Artificial intelligence (AI) does not exist in the world of science fiction. As a technology, AI-driven chatbots are revolutionizing business processes in multiple industries, while also impacting several aspects of our life, and how we interact with people in the virtual world. Therefore, as various markets fully embrace AI, they get smarter in today's always-on world. According to several analysts, the global chatbot market size, valued at $525.7 million in 2021, is expected to grow at a compound annual growth rate (CAGR) of 25.7 per cent from 2022 to 2030. The industry attributes such phenomenal growth to the rapid adoption of customer service activities by online enterprises and e-commerce businesses to reduce operating costs.

Artificial intelligence is one of the most powerful technologies for reshaping business in decades. It has the ability to optimize many processes throughout organizations and is already the engine behind some of the world's most valuable platform businesses. In our view AI will become a permanent aspect of the business landscape and AI capabilities need to be sustainable over time in order to develop and support potential new business models and capabilities. Specifically, we believe that companies need to establish dedicated organizational units to entrench AI. This is an important business tool that cannot be left to bottom-up whimsy.

What is the difference between a chatbot and a virtual assistant?

Deep Learning is a type of Neural Network Algorithm that takes metadata as an input and process the data through a number of layers of the non-linear transformation of the input data to compute the output. This algorithm has a unique feature i.e. automatic feature extraction. This means that this algorithm automatically grasps the relevant features required for the solution of the problem. This reduces the burden on the programmer to select the features explicitly. This can be used to solve supervised, unsupervised or semi-supervised type of problems. In Deep Learning Neural Network, each hidden layer is responsible for training the unique set of features based on the output of the previous layer. As the number of hidden layers increases, the complexity and abstraction of data also increase.