To achieve successful AI automation, it is essential to collect inputs, outputs, and, critically, the reasoning that connects them. This chain of thought encompasses the questions asked, important factors considered, and the thought processes utilized in decision-making. However, many organizations fail to document these reasoning details as they prioritize speed over thoroughness. This lack of explicit knowledge hinders AI's capacity for reasoning due to insufficient data on human mental processes. To address this, organizations should aim to gather at least 10 gold standard examples that include the reasoning behind decisions. This process often involves multiple sessions to fully uncover the various considerations that influence outcomes. Failure to document this reasoning significantly jeopardizes the potential for effective automation.

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