The text discusses the challenges and pitfalls of implementing machine learning and artificial intelligence in organizations, highlighting the need for a strong foundation in data infrastructure, processes, and practices to support these technologies. To avoid common mistakes, such as overkill solutions or insufficient supporting infrastructure, organizations must identify specific problems that machine learning can solve and have mature enough data operations to support its use. The text also introduces two models for charting a progression to machine learning: the data maturity curve and the data hierarchy of needs, which describe an organization's orientation towards data and tools, processes, and capabilities with respect to data, respectively. By leveraging technological solutions to build a robust data infrastructure and developing a proactive and innovative orientation towards data, organizations can create a solid foundation for pursuing machine learning and realizing its potential.