Knowledge Management has become the most strategic resource in the new business environment. This research is based on the analysis of the strategic knowledge held within a multinational group; a leader in the design and production of a great variety of components for the automotive industry. It focuses on achieving feasibility and real applications by identifying knowledge gaps that must be overcome to perform certain activities, so as to take the right decision on its acquisition in terms of what to acquire, how to acquire it, and the associated time and costs. We use a recently developed artificial neural architecture called Cooperative Maximum‑Likelihood Hebbian Learning, a tool to develop part of an Integral Global Model of Business Management, which has the potential to bring about a global improvement in the firm by adding value, flexibility and competitiveness. From this perspective, the model used in the study generalizes the hypothesis of organizational survival and competitiveness, so that the organization is able to identify, strengthen, and use key knowledge to reach pole position. Our conclusions suggest that it is possible to specify the knowledge that is held but is underused in the departments, taking into account their current levels of knowledge, their relevance and the urgency to acquire new knowledge. Moreover, an analysis of the required evolution rate of the present knowledge may be included which, among other aspects helps detect new knowledge, eliminate obsolete knowledge and validate new needs.