Today I had the opportunity to participate in the discussion on the topic “What are the boundaries of AI adoption by Enterprise Automation platforms?” during Enterprise Automation Forum 2019. The general summary that only we are the main obstacle has led me to do some exploration. After a short search I found this article:
Main concept is that AI adoption is disturbed by: cultural conditions, fear, lack of experts and lack of strategic approach. It seems to me that these barriers can be transferred directly to AI adoption by Enterprise Automation platforms. In my opinion “lack of experts” will be a key barrier. The other three are related to a lack of adequate knowledge.
How we can mitigate these restrictions? Fist of all, let’s see what AI experts are doing:
What is an average work day for a data scientist (a bit old, but still valid)
They spend about 80% of their working time preparing data for AI.
It seems that for rapid adoption of AI in enterprise automation platforms it is appropriate to prepare the data that will be used by AI. Then the significant barrier related to the lack of experts will be much smaller, and a better understanding of data used by AI will reduce the remaining barriers.
Data Governance (DG) tools called “data catalog” can help with that. Maybe there is some place for some kind of data catalogs for Enterprise Automation Platforms?