Every company, every enterprise is in search of tech innovations, a drive that can herald an industrial revolution like never before. It is a quest for process-driven technological capability that can take an organisation to the next level altogether. Institutions across the world are leaving no stone unturned when it comes to their research and development practices, to apply Artificial Intelligence in their respective businesses. Artificial Intelligence, or popularly called AI is being implemented in all possible processes for extraordinary capability and high business impact.
However, even as this keen interest in AI is new, the technology isn't. It has been in discussion for quite some time now. That traditional AI never was much into reasoning as much as automation. The age-old AI approach made an impression with its innovative techniques but did not have such a momentous hold on the minds of the consumers like today. And that was perhaps because it lacked on the reasoning part substantially.
This new-age AI for today’s businesses harps primarily on deep reasoning and brings about a shift in the earlier dependence on the known datasets. Instead, today’s deep reasoning helps to perform undocumented learning from the datasets that are not only large but unlabeled as well. This helps to reason that AI can be applied much more broadly in today’s competitive times.
So, how come the scenario is changing so drastically? What has taken place to bring about this dramatic shift? Well, it isn’t the new algorithms only that are bringing about the change. It's because of the progressions in the AI procedures that can make the most of the relevant converging trends – computing is turning out to be more flexible and ambient, and large data sets are becoming convenient when it comes to extracting them for the right uses, storing them for the relevant reasons, managing them for the best of the capabilities and finally using them.
And this scenario is becoming more relevant in today’s age. Businesses are working with clients to mechanise customer support with the help of these basic techniques. AI helps us by taking in a body of knowledge and learning when it comes to responding to customer service queries, and keep up the learning as a continuous process, with more at every interaction, across a wide variety of areas. And this is augmenting growth and bringing in enhanced functionalities unwitnessed before.
It is evident that AI has a very critical reliance on data. The challenge is that this data normally exists in a very transient state, mostly across innumerable sets of legacy systems. That is why the need is to systematically capture and utilise them in a strategic pattern. The ultimate goal is to help build AI algorithms, which can drive substantial digital transformation for the business processes. These are certain essential details of data strategy, which often lose their relevance when only AI is harped upon.
However, for those who are out to venture on the AI journey, data is a crucial insight that needs significant reflection.