The need for an AI strategist

Bridging the gap

In the latest CIO/IDG research that surveyed more than 200 IT executives of large companies, majority of the companies agreed AI had the massive potential to drive disruptive innovations across most enterprises.In fact 90% of them are investing in AI.

In reality only a very few of them succeed.As per the report the top reasons for failure are,

• Data-related challenges hinder 96% of Organizations from achieving AI.
• Technology skills, leadership, and lack of a cohesive strategy are the biggest hurdles faced by data engineering and data science.
• Increasing Complexity:Organizations Invest in an Average of Seven Different ML Tools.

Source:"Conquer the AI dilemma by unifying data science and engineering" by DataBricks

This of course is a very IT technology implementation oriented point of view.The suggested solution is to create a unified analytics platform which fosters better collaboration between data scientists and engineering.

But before rushing ahead and trying to implement a unified analytics platform consider the findings of the strategy consulting firm McKinsey&Co. in it’s article regarding “How advanced industrial companies should approach AI strategy”