This article explores the common hurdles organizations face when implementing AI, such as data scarcity, solution ambiguity, and unclear objectives. It offers guidance on identifying suitable AI projects, adapting solutions to imperfect contexts, and selecting opportunities with the greatest potential benefits for businesses and society. The insights are drawn from experts at Google DeepMind and Harvard Business School, drawing on their experience in tackling complex real-world problems with AI.
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.
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.
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