That analogy is way off. There are plenty of legitimate problems for which ML is a good solution. It's just the vast majority of companies employing ML techniques would find simple analytical techniques (basic statistics: regressions, simple clustering, etc.) to be at least as useful. They'd also be less costly in time and money as compared with building out even the basic infrastructure to implement an ML pipeline.