From Chatbots to Conversational AI


orporations of all sizes have been looking for ways in how to build greater interactions between various stakeholders ranging from employees to customers. With the emergence of Chatbots in 2000’s corporations had a mechanism for undertaking just that. Companies could now adopt nascent natural language technologies to generate traffic, usage and engagement.

The chatbot craze was at its peak just a couple of years back, fueled by Facebook’s announcement of a developer-friendly platform for building them. Soon, toolkits that helped build a bot in five minutes were flooding the market and companies were making new bot announcements at the speed of light. Today, we realize that most of these chatbots were pointless.

Understanding Chatbots

Chatbots are scripted programs usually deployed as an add-on to a business’s website, social media pages or standalone apps. They interact with customers, mimicking an actual person using natural language processing. While chatbots were introduced to offer customers a more personalized interaction compared to catalogues and forms, they followed a linear predefined conversation route.

They could only tackle conversations with an outlined flow- ordering food, making a restaurant reservation etc. Initially successful, underdeveloped and redundant bots that failed as soon as the user deviated from its limited scripted patterns caused the chatbot revolution to meet the same fate as its predecessor. Clearly, chatbots weren’t equipped with the level of comprehension that was required to understand the intricacies of human conversation.

The emergence of conversational AI

With chatbots failing to deliver on expectations, businesses are now turning towards conversational AI platforms, especially those in banking, insurance and telecommunications – industries with complex use cases. This artificial intelligence-driven customer service technology is capable of productive interactions with customers that are human - casual, friendly and relaxed.

Leveraging multiple technological advancements such as speech synthesis, natural language understanding (NLU), machine learning and cognitive technologies, conversational AI can break free from the boundaries of scripted interactions and hold natural conversations with users, replying to queries based on dynamic programming rather than following a script. Such conversationally intelligent chatbots will be able to understand human speech, sense the tone and context, and respond accordingly.

A modern conversational AI differs from its predecessor in the following ways:

Multichannel: One of the most discerning factors of conversational AI is that it can interact with its user over multiple channels via multiple media such as voice, text or query, compared to chatbots which were solely limited to text chats

Machine Learning: Conversational AI has the ability to learn from past interactions, conversations. The platform uses these learnings to drive the conversation with its user instead of a fixed script

Cognitive Abilities: A conversational AI understands the complications of human conversation - nuances, slangs, mispronunciation, abbreviations - while navigating through communications seamlessly

Retention: The AI platform stores user data and preferences and creates a unique and personalized experience for every user based on their preferences and interaction history. This was beyond the scope of a traditional chatbot

Transaction: Chatbots where intended to act as a bridge between customer and vendor whereas a modern AI platform is equipped to securely and accurately carry out complex transactions such as making a purchase or renewing subscriptions automatically, thus effectively removing the human element from the process

With vastly improved functionalities such as these, conversational AI has seen widespread application across industries. Platforms such as IBM’s Watson, KAI and Google’s Assistant have been evolving each day into more complex systems that might in the future even completely eliminate the need for human interaction in customer service.

AI in different industries

Eliza, one of the first chatbots to be developed, was a computer program that simulated an actual therapy interaction. Developed in 1966, she assumed the role of a psychotherapist, to the extent that many patients believed they were speaking with a human. Following that trend, the development of newer chatbots and presently conversational AI has seen widespread adoption in the healthcare industry. Bots are now used to provide preliminary diagnostics by interacting with patients before they talk to a doctor. They can even monitor the condition of patients under treatment and conduct checkups. These AI platforms are backed by huge datasets of diseases, symptoms and cure as well as diagnostic and medical histories of individual patients, based on which they structure their interactions. In the future, these platforms are expected to monitor and detect health problems automatically and take important medical decisions on the doctor’s behalf.

Not just healthcare, conversational chatbots can engage customers in different industries – banking, insurance etc., improving their experience without the need for a customer support team. Using natural language processing to assess the customer’s requirements, they will also use knowledge management to offer relevant and helpful answers.

Conversational AI in the future

Despite the failure of the chatbot craze, AI technology is making great strides in the business world. Intelligent conversational interfaces offer businesses a simple way to interact with and engage customers, suppliers, and employees. These intelligent assistants learn and continue to evolve each day. In the future, conversational AI will be lapped up by enterprises as its benefits will be recognized by a slew of industries.