Since their evolution, chatbots have grown from delivering linear, scripted user experiences to providing unsupervised and contextually-aware engagement.
One of the earliest chatbots created at the MIT Artificial Intelligence Laboratory, Eliza interacted using scripts and leveraged pattern matching and substitution technology. It had no built-in provision for contextualizing events. Like Eliza, many first-generation, rule-based chatbots were used for answering simple FAQs. Such chatbots did not leverage automated, machine-learning technology and required 6-9 months to train manually. Moreover, training them was an ongoing process, and the entire investment did not deliver the requisite ROI.
Over time, as customers and employees started demanding interactive, real-time, and personalized omnichannel engagement, organizations needed sophisticated AI-enabled chatbots to meet their expectations. Consequently, chatbots evolved to conversational AI with powerful capabilities, including machine learning, natural language processing (NLP), intent extraction, and sentiment analysis.
Let’s understand further.
Markets and Markets predicts, The global conversational AI market size is expected to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a CAGR of 21.9% during the forecast period.
So, it brings us to the real question, “What is Conversational AI?”
Conversational AI is a set of advanced technologies, including natural language processing (NLP), natural language understanding (NLU), machine learning, and speech recognition, to process written and verbal inputs and respond accordingly in a natural, human-like manner.
Conversational AI bots pull out entities and intents and can comprehend the nuances of the language, including grammar, slang, and canonical word forms. Moreover, they are trained to understand the type and intensity of the user’s emotion and respond accordingly.
For example, I am trying to find a new red dress. Here, “find” specifies the intent, while “red” and “dress” are the fundamental entities of the user’s request.
Or, take this example. The customer types, “I am pissed off with your delivery agent.” Here, the chatbot will identify the emotion (which is anger in this case) and rank the sentiment based on its intensity.
However, it is essential to note that there are fundamental differences between a chatbot and a truly conversational AI engagement.
Chatbots can be of two types –
(i) rules-based and
(ii) AI-driven. As seen above, a rules-driven chatbot follows a pre-defined workflow or script. In contrast, AI-driven chatbots understand the conversation’s context and the user’s intent and engage in a meaningful, dynamic dialog. As a result, an AI-enabled bot makes you feel that you are interacting with a human and not a computer.
|Rule based Chatbot||Conversational AI|
|Hi, how may I assist you? Type “Place Order” or “Check Menu.”||Hi, how may I assist you?|
|Where is my order?||Where is my order?|
|I’m sorry, I don’t understand. Type “Place Order” or “Check Menu.”||Your order is dispatched and will reach you by 8:27 pm.|
|I don’t need this. I need to know when my order will reach me.||Thank you.|
It’s clear from the example above that while a rules-driven chatbot carries out a keyword-based chat, a conversational AI chatbot uses NLU to gauge what the user is looking for at the moment and how specific topics relate to each other. Additionally, simple chatbots are trained on 100-200 customer intents; an AI-chatbot, on the other hand, is pre-trained on thousands of industry-specific customer intents and use cases.
In the journey of chatbots, conversational AI is where we stand today. Human language is complex, and conversational AI provides many advanced capabilities that let organizations go above and beyond scripted resolution paths. Some of these include –
Take the example of BlenderBot, the largest-ever open-sourced chatbot by Facebook.
The bot blends a host of conversational skills – empathy, persona, and knowledge – together with improved decoding techniques and a large-scale neural model with 9.4 billion parameters and a 14-turn conversation flow – making it one of the most engaging and human conversational-AI chatbots. Compared with Google’s Meena chatbot, 67% of the respondents claimed BlenderBot sounds more human, while 75% said they would prefer having a more extended conversation with BlenderBot than with Meena.
In fact, Facebook has done in-depth research on how often human evaluators preferred their chatbots over human-to-human chats over time, and the results are depicted below.
With the plethora of benefits they provide, it is clear that organizations must adopt chatbots with conversational AI capabilities.
Here’s our 101 to help you get started with conversational AI.
Today, with its innumerable advantages to business, conversational AI is being deployed in various consumer and employee use cases and processes, such as IT and security management, marketing, human resource, insurance, retail, banking and financial services, and healthcare.
At Acuvate, we help clients build conversational AI chatbots with our low-code enterprise bot-building platform called BotCore. With minimalistic coding requirements and a visual interface, our bots can be built and deployed within a few weeks, support multiple languages like French, German, Italian, etc., and can handle simple and complex conversations alike.
To know more about BotCore, please feel free to schedule a personalized consultation with our experts.