Artificial intelligence as a driver of corporate environmental sustainability : a qualitative exploration of current applications and future potential

Climate change is an inevitable phenomenon that requires a societal shift towards more environmentally sustainable practices. Companies are currently facing increasing pressure from various stakeholders, including regulators, to operate more sustainably. At the same time, the rapid advancement of Artificial Intelligence (AI) is creating new opportunities for businesses. This study examines the synergies between these two developments. Specifically, it analyzes how current AI applications support companies in their environmental sustainability efforts and explores the potential this technology holds for the future. The research aims to explore practical aspects of this intersection by providing an informative overview of existing AI applications, the challenges companies face, and the potential opportunities AI offers for environmentally sustainable corporate development. An inductive qualitative research approach was applied, involving semi-structured expert interviews with five participants. The interviewees worked either in the sustainability departments of companies or as business consultants and experts in the field. The data was analyzed thematically to identify underlying patterns and compared them with existing literature. The study identified two main categories of AI applications that are currently in use. Operational applications are integrated to improve efficiency, leading to reduced energy and resource consumption and thereby indirectly supporting environmental sustainability. In contrast, AI applications at the data, analytics, reporting, and strategic level help to process large volumes of data and support reporting tasks. Beyond pioneering companies, AI applications aimed specifically at sustainability are not yet widely implemented in companies and are still at an early stage of development. Nevertheless, significant potential was recognized in both application categories, particularly in relation to Generative Artificial Intelligence (Gen-AI). Realizing this potential, however, depends largely on addressing key implementation challenges, including the availability of high-quality data, regulatory clarity, and organizational readiness. Given the limitations of this study, future research could benefit from focusing on specific sectors, geographic regions, or AI models. Moreover, considering the fastpaced evolution of both AI and corporate environmental sustainability, studies conducted in the near future are likely to provide further valuable insights.