This applies also to ML - it applies to all tech projects, though yeah, it's harder in ML. But not figuring out the intermediate products is not an option though - your stuff will get killed prematurely if you don't.

The trick with ML is not to promise "98% precision and 92% recall by Q4", it's to figure out what kind of product is shippable with lower precision and recall. Or perhaps a stepping-stone model that allows some simpler use case, but gives you progress towards the greater goal.

It's always case-specific, but as a ML team you do need to figure out what your intermediate checkpoints are. You need to demonstrate not only progress, but that your progress is contributing to the company's goals.