01 Feb
The Evolution of Chatbots to Conversational AI
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.
What is Conversational AI?
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.
Rule based Chatbot vs. Conversational AI
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.
The evolution of chatbots: Where do we stand today?
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 –
- Context management – With conversational AI, bots will always learn from past user interactions and remember important details, including client information, customer preferences, employee profile, etc., making it easier to hold personalized, context-rich conversations.
- Sentiment analysis – As seen above, conversational AI bots comprehend the tone and emotion of a user’s utterance and respond accordingly; for example, they may steer the conversation in a different direction, alter the style, or bring in a human agent to take over the conversation.
- Dialog management – Human conversations are strewn with twists and turns. Conversational AI empowers bots to handle such complex dialog changes, including entity change, processing multiple entities within a single utterance, etc.
- Omnichannel and multilingual support – Conversational AI allows users to start a conversation in one channel (e.g., WhatsApp) and end it in another (e.g., Facebook) without losing context or continuity. Moreover, organizations can reach out to a global audience with chatbots that support different languages, like French, German, Italian, etc.
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.
- They deliver interactive, tailor-made, and value-adding engagements to build better customer and employee relationships.
- Personalized and immersive customer and employee experiences boost customer loyalty, build brand image, and increase employee productivity.
- Since conversational AI chatbots learn from past conversations and any new data that enters the system, they can accurately predict what users want to develop specific responses and upsell by offering personalized product recommendations.
- Moreover, since such bots rely on their taxonomy and cognitive capabilities to deliver self-service resolutions at scale, the return on investment is also high.
The Path Forward - How to get started with Conversational AI
Here’s our 101 to help you get started with conversational AI.
- Plan and Strategize on how conversational AI can be integrated with different business units.
- Build a powerful case for conversational AI by spreading the word amongst the various stakeholders.
- Choose the right conversational AI platform that helps you build, deploy, and train your chatbots.
- Determine the gap between your existing human and technical resources and those required for smooth implementation.
- Quantify the business value of conversational AI deployment, including improved CSAT, reduction in support costs, and other metrics.
- Launch a pilot project.
- Scale and optimize conversational AI for the entire organization.
Final Thoughts
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.
Abhishek is the AI & Automation Practice Head at Acuvate and brings with him 17+ years of strong expertise across the Microsoft stack. He has consulted with clients globally to provide solutions on technologies such as Cognitive Services, Azure, RPA, SharePoint & Office 365. He has worked with clients across multiple industry domains including Retail & FMCG, Government, BFSI, Manufacturing and Telecom.
Abhishek Shanbhag