What’s The Difference Between Chatbots and AI?

Conversational AI vs chatbot

Chatbots and AI are often used interchangeably. In reality, the capabilities between the two vary greatly.

Chatbots vs. AI. What exactly is the difference?

Chatbots vs AI

As more companies look to chatbots and conversational AI to provide anytime, anywhere customer support, it’s important to have a clear understanding of the differences. The possible scope of an initiative and the end consumer experience is vastly different. Rules-based chatbots are limited to very basic scenarios. AI-powered virtual agents are capable of engaging in much more natural, 1:1 conversations and resolve a wide-variety of customer support issues.

As the customer experience plays an increasingly critical role in consumer buying decisions and loyalty, companies must ensure that they are adopting the right technology for their business and their customers. 

Chatbots vs AI
If a person asks something it is not explicitly trained to handle, chatbots are easily confused. Conversational AI enables users to engage in natural, human-like conversation.

Chatbots: No AI, Ideal for Straightforward, Highly Predictable and Limited Scenarios.

Rules-based chatbots can automate very specific scenarios, such as looking up an order status or browsing through a product catalog. Basic chatbots don’t allow for a person to engage naturally in a conversation, but rather move the conversation forward via bot-prompted keywords or U/X features like Facebook Messenger’s suggested responses. 

If a bot does have the ability to understand specifically-trained questions, it’s often limited. Specific variations of a question must have been pre-trained for the chatbot to accurately understand a person’s intent. For instance, if a bot was trained to understand “Where’s my order?” but your customer asks the same question slightly differently, “Is my package arriving today?”, the bot will fail to properly map to the order status intent unless it has been explicitly trained to do so.

chatbot vs AI
Chatbots are easily confused, lacking Semantics to understand the context of a message and pick up on keywords only.

The user journeys with rules-based bots are often alinear. If a person says something that is not preempted by the bot or tries to correct themselves, the chatbot gets confused. It will most likely repeat the same question until it gets a response that it understands. For example, a chatbot designed to help people order a pizza will not know how to respond to a customer asking for nutritional facts in between selecting toppings. 

Users are handcuffed in how they can engage in a conversation. People are often prompted to repeat themselves (getting stuck in a loop) or start a conversation fresh. 

Chatbot training is a very manual process. Every flow and utterance of a question is programmed. A human workforce also identifies and implements ongoing improvements. 

If you’re deploying a rules-based bot, make sure that you select the right very specific use case. Fandango, for instance, has a bot that asks people for their zip code and pulls up movies playing locally. The Wall Street Journal lets users type in a stock symbol to get live stock quotes. These are both very specific and well defined use cases which work well for bots. 

With a chatbot, be upfront with your customers on its capabilities. You’ll need to provide an alternative method of getting support for other matters (i.e. I’m the Order Tracking bot. To find your order, type in your confirmation number below. If you need something else, please call ….). 

Conversational AI: Continuously-improving, Human-like Experience Personalized to the Individual  

AI-powered virtual agents provide a more human-like experience, are capable of carrying on natural conversation and continuously improve over time. 

AI Agents leverage semantics to accurately understand the context of what a person is saying, even if it has not been explicitly trained with the exact phrasing. For example, if a person types a long-form message about an issue with a product — “I got the side table delivered yesterday but it looks like it might have been broken while in route. There’s a crack in the front. Can you help me? I would like my money back.” — an AI would be able to decipher that a person is looking to return item and receive a refund. Conversational AI thinks like a human, not a robot. 

AI enables companies to provide hyper-relevant personalized engagement, not generalized support. Like humans, AI Agents are able to decide the next best action based on a variety of things including contextual-factors, customer profile, sentiment or business policies. It can alter how it responds based on a real-time sentiment analysis. For instance, an AI Agent treats a person who checks the status of their (on-time) flight differently based on how they react: a person who gives a thumbs up versus a person who responds with “Oh no!!” — presuming the traveler is likely to miss their flight.  

Conversational AI supports multi-turn dialogue, or the ability to switch between various user questions within a single conversation. This is what sets apart a human-like AI versus a robotic chatbot experience. If a person pivots the conversation, an AI Agent accurately responds without getting confused. For instance, a person can ask about the price of checking a bag in the midst of checking flight status. AI can also understand more short-form and slang than chatbots. 

An AI is trained through a combination of supervised and unsupervised learning. AI can learn from historic data. With customer service, this includes email, chat and messaging logs, to identify and group together similar questions and scenarios. Training is on auto-pilot: An AI learns how a situation was previously handled and teaches itself to act in the same way. 

AI also uses deep reinforcement learning to improve over-time based on real-life interactions. AI is able to determine patterns based on how end users are responding in various circumstances. This can be based on things like customer segmentation and contextual factors. For instance, if meal-delivery customers have issues with changing their subscription day, an AI would learn to proactively offer this information. 

The richness of the technology has Gartner predicting that by 2021, 15% of all customer service interactions will be completely handled by AI1.   

What To Keep In Mind As The Differences Between Chatbots vs. AI

Chatbot and AI adoption is skyrocketing. At first look chatbots and AI might appear very similar. When you go below the surface, though, the technology could not be more different. The initial training, the ongoing improvement and the end-customer experience are not even close to being in the same league. 

Interested in learning more about chatbots vs. AI? We’d love to discuss how our powerful AI platform provides the frustration-free experience your customers expect. Don’t use a robotic, limited chatbot that plummets your CSAT.  Let’s chat. 

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