Stanford parser is very good for preprocessing data. Things like part of speech tagging, named entity recognition, and dependency parsing. If you want to do something fun and interesting with your data, you will probably need to implement it yourself. Note, there are lots of other go-to tools nowadays besides the Stanford parser. Things like GloVe embeddings, open source translation systems (harvardseq2seq, open source sentence encoders (facebook fasttext) are probably necessary in many NLP pipelines.
When things "just work" with off the shelf tools then you probably don't need the researcher (although sometimes you will need them to just find the right solution/tool). When things don't work, you will need them. I guess this can be said about many fields though? (Databases, front end development, etc)
The big gains now are taking these pre-processing tools in speech, vision, NLP etc and using that as input to a NN for some problem domain. Every one of these is a startup.
When things "just work" with off the shelf tools then you probably don't need the researcher (although sometimes you will need them to just find the right solution/tool). When things don't work, you will need them. I guess this can be said about many fields though? (Databases, front end development, etc)