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Thought this was interesting. Across 160 teams of researchers, just about all failed to make good life outcome predictions on things like GPA, evictions, layoffs, and others. Data followed 4.5k families across 15 years, with 13k features (varied over time). Haven't looked at it directly yet, but will be turning the docs and data inside out... In the meantime, authors claim this as showing the limits of ML. Oh, and it's published in PNAS, so you know there's some big publication energy there.
https://www.pnas.org/content/117/15/8398
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Blunt and quickly
Asap. The best time to do that is gone. The second best time is now.
The sooner expectations are lowered the easier for everyone.
But one thing here... just because there are issues, doesn't mean you can't deliver at reduced scope or increased complexity. You almost never have data that's ready for optimal use at the get go. Think about adding layers. Not everything has to be done by a single transformer and with the data that happens to be on hand. Typos are more or less fixable with autocorrect, language detection is possible for subsetting, etc.