That's useless in this case. You need to be able to prove that it will work with all inputs, and there are too many combinations of inputs to exhaustively enumerate.
There is no way to determine that a non-trivial neural network won't drastically diverge in output due to small changes in input (eg one pixel attacks on image classifiers). This is true for all current models I know of.
Almost all neural network implementations have continuous outputs (ie the nodes in the output layer produce a value between 0 and 1). That doesn't change the above issue at all.
This is much less of an issue with traditional methods
If your AI has linear / continuous output, testing it should be no different than any other software.