PhD project This research project explores how digital data and analytics reshape culture, knowledge practices, and decision-making in real estate organizations. It examines whether the shift toward data-driven practices can drive innovation and support more sustainable ways of working in a traditionally conservative industry.
Real estate is a traditional, high-emission industry now pressured by rising costs, stricter demands, and expectations for transparency and efficiency—driving a need for data‑driven work and AI. This project examines how data and AI reshape daily practices, culture, and decisions in Swedish real estate firms, and how different roles use and interpret data. It also explores conflicts between experience and AI, trust in new tools, and organizational barriers. The goal is to understand whether data‑driven and AI‑supported work can foster innovation and more sustainable building management.
The real estate sector is one of the world’s most traditional and also one of the largest contributors to global carbon emissions. Buildings last for decades, operational practices are well-established, and decisions have historically relied heavily on professional experience and intuition rather than systematic data use. Today, however, the industry is undergoing significant pressure to change. Rising energy costs, stricter sustainability and climate regulations, and increasing expectations for transparency, efficiency, and better service quality are pushing real estate organizations to modernize their ways of working.
Digitalization has introduced new opportunities through the rapid growth of sensors, smart building systems, cloud platforms, and analytics tools. At the same time, new forms of artificial intelligence, especially those based on machine learning, are becoming more common in areas like energy optimization, predictive maintenance, and space utilization. Yet these technologies depend on reliable, well-managed data, and they often challenge established routines and professional knowledge. As a result, real estate organizations must transform not only their systems, but also their culture, practices, and ways of knowing.
This research project investigates how digital data, analytics, and data-driven AI tools shape everyday work, decision-making, and organizational culture in Swedish real estate companies. The project focuses on multiple professional groups, including managers, property technicians, digitalization teams, data engineers. By examining how these actors interpret, trust, share, and act upon data, the project seeks to understand how digital tools influence collaboration and reshape organizational practices.
A central part of the study is to explore the tensions that arise when data or AI-generated insights conflict with established expertise. For example, what happens when sensor data indicates a problem that technicians do not observe on-site or when an AI recommendation challenges a manager’s experience-based judgment? These moments of disagreement reveal important cultural and epistemic barriers that influence the pace and success of digital transformation.
The project also investigates broader organizational challenges associated with becoming data-driven, such as siloed data ownership, lack of shared definitions, limited data literacy, and conflicts between legacy routines and new digital workflows. These challenges often create friction that prevents companies from realizing the full potential of their technologies, especially when sustainability goals require coordinated, data-informed action.
Ultimately, the aim of the project is to understand whether - and under what organizational conditions -data-driven and AI-supported practices can enhance innovation, improve operational efficiency, and enable more sustainable building management. By combining insights from Information Systems research with real-world industry practices, the project contributes to a deeper understanding of how data and AI can meaningfully support the real estate sector’s transition toward a more environmentally responsible and digitally mature future.
