Conversational AI vs Chatbot: What’s the Difference

They converse through preprogrammed protocols (if customer says “A,” respond with “B”). Conversations are akin to a decision tree where customers can choose depending on their needs. Such rule-based conversations create an effortless user experience and facilitate swift resolutions for queries.

• Chatbots have various applications, but in customer support, they often act as virtual assistants to answer customer FAQs.
• Instead, conversational AI can help facilitate the creation of chatbot use cases and launch them live through natural language conversations without complicated dialog flows.
• AI technology is advancing rapidly, and it’s now possible to create conversational virtual agents that can understand and reply to a wide range of queries.

Conversational AI refers to a technology that enables computers or machines to engage in human-like conversations with users. It combines natural language processing (NLP), machine learning, and other techniques to understand and conversationally respond to human input. Conversational AI systems can be found in chatbots, virtual assistants, and voice-enabled devices. A chatbot can be found in various forms, ranging from simple rule-based systems to more sophisticated AI-powered models.

Chatbots vs Conversational AI: Understanding the Difference

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. Babylon Health’s chatbots vs conversational ai 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 issues commonly observed in primary care.

Crucially, these bots depend on a team of engineers to build every single flow, and if a user deviates from the pre-built script, the bot will not be able to keep up. Conversational AI is a general name that describes any technology that detects and responds to human inputs, whether they come in via text or speech. In the second scenario above, customers talk about actions your company took and stated what they expect to happen. AI can review orders to see which ones were canceled from the company’s side and haven’t been refunded yet, then provide information about that scenario. In fact, artificial intelligence has numerous applications in marketing beyond this, which can help to increase traffic and boost sales. Conversational AI draws from various sources, including websites, databases, and APIs.

Chatbot features:

Conversational AI refers to technologies that can recognize and respond to speech and text inputs. 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.

It can give you directions, phone one of your contacts, play your favorite song, and much more. This system recognizes the intent of the query and performs numerous different tasks based on the command that it receives. On the contrary, conversational AI platforms can answer requests containing numerous questions and switch from topic to topic in between the dialogue.

Difference Between Chatbot and Conversational AI

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. The most common type of chatbot is one that answers questions and performs simple tasks by understanding the conversation’s words, phrases, and context. These basic chatbots are often limited to specific tasks such as booking flights, ordering food, or shopping online. While often used interchangeably, chatbots and conversational AI represent distinct concepts.

This reduces wait times and allows agents to spend less time on repetitive questions. 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”). The conversations are sometimes designed like a decision-tree workflow where users can select answers depending on their use case. You can make the most of your strategy by looking into customer support AI solutions.

True AI does not rely on human effort to create decision trees for incoming support queries to then try to answer queries based on keyword matching. Conversational AI offers more of the true AI experience since it is not trying to match human language with a keyword. Conversational AI is enabling businesses to deliver the most personal experiences to their users by having more fluid and intelligent conversations.

Check out this guide to learn about the 3 key pillars you need to get started. So when customers ask a conversational AI bot a question that sounds a little different than previous questions it has encountered, it can still figure out what they’re trying to ask. One of the most common questions customers will ask about is the status of their shipment. With a chatbot, you’d have to be exact with your verbiage in order for the machine to give out the answer you’re searching for based on user inputs. Zowie is the most powerful customer service conversational AI solution available. Built for brands who want to maximize efficiency and generate revenue growth, Zowie harnesses the power of conversational AI to instantly cut a company’s support tickets by 50%.

Conclusion: Chatbot vs AI Chatbot – Which Solution is Better for Your Business?

It’s important to know the differences between chatbot vs. conversational AI, so you can make an informed decision about which is the right choice for your business. Chatbots can sometimes be repetitive, asking the same questions in succession if they haven’t understood a query. They can also provide irrelevant or inaccurate information in this scenario, which can lead to users leaving an interaction feeling frustrated. Conversational AI is capable of handling a wider variety of requests with more accuracy, and so can help to reduce wait times significantly more than basic chatbots. Conversational AI can also be used to perform these tasks, with the added benefit of better understanding customer interactions, allowing it to recommend products based on a customer’s specific needs. As we’ve seen, the technology that powers rule-based chatbots and AI chatbots is very different but they still share much in common.

Repetitive questions that companies see everyday are handled well with a chatbot since support teams can manage incoming customer questions better and answer them efficiently. Conversational artificial intelligence (AI), on the other hand, is a broader term for any AI technology that helps computers mimic human interactions. A chatbot is an example of conversational AI that uses a chat widget as its conversational interface, but there are other types of conversational AI as well, like voice assistants. If you don’t need anything more complex than the text equivalent of a user interface, chatbots are a simple and affordable choice.

interview questions commonly asked to AI Product Managers, along with sample answers:

When a visitor asks something more complex for which a rule hasn’t yet been written, a rule-based chatbot might ask for the visitor’s contact details for follow-up. Sometimes, they might pass them through to a live agent to continue the conversation. 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. 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. For example, the Belgian insurance bank Belfius was handling thousands of insurance claims—daily!

Although they take longer to train initially, AI chatbots save a lot of time in the long run. Because customer expectations are very high these days, customers become turned off by bad support experiences. These days, customers and brands say they care more about the customer experience than ever before, so it’s important to have the right tools in place to bring those positive experiences to fruition. Basic chatbots rely on pre-determined decision trees that require exact keyword matching to return the right output for the given customer input. Operational AI helps perform an operation or a function that allows for knowledge intake, while conversational AI helps with the back-and-forth between customers and agents for any customer support interaction. So while the chatbot is what we use, the underlying conversational AI is what’s really responsible for the conversational experiences ChatGPT is known for.

Because conversational AI can more easily understand complex queries, it can offer more relevant solutions quickly. Conversational AI uses technologies such as natural language processing (NLP) and natural language understanding (NLU) to understand what is being asked of them and respond accordingly. We saw earlier how traditional chatbots have helped employees within companies get quick answers to simple questions. Users can speak requests and questions freely using natural language, without having to type or select from options. SendinBlue’s Conversations is a flow-based bot that uses the if/then logic to converse with the end user.

A decade later, Kenneth Mark Colby at the Stanford Artificial Intelligence Laboratory created a new natural language processing program called PARRY. Although it was the first AI program to pass a full Turing test, it was still a rule-based, scripted program. Without deep integrations with company-specific data and the systems and apps within your organization, conversational AI use cases will be lackluster at best and downright useless at worst. Questions that your rule-based chatbot can’t answer represent an opportunity for your company to learn. You can easily tweak and modify the rules, whereas machine learning is more difficult to course-correct when things go wrong. With AI tools designed for customer support teams, you can improve the journey your customers go through whenever they need to interact with your business.