GENERATIVE ARTIFICIAL INTELLIGENCE AS A FACTOR TRANSFORMING ENTERPRISE COMPETITIVENESS: A THEORETICAL AND METHODOLOGICAL FRAMEWORK

Abstract

As the global economy transitions to a cognitive stage of development, generative artificial intelligence (GenAI) and modern agentic systems, as key general-purpose technologies, are becoming a driving force reshaping approaches to value creation and the formation of firms’ competitive advantages. The purpose of this article is to synthesise, systematise and theoretically substantiate an approach to assessing the role of generative artificial intelligence in enhancing enterprise competitiveness. Drawing on existing theoretical approaches to the understanding of competitiveness and its attributes, the author clarifies the content of this category under conditions of widespread GenAI adoption and proposes an extension of the enterprise competitiveness model developed under the editorship of O. H. Yankovyi. The article substantiates the rationale for distinguishing innovation potential as an internal characteristic of competitiveness and strategic partnerships as an independent external characteristic. Building on this, the author proposes an approach within which the implementation of GenAI technologies in enterprise activities is examined at seven levels, grouped into four contours: managerial, market-ecosystem, resource-and-infrastructure, and operational-process. For each level, the potential effects of GenAI deployment and their relationship with key attributes of competitiveness—profitability, market (segment) share and price elasticity of demand—are identified. The practical significance of the proposed framework lies in its potential use as a theoretical and methodological foundation for further empirical research, the development of sector-specific roadmaps for the deployment of cognitive data-processing technologies, and the forecasting of the effectiveness of transformation projects at the enterprise level in the digital economy.