Artificial Intelligence (AI) has started playing important role in Healthcare. For Example, an AI system is used to improve early breast cancer detection. Another AI system improves the performance of the Microscope to find cancer cells more efficiently. And, the AI is getting powerful steadily. An AI Algorithm can see and learn to analyze millions of publicly available images on Google Street View to determine the political leanings of a given neighborhood just by looking at the cars on the streets.

The business intelligence (BI) assembly line is broken, with adoption or utilization rates of only 30 percent in a typical organization, according to Gartner[1]. These adoption rates include all the users of the BI system – administrators who manage the system, analysts who build reports, and business users who consume reports for better decision making. This underutilization ignores employees outside the BI system who could be using valuable data to make better decisions. No one can argue that business performance and innovation are negatively impacted when data assets are not fully leveraged for decision making. How does this BI assembly line contribute to poor adoption?