Sure, everyone wants to get to the latent factors that really drive the outcome of interest, but I've never seen a situation in which principal components _really_ represent latent factors unless you squint hard at them and want to believe. As for gaining insight and explaining user behavior, I'd much rather just fit a decent model and share some SHAP plots for understanding how your features relate to the target and to each other.
If you like PCA and find it works in your particular domains, all the more power to you. I just don't find it practically useful for fitting better models and am generally suspicious of the insights drawn from that and other unsupervised techniques, especially given how much of the meaning of the results gets imparted by the observer who often has a particular story they'd like to tell.
I've used PCA with good results in the past. My problem essentially simplified down to trying to find nearest neighbours in high dimensional spaces. Distance metrics in high dimensional spaces don't behave nicely. Using PCA to cut reduce the number of dimensions to something more manageable made the problem much more tractable.
If you like PCA and find it works in your particular domains, all the more power to you. I just don't find it practically useful for fitting better models and am generally suspicious of the insights drawn from that and other unsupervised techniques, especially given how much of the meaning of the results gets imparted by the observer who often has a particular story they'd like to tell.