An intelligent classifier is used to predict categorical attributes automatically. It is a combination of data transformation functions and a classification algorithm. A data transformation function can either be a very basic or more complex function. For example, mapping every upper case letter to its lower case equivalent is a very basic function, but gives a very important opportunity to create a unified text corpus. On the other hand, sophisticated transformation algorithms as TF-IDF or doc2vec are available as well. Although these methods are algorithms, we still call them functions in this context.
On top of that, every classification algorithm has its own unique parameters with different effects.
As you can see, there are many functions and algorithms to be considered when employing artificial intelligence. It is understandable that nobody wants to deal with such a large number of parameters and therefore prefers an easy, pre-trained classifier. The disadvantage of this, however, is that when a classification scenario is generalized too much, the classifier accuracy gets worse. Every business data is unique and consequently every use case for artificial intelligence is unique.
It would be burned money if a classifier was not carefully prepared. This can happen quickly if a product is used that does not offer full flexibility for configuration.