Cognitive computing lets a machine learn from the environment around it, much like humans, by processing data and natural language. Apart from NLP and ML, such systems also use speech recognition, reasoning, computer vision, dialog and narrative generation, and human – computer interaction to make decisions. Among all these, NLP has been the most heavily used technology, as it lets a computer understand human language and take useful insights out of it. As it makes the translation process between machines and humans faster and more efficient, thereby informing users on terrorist threats, changing market conditions, social issues, and spams, its usage is increasing.

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Every day, 2.5 quintillion bytes of digital data is generated, as per an article published on the website of Forbes, in 2018. Humans, with all the conventional analysis techniques, are only able to study a small fraction of this data. The huge unstructured data sets often contain valuable information, which can potentially drive revenues for organizations. This is why, the need for a technology which can convert the unstructured sets into structured data is dire. With the usage of artificial intelligence (AI) technologies, such as machine learning (ML) and natural language processing (NLP), to accomplish the task, the cognitive computing market is growing across the world.

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Currently, large enterprises are the most significant users of this technology, as they generate relatively higher volumes of data than small and medium enterprises (SME). Additionally, AI technologies are costly, which is why they are deployed mostly by large companies, which have higher budgets. But, with the increasing demand for them and technological advancements, which are helping bring down their costs, even SMEs are adopting them. In fact, the procurement of cognitive computing by SMEs would be faster as compared to large firms, in the coming years.