Business use cases for Wilcoxon signed-rank test The Wilcoxon signed-rank test is generally used for non-parametric data (i.e. not normally distributed). When the sample gets large, the data will be approximately normally distributed. Therefore there is no need to use the Wilcoxon signed-rank test, and a parametric test would be preferred.
Considering this, the Wilcoxon signed-rank test would be most appropriate where we cannot get a large sample, and the sample is not normally distributed. (Please correct me if I am wrong).
Could you suggest to me a couple of business use cases for the use of the Wilcoxon signed-rank test?
 A: "When the sample gets large, the data will be approximately normally distributed."
This is absolutely not true, so the rest of the question is based on a false premise. There is no reason to expect a large sample up be any closer to Normal than a small one. Perhaps you've misunderstood the central limit theorem?
A: The gist of (good) nonparametric tests is that they are almost (but not quite) as good as parametric tests when the parametric assumptions are met, but they can blow away parametric approaches when the parametric assumptions are false.
Yes, t-tests have good robustness to deviations from normality, but this has to do with the type I error rate. The power can be quite poor, especially if you badly deviate from normality; for instance, log-normal distributions can have rather slow convergence, despite meeting the assumptions of the central limit theorem.
I’ll close with a link of possible interest.
A: One example application of the Wilcoxon signed-rank test is for comparing the performance of two classifiers across multiple datasets. See e.g. Demšar, "Statistical Comparisons of Classifiers over Multiple Data Sets" JMLR 2006.
It is argued this is better than the (often-used) t-test because the t-test assumes a normal distribution and commensurate differences in performance across multiple datasets.
