Is X' of any use for NLP at its current state of the art?
The simplest answer is that high performance NLP applications do not use X-bar theory as an explicit representation. A major factor in this is that parsing is most commonly evaluated against the Penn Treebank or various dependency annotated corpora, neither of which would be considered X-bar structures. For an idea of what kind of structures and information are used in statistical parsing, check out one of the seminal articles such as Head-Driven Statistical Models for Natural Language Parsing (Collins, 2003).
There is certainly some work in NLP that does not explicitly use an X-bar representation but shows that constraints that could be considered to have come from X-bar (or equally, later from Minimalism) are useful in certain applications. For an example in parsing, see the Empty Categories section of Fully Parsing the Penn Treebank (Gabbard et al., 2006); for POS tagging see A Simple Unsupervised Learner for POS Disambiguation Rules Given Only a Minimal Lexicon (Zhao and Marcus, 2009).
Outside of performance-driven NLP applications, there is some work. As Alan H. points out, Sandiway Fong's Principles and Parameters Parser is a great example. It follows from Principle-Based Parsing (Berwick, 1987). For more background in the evolution of these systems, see Robert Berwick or Mitch Marcus's dissertations.
I believe the problem with parsing X-bar structures is mainly performance based, it is difficult to get a parser to be fast enough to be useful. The transformations make the parsing non monotonic, which makes everything a royal pain for a computer.
There are however many systems who use other theories of syntax, such as HSPG, LFG, DCG, CG, TAG etc.
I believe that Sandiway Fong has made at least one principles & parameters parser, which might be old enough to have proper x-bar structure. There aren't many in that area doing computational linguistics, but probably the big name is Robert Berwick, so you might want to look at some of his work as well.
In response to another reply: Some popular versions of transformational grammars would be factorial time (based on number of words in a sentence), at least in the worst case, if they were implemented. Many who work in that line don't consider that important, because they don't consider what they do to be a model of performance but purely a model of knowledge. But not everyone buys that. See this article, for example. But I don't think the main culprit is x-bar theory. The main problem is global economy conditions, which in some models require the syntax to generate many permutations of a given sentence and then evaluate them after the fact.