This helps with determining when have you observed enough data to make a decision.

A/B tests, monitoring metrics, health, quality control all use this.

If you use LLMs, you might use this to determine if a model update or prompt change impacts results using fewer tokens.

The SPRT is probably already making your life better: it's used to decrease the cost of medical trails, optimize classifications in high-stakes examinations (i.e. for medical certifications), detect defective manufacturing processes, etc. It sounds like this paper extends the method to groups of hypotheses, whereas the basic version is limited to a null hypothesis and an alternative hypothesis.

Implement a statistical software suite that ubiquitously uses this framework instead of the usual hierarchical mixed modeling tools whose assumptions often don't match what experiments were actually done.

You can search for the "peeking" problem in A/B testing.

SPRT also very likely helped win a major war that involved many nations.