In the realm of machine learning, business leaders often face challenges when attempting to implement these technologies into their operations. The text outlines three critical non-technical points to address these challenges. First, separating the problem from the solution is essential, as businesses need to clearly define their objectives before selecting the appropriate machine learning approach, which may require collaboration between data scientists and business leaders. Second, employing the correct metrics is vital for accurately assessing the project’s success, with an emphasis on developing intermediate metrics that bridge technical performance and business outcomes. Lastly, understanding that machine learning projects are highly iterative is crucial, as they often require adjustments based on real-world data and conditions, necessitating a process that incorporates feedback from production systems while recognizing the broader organizational changes involved.