The art of choosing the right machine learning project

Machine Learning projects are known to fail frequently, according to Gartner 85% of all AI projects fail and even 96% fight with problems. Sure, when it comes to new technologies a high degree is normal, but these numbers are alarming. Typically, you read a lot about data quality, exaggerated expectations and wrong or non-existent goals. However, some of these issues can be avoided by assessing the projects in more detail before selecting a project for a Data Science/Machine Learning team. I would like to highlight some aspects from the perspective of an AI developer.