- February 17, 2020
- Posted by: admin
- Category: Artificial Intelligence
The worldwide industry for Conversational AI is projected to touch a market value of 15.82 billion USD by the year 2024, with a CAGR of 30.32% growth rate.
Conversational AI’s rise took place when exasperated users signed up for it after experiencing difficulties with chatbots. The human touch was missing in chatbot solutions, and to fill the gap, Artificial Intelligence tried to be human enough so the machines can do all the talking to the customers on behalf of enterprise personnel.
That brings us to wonder what is Conversational AI’s main goal.
Well, put in simple words, conversational AI works via messaging apps, speech-based assistants and chatbots to not only provide better automation but offer a greater degree of personalization in terms of customer experience.
Organizations have designed humanoid robots i.e. robots that look and talk like humans, to end the frustrating experience of customers with chatbots. Now, although Natural Language Processing technologies are achieving new heights with the evolving AI trends, they still leave scope for humans to identify and correct errors. This triggered the concept and demand for conversational AI.
One example would be a chatbot ‘Amelia’ of IPsoft transferring a query to a human agent when she is unable to solve it at any cost. But in the process, she learns and makes notes for further improvements for the next time.
Why are robots learning to converse with humans?
Chatbots are mostly designed in a way to generate instant responses without considering the overall context or intent of the individual trying to seek an answer. For example, somebody looking for brunch details of a restaurant might be different from another one looking for dinner details at the same venue; but chatbots might not be able to distinguish between these intricate differences.
Most of the chatbots make use of decision-trees offering predictable responses for every consumer, which reduces the level of personalized customer experience to a great extent. But that does not imply that enterprises are absolutely sure that using conversational AI would enable them to reach their desired goal and maximize revenues.
Even if the context comes in and brings along sentiment, intent and language parsing, it would not be sufficient to draw meaning from word structures. Humans can see go beyond the literal meaning of words with their individual perspectives, but machines cannot.
As per an Artificial Intelligence researcher at BYU’s Perception lab in Utah, if companies aim to create human-like conversations, they need to invest greater knowledge and find ways to imitate the human common sense and then correlate it to contextual information.
What are the enterprise’s natural learning solutions?
As enterprises cannot afford to lose their customers due to a lack of customized solutions, they are now focusing on implementing natural learning solutions.
According to Mark Beccue, modern customers demand natural learning applications that concentrate on a specific vertical so that they can eliminate errors while dealing with certain variations in the spoken language.
This process of designing natural learning solutions begins with initial testing and learning so that companies can tweak their verticals, enhance customer experience, and increase their chances of winning.