Machine learning has moved beyond the hype to become a meaningful driver of value for many organizations. Over half of businesses that have deployed machine learning-powered artificial intelligence (AI) initiatives say the technology has increased productivity. While it’s clear that machine learning is an essential part of business transformation, many organizations struggle to understand where to apply machine learning for the most impact. Selecting the right machine learning use case requires you to consider a number of factors. First, you need to find a balance between optimal business value and speed. A proof of concept built by a siloed data scientist is not likely to generate much enthusiasm for machine learning in an organization. What is more apt to attract the needed commitment and funding is showing how machine learning can address the practical issues your organization currently faces. Furthermore, you’ll want to find something that can be accomplished in 6–10 months so that you don’t lose momentum. This is especially true if this is your first foray into machine learning.
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