Implementering av AI för driftoptimering i kommersiella fastigheter

Sammanfattning

This study is a case study on the implementation of AI for optimizing building management systems in commercial real estate, with a focus on the perspective of property owners. The study addresses two main objectives. The first is to explore how AI can reduce energy consumption and improve building operations, as well as the incentives for adopting such technology. The second is to analyze the organizational and technical challenges associated with implementing AI and examine the risks perceived by stakeholders. The aim of the study is to investigate how AI technology can be utilized to optimize the management and operation of commercial properties, with a specific focus on property owners’ incentives and challenges. Additionally, the study examines the economic and environmental benefits of AI solutions, including energy savings and improved indoor climate. To achieve this, a qualitative case study was conducted, comprising a literature review and an interview study. The literature review establishes the theoretical basis for how AI contributes to operational optimization, while the interviews provide empirical insights from real estate companies that have implemented or are considering implementing AI. A total of ten interviews were conducted with seven real estate professionals and three individuals from the technology provider that served as the case company. The results demonstrate that AI implementation can lead to significant energy savings and more efficient operations in commercial properties. Respondents highlighted that AI-based systems can integrate effectively with existing building management systems, providing real-time monitoring of energy consumption and enabling automated adjustments that reduce energy costs while enhancing indoor climate conditions. AI's ability to process large datasets also allows it to anticipate future energy needs based on external factors such as weather and energy prices, further improving operational efficiency. However, the study identifies several technical and organizational challenges associated with AI implementation. Technical issues include compatibility problems with older systems, inaccurate sensor data, and difficulties integrating AI solutions into existing infrastructure. Proper preparatory work on the building’s systems and components is essential for optimal implementation. On the organizational side, skepticism among technical staff and building operators poses a significant challenge, often due to a lack of understanding of the technology’s functions and benefits, as well as fears that AI may threaten jobs. The study emphasizes the importance of education and communication to overcome these obstacles and underscores the need for close collaboration between property owners and technology providers. This study contributes to a deeper understanding of how AI can be leveraged to optimize operations and reduce the environmental impact of commercial buildings while providing insights into the challenges and opportunities property owners face during implementation. The findings can be used by real estate companies, property managers, and technology providers to better understand how to integrate AI solutions and navigate potential barriers. Future research is suggested on the long-term economic impacts of AI, as well as its social and occupational implications for building operators and other employees.