The idea that "When a measure becomes a target, it ceases to be a good measure" is something that every data scientist/statistician knows, but almost none heed in practice.
The worse lead companies that I've worked for are the ones that claim to be "data driven". Countless dashboards showing various progress towards various targets without even a hint of understanding what the big picture might even be.
One of the biggest insights I've had over a career working in data science is that the person solving a problem based on years of experience without any numbers backing their decisions almost always is making choices close enough to optimal that it isn't worth the extra energy to push it optimal.
An example is that the person selling hot dogs at the park is probably pricing them nearly optimal. You could bring in a team of dynamic pricing experts, build a data center to mine costumer data, and I'm willing to bet a few hot dogs that difference between the model optimal price and what the hot dog vendor is selling is not enough to justify the cost of figuring out the difference.
I likewise would not be surprised if the real, long run benefit of A/B testing does not justify the cost of both employee time and especially the SaaS products that help manage these processes... but let's not do that analysis because my salary depends on no one checking this.