extra cheese and pepperoni,” or “pizza with extra cheese and pepperoni, size large,” the chatbot would extract the essential information and respond with a price and a delivery time.

Rules-based chatbots can encounter difficulties if the customer asks the question differently. Staying with the pizzeria example, if a customer says, “I want a pie with pepperoni and extra mozzarella,” the decision-tree-based chatbot may need to ask for clarification that the customer is actually ordering a pizza.

How to Train Rules-Based Bots?

“Training” is often associated with preparing an AI chatbot to operate accurately in real-world conditions and continue to learn based on the inputs it receives. Rules-based bots aren’t “trained” per se. They respond based on rules that the business defines.

So, if the customer says “pizza,” “large,” “pepperoni,” and “extra cheese,” the chatbot initiates the order. Rules-based chatbots can be programmed to ask for more information or clarification or to transfer the call to a human agent if they can’t complete the task.

Understanding Rules-Based Bots and the User Experience

It’s important to understand the user experiences that the technology you deploy delivers, both for your team and your customers.

Rules-based bots are simpler than conversational chatbots, making them easier to implement and less complicated to manage. Additionally, if you have a good handle on the types of questions your customers will ask or the requests they make when they call, you can define rules so that the chatbot performs well.

On the other hand, if your customers misspell a word or ask a question in an atypical way, the chatbot may not be able to respond with a relevant answer. And since rules-based chatbots respond with preset outputs based on what a customer types or says, the experience can be robotic rather than like a natural conversation.

Two Different Types of Conversational Chatbots: Generative vs. Retrieval

In addition to categorizing chatbots as rules-based or AI, you can further divide AI bots into “generative” and “retrieval.”

  •       Retrieval-based chatbots are trained to respond based on existing information. They often leverage a business’s knowledge base, help center, customer information, or other data sources to provide the best answers.
  •       Generative chatbots can create original responses using natural language processing capabilities and extensive training.

How to Train Conversational Chatbot Solutions

Training conversational chatbot solutions begins with annotating data – labeling it in a way that makes it understandable to a machine. Solution builders can use preexisting language datasets and data specific to the business. After testing and launching the chatbot, it will continue learning and improving how it communicates. If the company sees that responses don’t quite hit the mark, it can retrain the chatbot with more data.

Conversational AI vs. Chatbot: Upgrade or Replacement?

Suppose your business currently uses a basic chatbot for things like call screening or answering users’ questions on your website or messaging apps. In that case, you may be wondering if an upgrade to a conversational chatbot is the next logical step.

Depending on how you use basic chatbot technology and how effectively it communicates with your customers, an upgrade may not be necessary. One example is deploying a chatbot to answer FAQs. If that’s the role a chatbot needs to play in your organization, AI may not be necessary.

A good gauge of whether you need an upgrade is the customer experience and the level of customer satisfaction that your current solution provides. If customers are often frustrated when trying to place an order, access information, or complete other tasks, it may be a sign that your business needs AI-based chatbots.

Additionally, if your human agents are struggling to keep up with high call volumes, a primary conversational chatbot can help. Agents who spend much of their shift answering simple questions or helping people access accounts or track shipments can offload those tasks to conversational AI and focus on more complex issues or customers that require an empathetic ear.

Furthermore, conversational chatbots can collect information for human agents for a smooth handoff, saving time and paving the way for an efficient, customer-pleasing experience.

How Does Conversational AI Work?

Several technologies give conversational AI chatbots their unique capabilities, including:

Natural Language Processing and Artificial Intelligence Technology

Natural language processing (NLP) is the subset of AI that enables a machine to understand what a person says or writes. Natural language generation (NLG) is the specific technology that allows the conversational AI platform to produce a relevant, contextual response for the customer.

Automatic Speech Recognition

Automatic speech recognition (ASR) combines machine learning and AI to convert human speed into readable text for a machine.

Natural Language Understanding

NLU allows machines to understand human language, both literally and a person’s intent, and even fill in gaps if a person doesn’t complete a thought or misspells a word.

How Conversational AI Technology Transforms Virtual Assistants and Chatbots

When conversational AI powers virtual assistants and chatbots, they can automate more workflows and enhance customer experiences. Some of the most popular use cases of conversational AI virtual assistants and chatbots include:

Customer Service

Customer service chatbots can give businesses an effective way to encourage self-service. When speaking to a conversational bot is as natural as talking to a human agent, interactions are productive and efficient – and customers never have to insist on transferring to an agent.

Lead Generation and Conversion

AI bots can follow up with people that express interest in a company’s online assets and qualify them as leads. When the product the company sells is digital, such as Software as a Service, the bot may even be able to close the deal, accept payment, and activate the user’s account.

Customer Research and Feedback

AI chatbots can conduct surveys conversationally to collect customer feedback. Conversational AI enables the survey to flow like a natural conversation, which can encourage participation and honest responses.

Conversational Chatbot Examples

Conversational AI applications are in use daily in thousands of households and businesses.


Wag! is an app that connects pet caregivers with pet owners across the U.S. Wag! Customers had many questions and concerns, but the company realized a conversational bot could address many of them. After deploying the solution, the company’s self-service rate increased to 44% – a significant achievement for a business that has completed 11 million pet care services.  


Whole Home Wi-Fi system provider eero makes excellent customer service a priority. The company wanted to deploy a solution that increased the use of its knowledge base, directing calls toward the resource and away from agents. With a conversational chatbot, eero reduced service tickets by 45% and boosted its NPS and CSAT scores.


Hungryroot, a weekly subscription food service, uses a conversational AI solution to find answers they need and resolve issues without waiting to speak to an agent. The conversational bot allowed Hungryroot to increase its average self-service rate to 50% and reduce the number of tickets by 20%.

Implementing a Conversational Chatbot

When you’re ready to launch a conversational chatbot for your business, the first place to start isn’t with technology; it’s with your customers.

Once you understand how your customers interact with your support team and how chatbots can enhance those experiences, create journey maps and use cases to define what your chatbot will do clearly. Then you can find a platform that meets your needs.

Next, inventory all the data sources your chatbot can leverage to generate responses to your customers and give the chatbot platform access to them.

After training and testing that shows the chatbot works as expected, launch and monitor effectiveness. If your chatbot needs to have better-defined rules or more training, take the steps you need until it performs optimally.

Is There a Chatbot in Your Business’s Future?

Since Eliza first carried on conversations over 50 years ago, chatbot technology has significantly advanced, and businesses have found more ways to use them to enhance user experiences. And that trend of innovation and growing adoption will continue.

Read Forrester’s predictions for how chatbots will evolve in the next five years and how chatbots can play a role in your business strategy.