Given the massive amounts of hype and promise surrounding AI and related technologies like machine learning, it’s become increasingly difficult to make critical innovation and investment decisions in the space. In the rush to develop new AI projects, many are taking a technology-first approach (e.g., selecting a vendor simply because they claim to use the latest AI techniques), which has led and will lead to many failed projects. Here we present a framework to reduce failures by starting with the end in mind—focusing on the outcomes, capabilities of today’s AI tools, and emerging solutions to the key challenges surrounding implementation.
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