Breakthroughs in the application of complex calculations to large volumes of data have enabled machine-learning methodologies to revolutionize business processes in nearly every industry. Some of the more recognized examples of machine-learning applications include personalized Netflix recommendations and related product modules from online retailers such as Amazon and Nordstrom. However, there are less sexy yet equally impactful machine-learning examples, which include revenue management solutions used in hotels that incorporate these methodologies into an algorithmic engine to help produce pricing and inventory recommendations. Unlocking the potential of machine learning for the office of finance remains a hot topic for financial planning and analysis (FP&A) leaders, industry analysts, and technology vendors alike. Even more specifically, continuous chatter surrounds the ways that machine learning can improve future FP&A processes and how finance leaders can prepare for deploying advanced analytics within their organizations.