Comparing Rule-Based Chatbots vs Conversational AI Chatbots

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. 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. Chatbots and conversational AI are two very similar concepts, but they aren’t the same and aren’t interchangeable. Chatbots are tools for automated, text-based communication and customer service; conversational AI is technology that creates a genuine human-like customer interaction.

Together, these technologies ensure that chatbots are more helpful, can fulfil more complex tasks, and are able to engage customers in more natural conversations. So, while rule-based chatbots and conversational AI-based bots are both used for human-bot interaction, they are very different technologies and also provide a completely different customer experience. Conversational AI chatbots are excellent at replicating human interactions, improving user experience, and increasing agent satisfaction. These bots can handle simple inquiries, allowing live agents to focus on more complex customer issues that require a human touch.

In fact, artificial intelligence has numerous applications in marketing beyond this, which can help to increase traffic and boost sales. Because it has access to various resources, including knowledge bases and supply chain databases, conversational AI has the flexibility to answer a variety of queries. From the Merriam-Webster Dictionary, a bot is  “a computer program or character (as in a game) designed to mimic the actions of a person”. Stemming from the word “robot”, a bot is basically non-human but can simulate certain human traits. Most people can visualize and understand what a chatbot is whereas conversational AI sounds more technical or complicated. Conversation design, in turn, is employed to make the bot answer like a human, instead of using unnatural sounding phrases.

Because they often use a simple query-and-response interface, they can often be installed and customized by a single operator following guided instructions. According to Wikipedia, a chatbot or chatterbot is a software application used to conduct an on-line chat conversation via text or text-to-speech, in lieu of providing direct contact with a live human agent. Most chatbots on the internet operate through a chat or messaging interface through a website or inside of an application. Although it gets some direction from developers and programmers, conversational AI grows and learns through its own experience. And conversational AI chatbots won’t only make your customers happier, they will also boost your business. In the following, we’ll therefore explain what the terms “chatbot” and “conversational AI” really mean, where the differences lie, and why it’s so important for companies to understand the distinction.

This reduces wait times and will enable agents to spend less time on repetitive questions. You can foun additiona information about ai customer service and artificial intelligence and NLP. Instead of sounding like an automated response, the conversational AI relies on artificial intelligence and natural language processing to generate responses in a more human tone. Rule-based chatbots (otherwise known as text-based or basic chatbots) follow a set of rules in order to respond to a user’s input.

What separates chatbots and conversational AI?

They follow a perfect set of predefined rules to match user queries along with the pre-programmed answers, usually handling common questions. Think of traditional chatbots as following a strict rulebook, while conversational AI learns and grows, offering more dynamic and contextually relevant conversations. Conversational AI is more dynamic which makes interactions more personalized and natural, mimicking human-like understanding and engagement. It’s like having a knowledgeable companion who can understand your inquiries, provide thoughtful responses, and make your conversations more meaningful and enjoyable. Many chatbots are used to perform simple tasks, such as scheduling appointments or providing basic customer service. They work best when paired with menu-based systems, enabling them to direct users to specific, predetermined responses.

Let’s take a closer look at both technologies to understand what exactly we are talking about. The best part is that it uses the power of Generative AI to ensure that the conversations flow smoothly and are handled intelligently, all without the need for any training. Yellow.ai’s revolutionary zero-setup approach marks a significant leap forward in the field of conversational AI. With YellowG, deploying your FAQ bot is a breeze, and you can have it up and running within seconds. Also, with exceptional intent accuracy, surpassing industry standards effortlessly, DynamicNLPTM is adaptable across various industries, ensuring seamless integration regardless of your business domain.

In fact, they are revolutionizing and speeding up the adoption of conversational AI across the board, making it more effective and user-friendly. However, a chatbot using conversational AI would detect the context of the question and understand that the customer wants to know why the order has been canceled. Read about how a platform approach makes it easier to build and manage advanced conversational AI chatbot solutions. As we’ve seen, the technology that powers rule-based chatbots and AI chatbots is very different but they still share much in common. Now it has in-depth knowledge of each of your products, your conversational AI agents can come into their own.

These advanced systems are capable of delivering personalized, lifelike experiences, making them suitable for companies focused on innovation and enhancing long-term customer satisfaction. Chatbots and conversational AI are often used interchangeably, but they’re not quite the same thing. Think of basic chatbots as friendly assistants who are there to help with specific tasks. They follow a set of predefined rules to match user queries with pre-programmed answers, usually handling common questions. Chatbots and conversational AI are often used interchangeably, but they are not the same thing. Think about the basic chatbots as friendly assistants who are always there to help with specific tasks.

With so much use of such tech around a broad range of industries, it can be a little confusing whenever competing terms like chatbot vs. conversational AI (artificial intelligence) come up. Meet our groundbreaking AI-powered chatbot Fin and start your free trial now. 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. In this example by Sprinklr, you can see the exact conversational flow of a rule-based chatbot. Each response has multiple options (positive and negative)—and clicking any of them, in turn, returns an automatic response. This is more intuitive as it can recognize serial numbers stored within their system—requiring it to be connected to their internal inventory system.

With this bot, Belfius was able to manage more than 2,000 claims per month, the equivalent of five full-time agents taking in requests. This causes a lot of confusion because both terms are often used interchangeably — and they shouldn’t be! In the following, we explain the two terms, and why it’s important for companies to understand the difference. Depending on their functioning capabilities, chatbots are typically categorized as either AI-powered or rule-based.

Conversational AI is a Technology

The ability of these bots to recognize user intent and understand natural languages makes them far superior when it comes to providing personalized customer support experiences. In addition, AI-enabled bots are easily scalable since they learn from interactions, meaning they can grow and improve with each conversation had. Yes, traditional chatbots typically rely on predefined responses based on programmed rules or keywords. They have limited flexibility and may struggle to handle queries outside their programmed parameters. On the other hand, conversational AI offers more flexibility and adaptability. It can understand natural language, context, and intent, allowing for more dynamic and personalized responses.

The more training these AI tools receive, the better ML, NLP, and other outputs are used through deep learning algorithms. There are benefits and disadvantages to both chatbots and conversational AI tools. They have to follow guidelines through a logical workflow to arrive at a response. This is like an automated phone menu you may come across when trying to pay your monthly electricity bills. It works, but it can be frustrating if you have a different inquiry outside the options available.

The voice assistant responds verbally through synthesized speech, providing real-time and immersive conversational experience that feels similar to speaking with another person. Babylon Health’s symptom checker uses conversational AI to understand the user’s symptoms and offer related solutions. It can identify potential risk factors and correlates that information with medical Chat PG issues commonly observed in primary care. You can find them on almost every website these days, which can be backed by the fact that 80% of customers have interacted with a chatbot previously. Some conversational AI engines come with open-source community editions that are completely free. Other companies charge per API call, while still others offer subscription-based models.

Chatbots are a type of conversational AI, but not all chatbots are conversational AI. Rule-based chatbots use keywords and other language identifiers to trigger pre-written responses—these are not built on conversational AI technology. Yellow.ai revolutionizes customer support with dynamic voice AI agents that deliver immediate and precise responses to diverse queries in over 135 global languages and dialects. On the other hand, because traditional, rule-based bots lack contextual sophistication, they deflect most conversations to a human agent. This will not only increase the burden of unresolved queries on your human agents but also nullify the primary objective of deploying a bot.

To add to the confusion, sometimes it can be valid to use the word “chatbot” and “conversational AI” for the same tool. Sign up for your free account with ChatBot and give your team an empowering advantage in sales, marketing, and customer service. This is a standalone AI system you control with advanced security for peace of mind.

They use natural language processing to understand an incoming query and respond accordingly. Traditional chatbots are rule-based, which means they are trained to answer only a specific set of questions, mostly FAQs, which is basically what makes them distinct from conversational AI. Chatbots are the best software applications that are specially designed to manage human-like https://chat.openai.com/ conversations with users through the help of text. They use natural language processing concepts to understand an upcoming query and respond according to that. Traditional chatbots are rule-based, which means they are properly trained to answer only a specific set of questions, mostly FAQs, which is basically what makes them distinct from conversational AI.

In customer service, this technology is used to interact with buyers in a human-like way. The interaction can occur through a bot in a messaging channel or through a voice assistant on the phone. From a large set of training data, conversational AI helps deep learning algorithms determine user intent and better understand human language.

This extensive training empowers it to understand nuances, context, and user preferences, providing personalized and contextually relevant responses. Both chatbots and conversational AI help to reduce wait times in contact centers by taking the burden of dealing with simple requests away from human agents, allowing them to focus on more complex issues. Rule-based chatbots don’t learn from their interactions and struggle when posed with questions they don’t understand.

Also, it supports many communication channels (including voice, text, and video) and is context-aware—allowing it to understand complex requests involving multiple inputs/outputs. Now that your AI virtual agent is up and running, it’s time to monitor its performance. Check the bot analytics regularly to see how many conversations it handled, what kinds of requests it couldn’t answer, and what were the customer satisfaction ratings. You can also use this data to further fine-tune your chatbot by changing its messages or adding new intents. Due to this, many businesses are adopting the conversational AI approach to create an interactive, human-like customer experience. A recent study suggested that due to COVID-19, the adoption rate of automation and conversational interfaces went up to 52%, indicating that many companies are embracing this technology.

They answer visitors’ questions, capture contact details for email newsletters and schedule callbacks for sales and marketing teams to get in touch with clients and prospects. For example, the Belgian insurance bank Belfius was handling thousands of insurance claims—daily! As Belfius wanted to be able to handle these claims more efficiently, and reduce the workload for their employees, they implemented a conversational AI bot from Sinch Chatlayer.

They can answer common questions about products, offer discount codes, and perform other similar tasks that can help to boost sales. Conversational AI can draw on customer data from customer relationship management (CRM) databases and previous interactions with that customer to provide more personalized interactions. The main aim of conversational AI is to replicate interactions with living, breathing humans, providing a conversational experience. The definitions of conversational AI vs chatbot can be confusing because they can mean the same thing to some people while for others there is a difference between a chatbot and conversational AI.

In a recent PwC study, 52 percent of companies said they ramped up their adoption of automation and conversational interfaces because of COVID-19. Additionally, 86 percent of the study’s respondents said that AI has become “mainstream technology” within their organization. Both types of chatbots provide a layer of friendly self-service between a business and its customers. Chatbots and conversational AI are often used synonymously—but they shouldn’t be. Understand the differences before determining which technology is best for your customer service experience.

Chatbots and Conversational AI are closely linked, serving similar roles in automating customer interactions. Chatbots are programs that enable text and voice communication, while Conversational AI powers these human-like virtual agents. Many businesses are increasingly adopting Conversational AI to create interactive, human-like customer experiences.

You need a team of experienced developers with knowledge of chatbot frameworks and machine learning to train the AI engine. This solution is becoming more and more sophisticated which means that, in the future, AI will be able to fully take over customer service conversations. Implementing AI technology in call centers or customer support departments can be very beneficial.

In fact, advanced conversational AI can deduce multiple intents from a single sentence and response addresses each of those points. There is only so much information a rule-based bot can provide to the customer. If they receive a request that is not previously fed into their systems, they will be unable to provide the right answer which can be a major cause of dissatisfaction among customers. The voice AI agents are adept at handling customer interruptions with grace and empathy.

This percentage is estimated to increase in the near future, pioneering a new way for companies to engage with their customers. By providing a more natural, human-like conversational experience, conversational AI can be used to great effect in a customer service environment. This helps to provide a better customer experience, offering a more fulfilling customer experience. Digital channels including the web, mobile, messaging, SMS, email, and voice assistants can all be used for conversations, whether they be verbal or text-based. If your business requires multiple teams and departments to operate because of its complexity or the demands placed on it by customers and staff, the new AI-powered chatbots offer much greater value. In recent years, the level of sophistication in the programming of rule-based bots has increased greatly.

A recent study found a 52% increase in the adoption of automation and conversational interfaces due to COVID-19, pointing to a growing trend in customer engagement strategies. Expect this percentage to rise, conduct in a new era of customer-company interactions. After you’ve prepared the conversation flows, it’s time to train your chatbot to understand human language and different user inquiries. Choose one of the intents based on our pre-trained deep learning models or create your new custom intent. To do this, just copy and paste several variants of a similar customer request.

Sometimes, people think for simpler use cases going with traditional bots can be a wise choice. However, the truth is, traditional bots work on outdated technology and have many limitations. Even for something as seemingly simple as an FAQ bot, can often be a daunting and time-consuming task. On the contrary, conversational AI platforms can answer requests containing numerous questions and switch from topic to topic in between the dialogue. Because the user does not have to repeat their question or query, they are bound to be more satisfied.

Chatbots use basic rules and pre-existing scripts to respond to questions and commands. At the same time, conversational AI relies on more advanced natural language processing methods to interpret user requests more accurately. Chatbots operate according to the predefined conversation flows or use artificial intelligence to identify user intent and provide appropriate answers. On the other hand, conversational AI uses machine learning, collects data to learn from, and utilizes natural language processing (NLP) to recognize input and facilitate a more personalized conversation. Conversational AI refers to technologies that can recognize and respond to speech and text inputs.

In essence, conversational Artificial Intelligence is used as a term to distinguish basic rule-based chatbots from more advanced chatbots. The distinction is especially relevant for businesses or enterprises that are more mature in their adoption of conversational AI solutions. Even the most talented rule-based chatbot programmer could not achieve the functionality and interaction possibilities of conversational AI. This is a technology capable of providing the ultimate customer service experience. From language learning support for students preparing for a semester abroad to crisis management assistance for those overseeing an emergency.

Natural language understanding

Further, it’s projected to experience an annual growth rate (CAGR) of 23.3% from 2023 to 2030. This tool is a part of intelligent chatbots that goes through your knowledge base and FAQ pages. It gathers the question-answer pairs from your site and then creates chatbots from them automatically. However, you can find many online services that allow you to quickly create a chatbot without any coding experience. For example, conversational AI technology understands whether it’s dealing with customers who are excited about a product or angry customers who expect an apology. The difference between a chatbot and conversational AI is a bit like asking what is the difference between a pickup truck and automotive engineering.

The most successful businesses are ahead of the curve with regard to adopting and implementing AI technology in their contact and call centers. To stay competitive, more and more customer service teams are using AI chatbots such as Zendesk’s Answer Bot to improve CX. Consider how conversational AI technology could help your business—and don’t get stuck behind the curve. What customer service leaders may not understand, however, is which of the two technologies could have the most impact on their buyers and their bottom line. Learn the difference between chatbot and conversational AI functionality so you can determine which one will best optimize your internal processes and your customer experience (CX). In simpler terms, conversational AI offers businesses the ability to provide a better overall experience.

Yes, rule-based chatbots can evolve into conversational AI with additional training and enhancements. While chatbots operate within predefined rules, Conversational AI, powered by artificial intelligence and machine learning, engages in more natural and fluid conversations. Conversational AI is transforming customer service, enhancing user experiences, and enabling businesses to offer more personalized interactions. Compared to traditional chatbots, conversational AI chatbots offer much higher levels of engagement and accuracy in understanding human language.

Today’s businesses are looking to provide customers with improved experiences while decreasing service costs—and they’re quickly learning that chatbots and conversational AI can facilitate these goals. Customers reach out to different support channels with a specific inquiry but express it using different words or phrases. Conversational AI systems are equipped with natural language understanding capabilities, enabling them to comprehend the context, nuances, and variations in your queries. They respond with accuracy as if they truly understand the meaning behind your customers’ words. Businesses worldwide are going to deploy chatbots to automate user support across channels.

The conversations are sometimes designed like a decision-tree workflow where users can select answers depending on their use case. To say that chatbots and conversational AI are two different concepts would be wrong because they’re very interrelated and serve similar purposes. Conversational AI is enabling businesses to deliver the most personal experiences to their users by having more fluid and intelligent conversations.

This makes them a valuable tool for multinational businesses with customers and employees around the world. Because conversational AI uses different technologies to provide a more natural conversational experience, it can achieve much more than a basic, rule-based chatbot. Although they’re similar concepts, chatbots and conversational AI differ in some key ways. We’re going to take a look at the basics of chatbots and conversational AI, what makes them different, and how each can be deployed to help businesses. NeuroSoph is an end-to-end AI software and services company that has over 30 years of combined experience in the public sector.

Together, we will explore the similarities and differences that make the plan unique in its way. Conversational AI, on the other hand, can understand more complex queries with a greater degree of accuracy, and can therefore relay more relevant information. Let’s start with some definitions and then dig into the similarities and differences between a chatbot vs conversational AI.

• The system welcomes store visitors, answers FAQ questions, provides support to customers, and recommends products for users.
• You can also gather critical feedback after the event to inform how you can change and adapt your business for futureproofing.
• A rule-based chatbot can, for example, collect basic customer information such as name, email, or phone number.
• They have limited capabilities and won’t be able to respond to questions outside their programmed parameters.

Customer service teams handling 20,000 support requests on a monthly basis can save more than 240 hours per month by using chatbots. These bots are similar to automated phone menus where the customer has to make a series of choices to reach the answers they’re looking for. The technology is ideal for answering FAQs and addressing basic customer issues. Your customer is browsing an online store and has a quick question about the store’s hours or return policies. Instead of searching through pages or waiting for a customer support agent, a friendly chatbot instantly assists them.

Conversational bots can provide information about a product or service, schedule appointments, or book reservations. While virtual agents cannot fully replace human agents, they can support businesses in maintaining a good overall customer experience at scale. In fact, by 2028, the global digital chatbot market is expected to reach over 100 billion U.S. dollars. In order to help someone, you have to first understand what they need help with. Machine learning can be useful in gaining a basic grasp on underlying customer intent, but it alone isn’t sufficient to gain a full understanding of what a user is requesting. Using sophisticated deep learning and natural language understanding (NLU), it can elevate a customer’s experience into something truly transformational.

Both chatbots’ primary purpose is to provide assistance through automated communication in response to user input based on language. They can answer customer queries and provide general information to website visitors and clients. Rule-based chatbots can also be used to resolve customer requests efficiently. For example, they can help with basic troubleshooting questions to relieve the workload on customer service teams. Make sure to distinguish chatbots and conversational AI; although they are regularly used interchangeably, there is a vast difference between them.

While rule-based bots can certainly be helpful for answering basic questions or gathering initial information from a customer, they have their limits. For one, they’re not able to interact with customers in a real conversational way. Also, if a customer doesn’t happen to use the right keywords, the bot won’t be able to help them. When you integrate ChatBot 2.0, you give customers direct access to quick and accurate answers.

Get potential clients the help needed to book a kayak tour of Nantucket, a boutique hotel in NYC, or a cowboy experience in Montana. You can also gather critical feedback after the event to inform how you can change and adapt your business for futureproofing. Conversational chatbots vs conversational ai AI can help with tutoring or academic assistance beyond simplistic FAQ sections. At the same time, they can help automate recruitment processes by answering student and employee queries, onboarding new hires, and even conduct AI-powered coaching.

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. You can create bots powered by AI technology and NLP with chatbot providers such as Tidio. You can even use its visual flow builder to design complex conversation scenarios. The biggest of this system’s use cases is customer service and sales assistance.

Under the hood, a rule-based chatbot uses a simple decision tree to support customers. This means that specific user queries have fixed answers and the messages will often be looped. At their core, these systems are powered by natural language processing (NLP), which is the ability of a computer to understand human language. NLP is a field of AI that is growing rapidly, and chatbots and voice assistants are two of its most visible applications. Rule-based chatbots—also known as decision-tree, menu-based, script-based, button-based, or basic chatbots—are the most rudimentary type of chatbots. They communicate through pre-set rules (if the customer says “X,” respond with “Y”).

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. Additionally, these new conversational interfaces generate a new type of conversational data that can be analyzed to gain better understanding of customer desires. Those who are quick to adopt and adapt to this technology will pioneer a new way of engaging with their customers.