The adoption of data within large enterprises has as much to do with employees willing to accept change as it does with the technology stack. While technology has a role in creating trust, Kimberly Nevala, director of business strategies at SAS, believes that focusing on people and getting employees to embrace ambiguity and acknowledge risk is truly how organizations become data driven.
Highlights from Nevala’s presentation include:
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With artificial intelligence and machine learning systems, two plus two doesn’t always equal four. Sometimes the answer will be five because machines will make the occasional mistake. These are probabilistic systems with a level of uncertainty inherent in their designs. (07:39)
There are three questions organizations should ask themselves to rationalize the risk of any AI or machine learning project. The first is: what are the specific errors or mistakes and the concrete outcomes that could result if the organization gets it wrong? Secondly: what is the bias that will impact how people will want to utilize that solution? And lastly: what is the risk tolerance of the organization? (09:50)
If organizations are going to change in minor or moderate ways how they engage with employees, customers, and partners in the realm of leveraging AI or machine learning, then all parties need to be willing to change, accept mistakes, and allow organizations to fix them when they occur. Because these systems — how they are implemented, integrated, and engaged with — are not one-size-fits-all solutions. (17:11)
You can see Nevala’s complete presentation in the video above.
