What you say is all true and I actually completely agree with you (and like how you articulate those points – great to read it distilled that way) but at the same time probably not a good idea at all to do in most circumstances.
It is alright to decide that in certain cases you can act with imperfect information.
But to be clear, I actually think there may be situations where pouring a lot of effort into really understanding confusion is confusion. It‘s just very context dependent. (And I think you consistently underrate that progress you can make in understanding confusion or any other thing impacting conversion and use by using qualitative methods.)
Regarding underestimating qualitative methods: I'm actually all for them. It may turn out, it's all you need. (Maybe, a quantitative test will be required to prove your point, but it will probably not contribute much to a solution.) It's really that I think that A/B testing is somewhat overrated. (Especially, since you will probably not really know what you're actually measuring without appropriate preparation, which will provide the heavy lifting already. A/B testing should really be just about whether you can generalize on a solution and the assumptions behind this or not. Using this as a tool for optimization, on the other hand, may be rather dangerous, as it doesn't suggest any relations between your various variables, or the various layers of fixes you apply.)
It is alright to decide that in certain cases you can act with imperfect information.
But to be clear, I actually think there may be situations where pouring a lot of effort into really understanding confusion is confusion. It‘s just very context dependent. (And I think you consistently underrate that progress you can make in understanding confusion or any other thing impacting conversion and use by using qualitative methods.)