Customer Experience

Why Do AI Chatbots Work Better Than Chatbots For Customer Experience?

Have you mistakenly assumed that chatbots and conversational AI chatbots are the same? Don’t worry; you’re not the only one who feels this way. When most people think of conversational AI, they think of chatbots that can be seen on many corporate websites. 

While they are correct in that that is one example of conversational AI, many more demonstrate the functioning and capabilities of AI technology. In this post, we’ll look at the history and applications of conversational AI and how it’s being applied in places other than chatbots. 

Difference between chatbots and AI chatbots

Even though they are commonly used interchangeably, the terms “chatbot” and “conversational AI” do not apply to the same thing. In truth, these two terms do not belong in the same category! Computer programs are known as chatbots. They might be based on rules or artificial intelligence. 

As a result, conversational AI is simply one of many technologies that can be used to power chatbots. Chatbots are computer programs that act as if they are conversing with a human. They’re the website’s pop-up chat, your bank’s customer service chat, and the automated voice that asks you to describe your problem before routing you to the appropriate human agent when you phone a contact center.

Finally, chatbots and conversational AI are the subsequent approaches to automate customer care, giving the customers assistance around the clock.

On the other hand, rule-based chatbots and those that use conversational AI have different capabilities and are developed differently. They are both very distinct in terms of how they work.

You want to know how rule-based or keyword-based chatbots stack against AI-driven chatbots.

Traditional chatbots are text-based and typically only appear on one of a company’s platforms, such as its website. Conversational AI is omnichannel, meaning it can be utilized and accessed via various platforms and mediums, including text, speech, and video. “When customers reach out to connect with companies, intelligent conversational experiences and a fine-tuned AI application allow for better, smoother options,” Carrasquilla stated.

Digital assistants like Cortana, Google Home, Siri, smart speakers like Amazon Alexa and Google Home, and virtual call center operators are common examples. Artificial intelligence is quickly progressing, and it is now possible to create conversational virtual agents that can understand and respond to various questions. Chatbots wish they could be conversational AI.

Business owners may wonder how the two differ and which is best for their organization. We’ll compare and contrast chatbots with conversational AI to answer those questions. Conversational AI refers to any technology enabling users to converse and receive responses. It includes traditional chatbots, intelligent home assistants, and various types of customer support software.

Traditional Chatbots

Consider keyword-based chatbots as flow charts to better understand how they work. Chatbots work because a team behind the bot feeds it keyword questions and preset responses to those questions. Each reaction is programmed in advance to lead to the next. Interactive decision tree chatbots, also known as rule-based chatbots, follow a specific pattern. They follow a set of rules, as the name suggests. In utilizing these guidelines, the bot will be familiar with the types of problems it encounters and will be able to solve.

Rule-based chatbots plot out talks like a flowchart. They do this before a customer’s question and how the chatbot should answer. Simple or complex rules can be used in rule-based chatbots. They can’t answer any questions that aren’t in line with the established guidelines. Interactions do not teach these chatbots anything. Furthermore, they only perform and work in the scenarios you have prepared.

This way, the chatbot will know that if you ask to check your bank account, it should provide you with a login link or a password reset option. The next set of answers will differ depending on the path you take. 

Selecting the forgot password option, for example, may prompt the chatbot to give a password recovery option by email or phone, with your choice leading to another pre-programmed action. This is useful for simple activities such as locating specific information on a bank’s website. 

On the other hand, Keyword-driven chatbots do not allow for queries that are not part of their programming. Thus, the team must have anticipated and developed the bot to handle any possible client query.

Traditional chatbots are keyword-based, which means they won’t understand a customer’s purpose if it isn’t phrased precisely how the team intended. Customers may become disappointed due to chatbots because the bot’s restricted scope means it cannot always fix the customer’s issue.

AI chatbots 

Using Natural Language Processing (NLP) and Automatic Semantic Understanding (ASU), conversational AI chatbots can understand their clients’ needs regardless of how they express them. Conversational AI, unlike traditional chatbots, does not require each exact query to be pre-programmed, and it will comprehend what the client wants regardless of how it is phrased. 

This implies that whether you entered “bank statements” or “I’d like to see my accounts for the previous month,” the AI-powered bot will comprehend that you want to check your bank statements. Even if you misspell something in your message, it will understand! The more it interacts with clients, the more conversational AI will learn. This means that, over time, it will improve its language understanding and efficiency in providing clients with the required assistance. 

Conversational AI simulates real-world discussions using natural language processing (NLP), automatic speech recognition (ASR), and advanced dialogue management. Deep learning is also used to keep improving and learning from each discussion. Traditional chatbots must stick to pre-defined customer service scripts and rules and cannot answer questions that aren’t incorporated into their conversational flow, but a bot is more flexible. It can bounce from one topic to another, as do human conversations.

Many people consider AI Bots to be a more advanced version of chatbots. They are ideal for businesses with a large amount of data. AI chatbots take longer to train at first but can save time in the long run.

 Chatbots powered by artificial intelligence:

  • understand patterns of behavior  
  • have a broader range of decision-making skills
  • can comprehend many languages and learn from information gathered
  • continually improve as new data comes in, understand patterns of behavior
  • have a more comprehensive range of decision-making skills and can understand many languages

What are the advantages of conversational AI in the industry?

In any customer-facing technology market, the ultimate benefit of conversational AI is improved client experience. Three interrelated variables make this possible.

#1 – A more user-friendly query interface

Conversational AI improves the customer experience by allowing customers to participate via text, one of the most common communication methods. It also enables customer help to be available 24 hours a day, seven days a week, and it is quick and straightforward.

#2 – Waiting periods are shorter

Conversational AI frees up capacity for human customer support by automating a high volume of client interactions. As a result, clients speaking with the bot and those in line with a human agent will have lower wait times & offer faster resolution in first time. Customers are happier when waiting times are reduced.

#3 – Effective human assistance

As a result, human agents can answer more queries in less time and access a continuous stream of information to assist them in their tasks. Overall, client service is timelier and more targeted to their specific requirements and preferences. Conversational AI can support the hyper-personalization that customers expect today because of its capacity to access client data in real-time.

“To serve information that is especially relevant to a client, hyper-personalization blends AI and real-time data.” According to Chris Radanovic, a conversational AI expert at LivePerson, consumers, and brands are embracing conversational AI since it offers tailored experiences that are much faster and more convenient than traditional methods of connecting with enterprises. To wait on hold for a call or scroll through multiple pages to find the information buyers need is inconvenient for buyers.


People enjoy how open ai chatbot is about what it can and can’t accomplish. People rarely initiate a discussion when it is wide open. Things tend to flow more smoothly when the consumer has buttons and a clear path.

While AI chatbots have their place, our clients have found that rule-based bots are more than capable of handling their needs. Naturally, the more you train your rule-based chatbot, the more versatile it becomes. Questions that your rule-based chatbot cannot answer are an opportunity for your firm to gain knowledge. You can quickly change and modify the rules when things go wrong, whereas machine learning is more difficult to course-correct.

Conversational AI has shown to be incredibly good at simulating human conversations, but it has also established itself as a reliable mode of communication. Today’s AI-powered chatbots are far from the clunky chatbots we used to encounter on corporate websites. Brands across all channels use conversational AI to provide hyper-personalized discussions with customers in real time.


  • Jayanti is a SaaS content writer at Knowmax, an AI knowledge management platform created by CX experts with 10+ years of experience in omnichannel customer service. She fuses her research and writing skills to bring products to life. She is passionate about reading fiction, writing poetry, and learning Japanese, and believes in creating holistic life experiences to keep her creative juices flowing.

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