Conversational AI chatbot solutions and artificial intelligence have never been more popular than they are today. In fact, data from Google Trends shows that interest in chatbot AI solutions has increased ten-fold over the last 5 years.
During this explosion of interest, “chatbot” has evolved into an umbrella term that may inaccurately describe what a chatbot can and cannot do. Chatbots and AI are often used interchangeably. In reality, the capabilities between chatbot technology and artificial intelligence are very different.
Chatbots vs. AI. What exactly is the difference?
More companies are looking to virtual assistants and conversational interfaces to provide anytime, anywhere customer support. So, it’s important to have a clear understanding of different technologies. That’s because the scope of a conversational agent initiative and the end-user experience is vastly different. Rules-based chatbots are limited to very basic scenarios. On the other hand, AI-powered virtual assistants are capable of engaging in natural language understanding, participating in 1:1 conversations due to machine learning, deep learning, and conversational experience.
Companies must ensure that they are adopting the right technology for their business and their customers. This is because the customer experience plays a critical role in consumer buying decisions and loyalty.
Rules-Based Chatbot: AI Not Found, Ideal for Straightforward, Highly Predictable and Limited Scenarios.
Rules-based chatbots can automate customer service in very specific scenarios. For example, looking up an order status or browsing through a product catalog. Basic chatbot technology moves the conversation forward via bot-prompted keywords or UX features like Facebook Messenger’s suggested responses. (As compared to typing in a question in free-form, using slang and engaging naturally in a conversation).
AI chatbot platforms have limited, if any, natural language processing. Variations of a question must be pre-trained for a chatbot to accurately understand what a person is trying to say. For instance, a virtual assistant is trained to understand “Where’s my order?” If a customer asks the same question slightly differently, “Is my package arriving today?”, the bot will not accurately understand the intent of the question is “order status.”
That is, unless it has been explicitly trained to do so within the labeling and learning provided in its training data.
Rules-Based Bots And The User Experience
The user experience with rules-based bots is often alinear. If a person says something that is not preempted, a chatbot will get confused. The virtual assistant will most likely repeat the same question until it understands a response. For example, a chatbot designed to help people order a pizza will not know how to respond to a customer asking for nutritional facts as they are selecting toppings.
How to Train Rules-Based Bots
Chatbot training is a manual process, and requires programming every flow and utterance of a question. A human workforce also identifies and implements ongoing improvements.
If you’re deploying a rules-based bot, make sure that you select a very specific use case. Fandango, for instance, has a bot that asks people for their zip code and pulls up movies playing locally. In another example, The Wall Street Journal lets users type in a stock symbol to get live stock quotes. These use cases are very specific and well defined, and work well for bots.
Be upfront with your customers on a chatbot’s 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 Chatbot 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 use natural language processing and leverage semantics to understand the context of what a person is saying. For example, a customer 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-powered virtual agent would be able to decipher that a person is looking to return an item and receive a refund. An AI thinks like a human, not a robot, and is able to maintain a conversational flow.
Conversational Chatbot Solutions And The User Journey
AI enables companies to provide hyper-relevant personalized engagement, not generalized support. Like humans, AI virtual agents are able to decide the next best action based on a variety of things including contextual-factors, customer profile, sentiment or business policies. Furthermore, 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 virtual agent would presume that a person who responds with “Oh no!!” that they are likely to miss their flight.
Conversational chatbot solutions powered by AI also support multi-turn dialogue. This is the ability to switch between various user questions within a single conversation. This is what sets apart a human-like AI versus building chatbots. An AI-powered virtual agent responds without getting confused if a person pivots the conversation. 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.
How to Train Conversational Chatbot Solutions
AI training is a combination of supervised and unsupervised learning. AI can learn from historic data. With customer service, this includes customer support 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 has been 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-powered virtual agents are able to determine patterns based on how end users are responding in various circumstances. This is 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. The best AI chatbots tend to be the most self-sufficient when it comes to adapting. When you hear about terrible chatbot fails, those are likely stemming from less-sophisticated bots and/or an improper way to set them up – basically launching a bot without enough training.
What To Keep In Mind As The Differences Between Chatbots vs. Conversational Chatbots With AI
Conversational chatbot and AI adoption is skyrocketing. In fact, according to Accenture, 60% of surveyed executives plan to implement conversational bots for after-sales, customer service, and social media. Accenture isn’t the only organization projecting big movement within the chatbot space – just take a look at these very telling chatbot stats. At first glance, chatbot technology and AI-powered conversational interfaces 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 artificial intelligence and chatbot technology? We’d love to discuss how our powerful AI customer service platform provides the frustration-free experience your customers expect. Don’t use a robotic, limited chatbot solution that plummets your CSAT. Let’s chat.