There is a great challenge in the business sector to adopt new technologies that boost companies to break into Industry 4.0, especially to obtain the capacity to adopt and develop complex systems based on: artificial intelligence, Big Data, Data Mining, and Cyber Physical Systems. However, efforts tend to be more of an empirical process, rather than a prior analysis, that allows companies to identify the complexity of the situation and trigger a viable implementation. For this reason, this research carried out a systematic review to identify and analyze, from the Systems Science approach, the proposed and most used models to face these organizational problems. In total, 42 of the 3800 documents were filtered for discussion using a systems approach. In addition, one of the models was tested by interviews with Mexican managers to understand how it promotes the abstraction of complexity necessary for a viable system change. The findings at the end of the work were to determine the lack of systemic properties in the current proposals, especially in the efforts to adopt artificial intelligence and the need to have a suitable model for the context of technology.

This manuscript presents the development and testing of a novel model designed to help organizations, particularly small and medium-sized enterprises (SMEs), address the challenges of integrating new technologies within the frameworks of Industry 4.0 and 5.0. The proposed model is a metamodel that evaluates organizational and contextual vulnerabilities concerning both existing technologies and potential external technologies under consideration for adoption. It synthesizes three foundational frameworks: the Viable System Model (VSM), the principles of viable and sustainable systems, and the Technology, Organization, and Environment (TOE) Model. The findings demonstrate the practical applicability of this model in an SME context, showcasing its ability to facilitate the gradual and sustainable adoption of new technologies. By aligning business needs with technological solutions and leveraging insights from computer science and organizational cybernetics, the model adapts to varying levels of technological adoption, integrating organizational dynamics and business evolution to support the implementation of emerging technologies.