The global chatbot market is anticipated to reach $9.4 billion by 2024. This comes as no surprise given the value proposition that chatbots and conversational AI bring to the table—24/7 customer assistance at lower operational costs topping the list.
The terms chatbot and conversational AI are often used interchangeably. But they aren’t really synonymous. In fact, customer service leaders need to understand both technologies to identify which would have the most impact on their buyers and their bottom line.
Let’s decipher the difference between chatbots and conversational AI so you can streamline your internal processes and amplify both customer and agent experiences.
What are Chatbots?
Chatbots are computer programs designed to mimic human conversations to create better customer experiences. Some bots function based on predefined conversational flows, while some leverage artificial intelligence (AI) and natural language processing (NLP) to decipher user intent and send automated, contextual responses in real time.
Traditional chatbots are effective in managing a narrow range of tasks. They leverage rule-based programming to match user queries with predefined answers, often for basic FAQs. For example, if the customer asks “X”, reply with a “Y”. Rule-based chatbots’ limitations become apparent when they receive a request for which they aren’t trained. In such a scenario, they fail to assist the user and often generate an unhelpful response such as "Sorry, I don't understand."
What is Conversational AI?
Conversational AI is an umbrella term for AI-driven communication technologies like chatbots and virtual assistants. The interaction can occur through a bot in a messaging channel or a voice assistant on the phone. Rather than sticking to a rigid structure, conversational AI leverages natural language understanding (NLU), deep learning, ML, and predictive analytics to deliver a more dynamic user experience.
NLU: The Key to Unlocking Conversational AI’s Potential
Is it possible to provide meaningful help without understanding what a person needs help with? Not really!
Machine learning sets the context of user intent on a basic level, but it falls short of truly grasping the entirety of a user’s request. That is where advanced technologies such as deep learning and natural language understanding (NLU) work their magic. They empower conversational interfaces to better understand human language by segmenting words and sentences, discerning grammatical structures, enabling speech recognition, and leveraging semantic knowledge to deduce intent. The image below explains how:
Chatbot Vs. Conversational AIExploring the Potential Pros & Cons of Rule-Based Chatbots & Conversational AI
In a Nutshell
One can easily gauge from the charts above that conversational AI offers more benefits. Yet, chatbots are more prevalent and utilized on a global scale. The simple reason being the specific requirements of organizations vary on their sizes, sectors, and business models.
For example, if you are an owner of a medium-sized fashion brand who’s looking for a solution to ramp up customer engagement efforts, then a chatbot is the best fit for you. It can assist your burgeoning clientele with the tracking status of their online purchases. The predefined conversational flow, leveraging bot-prompted keywords or UX features like suggestion buttons, will allow users to enter their order number, check their status, or request exchanges and refunds.
So, for small-medium businesses or large organizations that require to fulfill a single task, a chatbot best fits the bill. On the flip side, when data-intensive enterprises such as healthcare and banking need assistance with an extensive array of services, conversational AI should be the go-to solution.
Which one are you leveraging in your organization and how has it been a game-changer? Do let us know in the comments below.
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