We all know that one use case is not another, that one MT engine may work well for some language pairs and some domains but may perform poorly in other pairs or domains. Large language models (LLM) occasionally outperform MT, and there are also cases where MT is needed.
In a nutshell: it is a complex landscape, and testing is required in every situation and for every use case.
Technology, when used with discernment, helps translate more and faster. But don't take anything for granted, do the research first, test, compare, evaluate. That has a cost. Managing client expectations includes explaining what this testing entails. No one-size-fits-all solution is credible.