Information Control Nets have been well used as a model for knowledge mining, discovery, and delivery to increase organizational intelligence. In this document, we extend the notions of classic Information Control Nets  to define new concepts of Stochastic Information Control Nets. We introduce a simple and useful AND-probability semantic and show how this probabilistic mathematical model can be used to generate probabilistic languages. The notion of a probabilistic language is introduced as a normalizer for comparisons of organizational knowledge repositories to organizational models. We discuss model-log conformance and present a definition of fidelity of a model. We show how to manipulate the residual error factor of this model. We describe a set of recursive functions and algorithms for generation of probabilistic languages from stochastic ICNs. We prove an important aspect of our generation algorithms: they generate probabilistic languages that are normalized. Since ICN models with loops generate infinitely many execution sequences, we present new notions of most probable sequence generation, and -equivalent approximation languages. These definitions can be applied to many aspects of organizational modeling including the process, the informational, and the resource perspectives. The model that we introduce here can be used to augment and expand on analyses that have been useful and insightful within varied enterprise information systems modeling and organizational analysis applications. © 2012 Elsevier Inc. All rights reserved.