While researchers have been working on the various disciplines within the board umbrella of AI for over 7 decades, there existed earlier a major roadblock to migrating these techniques from the Research Laboratory to the Business Enterprise. It was the enormous computational expense involved.

Today this is no longer an issue. The reality today is that enterprise servers today have the computing power and capability of yesterday's supercomputers. Servers, processors, processing memory, network and storage capabilities and speeds today are hundreds of times greater than they used to be. In recent times there has been much focus on GPUs from NVIDIA. 

As a consequence highly complex computing algorithms/data processing involving enormous volumes of data can be run in a few minutes, even seconds, instead of days and sometimes weeks. Business automation has come a very long way since the early days of calculators and vending machines.

In the world of the traditional Business Enterprise there have existed for a while numerous multi-tiered complex software systems such as ERP and CRM that run on distributed computer infrastructures and exhibit high-performance in terms of speed and user-loads. Such systems have already enhanced their offerings by first including advanced logical reasoning and analytics. More recently today they are all focused on adding machine intelligence features to their functionalities.

Today's Digital’ Business Enterprise systems tend to be composed of Cloud, Web and Mobile Apps, Big Data and Social Media technologies. With the plethora of data (big data) available on these digital platforms, all organizations are all looking toward Machine Learning to make their platforms and offerings smarter.

We are now embarking on the new generation of ‘Enterprise Machine Intelligence Systems’.

The AI industry today is already worth half a trillion dollars and is expected to exceed $2.0 trillion in 2030.