Right but the "how" is the tricky part. For example, wrt to ML, what is the milestone and how do you know you will hit it by a particular date? Very difficult to say with ML.
This is where smart analytics teams are super useful. It is a really hard projection estimation! Even understanding what the F1 score should be to be "good enough" is nearly impossible, much less understanding what it takes to go from "where we are" to "good enough".
What you can do is estimate something like "we think we need $measurement_value on $metric by $date, we are at $current_value. If we're not at 75% of the gap in 50% of the project time (when we have low hanging fruit), we cancel."
