Most ML projects in industry are leveraging understood techniques in novel ways to build real customer facing products within reasonable investment timelines. To say that one can't provide any scope or estimates on these seems weak.
On the other hand, there are practices that can save you a ton of time even if you are working alone. In an academic setting this can often mean you just learn the ineffectual techniques of whomever came before you. In this case a small amount of education and change of practice (i.e. process) absolutely can dramatically improve overall lab productiveness. Industry isn't immune at all - especially when treating your "R&D" group quite separately from your "production engineering".
Another thing to remember is that there is no such thing as no process; you have one even if you don't understand it. It can be lightweight and almost invariably you can improve it (thought that's not always worth the time spent)
It's a fallacy to believe that there is a bright line distinction though, and that no engineering practices or tools will improve your research productivity. Traditionally e.g. academic labs traditionally eschew all such approaches not because they wouldn't benefit, but because they don't understand what they are missing and have little incentive to learn, and few opportunities for it.