This question might be somewhat general and not completely relevant to this forum but I think here is the most relevant place to ask the question.

Currently, deep learning, RL and generally black-box approaches are gaining much attention and many practitioners and academia are using these problems to solve their optimization problems. For example, in real-world prediction problems, LSTM artificial neural networks (ANNs) are common which are black-box algorithms with good accuracy but in many cases, there is no proof of convergence. However, in the field of OR/MS, the most common approaches are Time Series approaches like ARIMA. etc. Or, for example, decision trees are more popular in OR/MS because they are interpretable but with lower accuracy compared to deep learning. In this situation, AI algorithms enable researchers to use different sources of data like historical data, crawling webpages, reading news, etc. and use all of them to predict while conventional approaches in OR/MS do not let us to this.

To my view, the computer science community is using approaches that are more acceptable in the industry and more applicable while OR/MS is sacrificing applicability in real-world cases to solve problems with convergence proof. For example, many papers use linear regression with unknown coefficients because they can prove that their algorithm can converge to the true values of coefficients (if the true model is also linear).

These are my views based on papers published in Management Science, Operations Research, and M&SOM. Some people may consider this question a subjective one and wants to close this question but it is really confusing question that I cannot find the justification for some times.