Here are the 3 building blocks of AI data strategy Sponsored by IBM
s artificial intelligence begins to mature—not just as a technology but also as a scalable business practice—understanding why some companies are more successful than others at it often comes to knowing how to execute on ambitious plans.
While data collection, organization and infrastructure remain critical handicaps at many companies, successful datais a more holistic concept, says Seth Dobrin, chief data officer of IBM Cloud and Cognitive Software. “Leaders of AI initiatives need to map out the key decisions being made and align them to tangible value—specifically cost avoidance, cost savings or net new revenue,” says Dobrin.
Combining these elements helps lay the essential groundwork for data strategy. “You frame it up as decisions tied to real tangible value,” Dobrin says. “And you use both of those things—the decision portfolio and the technical evaluation—to prioritize your decisions.”One of the most difficult but essential ingredients of AI success is cultural change. The reasons for that are—no surprise—cultural.
Simply put, diversity drives business value with AI. Case in point: Companies in the top quartile for racial and ethnic diversity are 35% more likely to outperform national industry medians with financial returns, according to a McKinsey. Moreover, companies in the same upper tier for gender diversity are 15% more likely to outperform the same median.
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