15 Kellogg Insight enough to detect complicated statistical relationships among variables, but because they are “black boxes,” the nature of those relationships isn’t easy to conceptualize. This can increase the tendency to blindly trust whatever results the algorithm spits out, rather than asking com - mon-sense questions about whether wonky results are a function of how the model was built or trained. “If you buy into this idea that AI ultimately scales analytics, please don’t think that it means you’re off the hook when it comes to under - standing how to create knowledge and what you can learn from data,” says Zettelmeyer. Know It—Do Not Just Think It As Zettelmeyer sees it, decision-making in the business world is being revolutionized in the same way that healthcare is with the widespread adoption of “evidence-based medicine.” As advanced analytics and AI bring about this revolution, managers with a working knowledge of data science will have an edge. Beyond being the gatekeepers of their own analytics, leaders should ensure that this knowledge is shared across their organization—a disciplined, data-literate company is one that is likely to learn fast and add more value across the board. “If we want data analytics and AI to succeed, everyone needs to feel that they have a right to question established wisdom,” Zettelmeyer says. “There has to be a culture where you can’t get away with ‘thinking’ as opposed to ‘knowing.’” Developing such a culture is a big challenge for leaders. Organizations are rarely willing to admit the need for change, and few managers feel Based on insights from Florian Zettelmeyer