AI algorithms, specifically in industries, can be used to work and manipulate the data to estimate or learn this data to achive a solution or prepare trained data to feed that into an optimization model.
On the other hand, the simulation techniques, especially discrete-event and Mont-Carlo, use almost the same data, e.g. number of failures in a specific station in the production line, to either estimate a function to robust some parameters as a final solution or feed these parameters into an optimization model. And already investigate the behavior of the complex system in a dynamic scheme with an appropriate feedback.
Now my question is, what exactly is the main difference between an AI method and a simulation technique for example to estimate the failures or production utilization?