AI-ENABLED BUSINESS MODELS IN THE CIRCULAR ECONOMY: UKRAINIAN CONTEXT - Lisova, Ruslana (Scientific Bulletin of Ivano-Frankivsk National Technical University of Oil and Gas (Series: Economics and Management in the Oil and Gas Industry), 2025)
The article under discussion herein expounds upon the conceptual framework for integrating artificial intelligence (AI) technologies into business transformation processes in the context of achieving sustainable development goals through the implementation of circular business models. The primary focus is directed towards the potential of AI as a catalyst for transformative changes in conventional business practices, encompassing the enhancement of energy efficiency, the reduction of greenhouse gas emissions, and the optimization of resource utilization. The author undertakes a thorough analysis of three distinct categories of AI-oriented circular models: narrowing, slowing down, and closing resource flows. The use of intelligent technologies has been demonstrated to facilitate the identification of bottlenecks in business processes, the prediction of maintenance needs through predictive analytics, and the assurance of material reuse through AI-technologies. The paper critically assesses the prerequisites for implementing such models in the Ukrainian context. The analysis identifies several key barriers to the adoption of these models in Ukraine. These include the low level of digital maturity of enterprises, limited access to modern digital solutions, outdated waste processing infrastructure, a shortage of specialists with relevant digital competencies, and a lack of incentive government policies. Concurrently, a range of opportunities for technological breakthroughs have been identified, including the flexibility of small and medium-sized businesses, the active development of national digital platforms (particularly Diia), and a substantial human capital base in the domains of IT and data analytics. A conceptual analytical framework is proposed for further empirical research using machine learning methods and synthetic data to assess the impact of AI on sustainable development indicators. The paper goes on to offer a series of recommendations, including the integration of AI into the economy through the support of pilot projects, the development of public-private partnerships, the digitalization of material flows, and the formation of a circular innovation ecosystem. The article posits a theoretical foundation for prospective applied research and the formulation of strategic decisions within the domain of sustainable business. This framework is situated within the broader context of digital transformation and post-war economic recovery.