Machine learning can be highly beneficial for businesses, but it requires a solid foundation of data infrastructure and mature data operations before pursuing projects that are open-ended, complicated, and risky. Before diving into machine learning, organizations should have a regular cadence of reports, governed and secured data, and widespread data-driven decision making. Once this foundation is in place, machine learning opportunities can be identified by considering the type of problem to be solved, such as pattern recognition, prediction, or automation, and applying machine learning flavors like unsupervised, supervised, or reinforcement learning. Practical examples of machine learning use cases include financial forecasting, personalization and recommendations, customer and product segmentation, marketing and sales forecasting, supply chain optimization, anomaly detection, and business process automation. These applications can be achieved through various machine learning flavors, including heuristics-based methods, and can have a significant impact on a business's productivity and competitiveness.