Machine learning is making substantial impacts on businesses around the world, but many organizations struggle to understand where and when to optimally use ML. To enable successful deployments, businesses must first recognize which problems are most amenable to ML and, second, ensure the right processes are in place to evaluate its impact. In general, ML algorithms build relationships between inputs and outputs by leveraging statistical properties of the data. As researchers expose the algorithm to more data, it learns and adapts. Eventually, the relationship becomes accurate enough that the algorithm generalizes to predict outputs from new inputs.