AI Adoption in Sustainable Strategic Decision-Making in the Real Estate Sector

Sammanfattning

This study investigates the adoption of Artificial Intelligence (AI) in sustainable strategic decision-making within the Swedish real estate sector. As the sector faces increasing sustainability requirements, expanding access to digital technologies, and growing demands for data-driven governance, AI is often presented as a tool with the potential to support more informed and effective strategic decisions. However, a substantial gap remains between this potential and its actual use in organizational decision-making. The problem addressed in this thesis concerns why Artificial Intelligence is not widely adopted in sustainable strategic decision-making, and how technological, organizational, and environmental drivers and barriers interact to shape this adoption. This problem is significant because the real estate sector must improve sustainability performance while operating in a context characterized by fragmented data infrastructures, regulatory pressure, and established decision-making routines. It is also suitable for a Master’s thesis project because existing research has identified several individual adoption barriers, but provides limited insight into how these barriers interact in a specific sectoral context and why increasing data availability does not necessarily lead to more advanced strategic use. To address this problem, the study applies the Technology–Organization–Environment framework together with absorptive capacity theory. The empirical material consists of eight semi-structured interviews with professionals in the Swedish real estate and consulting sectors. The data is analyzed through a thematic and abductive approach, enabling the study to move iteratively between empirical patterns and theoretical interpretation. The findings show that the limited adoption of AI is not primarily caused by a lack of technology or external pressure, but by organizational and systemic constraints. Three central mechanisms are identified. First, a data paradox emerges, where organizations possess increasing volumes of sustainability-related data but lack the structures and capabilities required to transform it into strategic decision support. Second, a reporting trap is identified, in which regulatory pressure encourages extensive sustainability reporting without necessarily improving operational or strategic use of data. Third, a trust gap limits the use of AI in high-stakes decision-making, as limited interpretability and organizational competence reinforce reliance on experience-based judgment. The study further suggests that AI adoption can be interpreted through a multiplicative interaction between technological, organizational, and environmental factors, where the organizational dimension appears to act as a central limiting constraint. The thesis contributes by offering a more integrated explanation of AI adoption in sustainable strategic decision-making and by showing why investments in data systems and AI tools alone are insufficient. The findings provide a basis for organizations to move from sustainability reporting toward sustainability steering by strengthening internal competence, improving interoperability, developing intermediary roles between data and decision-making, and aligning digital investments with long-term strategic capability.