This article explores the common obstacles organizations face when implementing AI, such as data scarcity, solution overload, and difficulty in evaluating outcomes. It offers guidance on identifying suitable AI projects, finding solutions when the context doesn't perfectly align with AI's strengths, and selecting projects with the greatest potential benefits for businesses or society.
While AI has the potential to solve major problems, organizations embarking on such journeys of often encounter obstacles. They include a dearth of high-quality data; too many possible solutions; the lack of a clear, measurable objective; and difficulty in identifying whether a proposed solutions is “good.
” In tackling these issues, Google DeepMind, Alphabet’s AI lab that is striving to solve extremely challenging real-world problems, has come up with solutions that can benefit others. And it also offers guidance in choosing which opportunities to pursue after potential projects have overcome those hurdles.Artificial intelligence has witnessed a remarkable surge, captivating researchers, product teams, and end users alike with its transformative potential. But despite its recent popularity, AI is only successful in solving problems with very specific features. In this article, we offer guidance to help organizations identify the right problems to address with AI, to find solutions when the context does not perfectly align with what AI is best at solving, and, after potential projects have overcome those hurdles, to choose those that can offer the largest benefits for business or for broader society.is the Glenn and Mary Jane Creamer Associate Professor of Business Administration at Harvard Business School and co-principal investigator at the Platform Lab at Harvard’s Digital Design Institute . She is also a fellow at the National Bureau of Economic Research and the Center for Economic Policy Research.is the vice president of science and strategic efforts at Google DeepMind, where he leads AI for Science research efforts such as AlphaFold, a breakthrough AI system for predicting the 3D structures of proteins, and SynthID, a state-of-the-art method for watermarking and detecting AI-generated content.is the Allen & Kelli Questrom Professor at Boston University, a digital fellow at the MIT Initiative on the Digital Economy, and a visiting scholar at the Harvard Berkman-Klein Center.
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