Portfolio choice is the final piece of an asset allocation activity, it is here where the risk and return estimates are used by a model to generate asset weights.
In 1991 when the Black-Litterman model was developed there was not a large number of portfolio choice options. Now in 2014 there are many methods, some of which are naturaly compatible with the outputs of the Black-Litterman model, for example robust optimization and resampling.
The output of the Black-Litterman model is a set of estimates of the mean returns to the assets, expressed as a distribution with an estimated mean and a precision of the estimate. Many portfolio choice models take as inputs point estimates of the mean and the estimated mean from the Black-Litterman model can be used as that point estimate. In this case we lose the information embodied in the precison. Robust optimization and resampling can both use an estimate of the mean which is a distribution. Robust optimization with an elliptical uncertainty set for the mean and no uncertainty for the covariance is the case which would most naturally be driven by the Black-Litterman outputs. In resampling techniques, statistcal means are often used to estimate the uncertainty region, but taking a Bayesian approach the precision of the estimated mean could be used instead
Because the Black-Litterman model estimates the means and the uncertainty region, the posterior uncertainty region can be significantly different from the prior uncertainty region because of the mixing of the prior and the investor's views.