It’s about closing the gap. Digital business, big data and the Internet of Things has fueled an exponential growth in data. Until recently humans equipped with traditional data and analytics capabilities have done pretty well. But as the transformation to digital accelerates at a pace, human know-how and traditional data and analytics capabilities are no longer enough. In order to keep up and close the capability gap, we need something more. AI holds the promise of being the technology that gets us there. That’s one reason why AI is such a hot topic at the moment — everybody’s desperate to find a way to close the gap.
In addition, AI has now reached a level of maturity where it's no longer theoretical, it’s no longer something only for academia or for rocket scientists. AI has now come out of the lab and reached a state where you can get real business value from it.
Finally, vendors in the data and analytics market have begun to inject various kinds of AI into their technologies to address data and analytic complexity and expand insights to more people across the enterprise. As a result, AI is now more generally available to organizations — as part of their enterprise applications and the analytic content they consume every day. For example, vendors in the analytics markets and related spaces have begun to include machine learning in their tools to automate tasks and time to unbiased insights. We’re seeing elements of machine learning, which is one style of AI, transforming how content is created using modern analytics and BI tools, data science and machine learning platforms, data preparation tools and data integration technologies.