"Siloed communication stood in the way of progress. It spoke to an underlying data problem, though. Each data scientist had a treasure trove of information. We helped make that data accessible to everyone else, which made much more possible."
"To be successful you need a strong business case and must approach the development of an AI application as if it were a traditional enterprise app instead. But that's where companies need to diverge from the norm."
"Whether it is in the cloud or a data center, the location of the data isn't as important as the data itself to determine next steps."
Companies often create silos that hinder collaboration, especially in international organizations with data-sharing regulations. A CIO reported having 800 data scientists across various units, yet they struggled to work together. Establishing an AI Center of Excellence facilitated knowledge sharing, allowing data scientists to solve previously unsolvable problems. A data-centric model is crucial for AI success, as it emphasizes the importance of data over infrastructure. Companies must focus on their data estate to extract value and develop effective AI applications.
Read at Fast Company
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