Modeling the Collective Intelligence of Innovatively Integrated Enterprises

The article addresses the challenges of modelling the collective intelligence of innovation-integrated enterprises in the context of digital transformation. An overview of mathematical models used to describe collective intelligence technologies is given. It is noted that collective intelligence technologies focus on the effective use of intellectual potential in working with an enterprise’s organisational capital. The concept of IQ as a measure of intelligence has been confirmed as applicable to the enterprise. The paper proposes a new mathematical model for calculating the collective intelligence quotient IQ, which makes it possible to compare group capabilities with individual ones, and, in particular, allows demonstrating the possibility of increasing the efficiency of the intelligence quotient for each group member by dividing work according to the competencies of the participants. This mathematical model for calculating collective IQ can serve as a basis for assessing the efficiency of enterprises in using collective technologies. To address the most difficult modelling task – the synergy of the intelligence of different people in the course of joint work – a model is proposed that assesses synergy based on participants' analytical or creative abilities. A variant of such synergy is a joint solution to a problem using brainstorming technology. The proposed model enables assessing the effectiveness of collaboration and can also be used to select participants for collaboration. The scientific novelty of the study lies in the study of collective intelligence technologies in enterprise management, substantiation of the place of these technologies in the tasks of corporate informatization and calculation of the effectiveness of new technologies, which made it possible to prove the special role of collective intelligence technologies in the organization of work in the knowledge age. The results of scientific research and the authors' practical recommendations contribute to the effective use and development of collective intelligence in the design of knowledge management systems at enterprises and their network associations in promising knowledge-intensive technological areas.