It is heavily speculated that the number of applications of artificial intelligence are going to far exceed the applications of information technology. Although we are still at the beginning of this technological evolution, we already see many real applications of AI, and these applications are expected to grow further.

Companies, big and small, are realising that they need to adopt AI technologies to compete and stay relevant with the current trends. The essential fuel of AI is data, and the ability of companies to capture, store and manage data will define their capability to adopt AI. Companies that have invested heavily in building strong data foundations for their business intelligence applications are clearly ahead of the game in onboarding the journey of AI.

The key difference between the traditional data mining and business intelligence systems and the modern ones that leverage data science is the removal of intermediate human intervention that was traditionally required. This means that machines will actually take many decisions on their own, just to manage and improve their regular operations.

What drives these decisions are the algorithms that run in the background, and there would be thousands of these algorithms interconnected to each other that would help the machines to take decisions. Self learning algorithms rely on the data that they are exposed to, and suddenly we see that the data is the key driver that will start influencing the behavior of these self learning machines.

Data security and integrity will become enormously important with data science rising to prominence and while most of the activities will be done by the machines in the future, managing data ethics and the algorithms that influence machine behaviors will definitely keep the future humans busy.