Fully agree! In the case that tabular data means business process records, there's absolutely no need to use anything more complicated than a principled statistical model. Focus on the quality of the data and the business problem, not exotic ML.
In more detail: the business data is prone to: evolving processes / systems / products / markets / customers; errors / omissions / corrections; tail events; hirings / firings; data loss; etc etc. The datasets tend to be small, messy, complicated, subjective. Nothing about this suggests needing a large, complicated model.
In more detail: the business data is prone to: evolving processes / systems / products / markets / customers; errors / omissions / corrections; tail events; hirings / firings; data loss; etc etc. The datasets tend to be small, messy, complicated, subjective. Nothing about this suggests needing a large, complicated model.