Without understanding what the business and technical goals of the project are (and how they relate to each other!) then making sure an ML project is actually valuable, and that value can be measured effectively, is impossible. That is why it is so important to engage the relevant business and technical stakeholders as early as possible and ensure their goals for the project align and that these can be measured effectively. Once these goals are clear it is important to make sure everyone is on the same page and keep these business goals front and center during the entire project. Your ultimate goal is not to create a high-performing model but to reduce the time/pain/cost for real people doing real things! What your technical KPIs are (is 80% accuracy a good or bad outcome?) are totally dependent on the business goals of the project and this should be reinforced often. All too often the theoretical value of an ML project is mortally undermined by data constraints.

Machine learning runs the world. It generates predictions for each individual customer, employee, voter, and suspect, and these predictions drive millions of business decisions more effectively, determining whom to call, mail, approve, test, diagnose, warn, investigate, incarcerate, set up on a date, or medicate. But, to make this work, you've got to bridge what is a prevalent gap between business leadership and technical know-how. Launching machine learning is as much a management endeavor as a technical one. Its success relies on a very particular business leadership practice.