For the data-centered, insights are everything - improving efficiency, profitability and increasing revenue - ultimately giving your organization a competitive edge. Data is the key to unlocking insights driving automation. The challenge for many organizations is finding the resources that best enable their Analytics and AI strategy in the midst of all the noise that exists today.

Let's begin with what AI means and what it means to businesses. AI is the simulation of human intelligence by computer systems. And increasingly, in a wide range of industries, organizations are using AI to increase business value.

AI is implemented in two phases - Training or model development, which is the process whereby data scientists develop and optimize models with a curated dataset - and inferencing, which is applying a trained model to new data to derive insights and enable automation.

Here is where the other two buzzwords of Machine Learning and Deep Learning come into the picture. While they seem to be used alongside AI, and sometimes even synonymously, each of these terms has a specific meaning, a connotation, and a business case.