Whats an explanation of PCA that is intuitive for someone in senior leadership who doesn't have a technical background?
Try to make your explanation simple and brief. Maybe something like "PCA distils a dataset's features into as few attributes as possible; these attributes explain as much of the observed trend as possible while being as different from each other as possible". If you can do this using visual aids, even better.

As far as analogies go, maybe use an example of a motor accident. There are many predictors of an accident, but PCA may combine the majority of them to form 2 features - perhaps vehicle attributes and driver attributes. These are different but arguably are both highly predictive of a motor accident. [I realise this is not technically how PCA works, but might help a layman understand].