AI-Driven Digitalization in Finland’s Waste management Sector: Enhancing Circular Economy through Sustainable Technological Innovation

Abstract

This paper examines the role of Artificial Intelligence (AI)-based digitalization in the implementation of the Circular Economy (CE) in the waste management (WM) industry in Finland by applying the Socio-Technical Systems (STS) theory. The study is a qualitative single-case study of Stormossen one of the most successful Finnish companies in the waste management area- the study explores the relations between technological structures, organizational operations and adaptation of people. Semi-structured interviews, document analysis and observations were employed to collect the data that were analyzed through the six-phase thematic analysis of Braun and Clarke (2006). The findings reveal eight interrelated themes: digital integration and tools, data collection and usage, organizational adaptation and learning, socio-technical interactions, CE outcomes, challenges and opportunities, and emerging AI potential. Results show that AI and digital tools enhance operational efficiency, traceability, and decision-making, while organizational learning and leadership foster socio-technical alignment. However, barriers such as data fragmentation, skill gaps, and limited policy integration constrain progress. The contribution to the theory of the study, in turn, is the generalization of the Socio-Technical Systems theory to a sustainability and AI platform, with the focus on the joint optimization of human and technical systems. In practice, it provides a digitalization roadmap to waste management companies on their way to the circular model in accordance with the CE objectives of Finland.