In a way actually! The things that are hard to A/B test get neglected. If you need A/B testing (ie: data) to make any decision, then places where data is purely qualitative - there will be no data to be had and therefore no decision can be made.

The Expedia example really comes to mind too of micro-optimizations and everyone working towards their own org's goal can all make sense individually, but really fail when taken as a whole: https://www.qualtrics.com/blog/upstream-thinking-saved-exped...

Expedia had an example of this: - the sales team wanted sales - the phone support team wanted to turn over calls quickly -

If there were a way to measure confusion, it would be improved and

Can I A/B test whether there will be a 10% increase in sales if I lower prices on Ec2 instances by 5% - YES! Can I A/B test that the presence of 30 service options is the tipping point of spending 1 hour to get something done vs wanting to hire an "AWS"