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Artificial Intelligence and machines : A curse or blessing for corporate real estate?
Corporate Real Estate Journal,
7
(4),
337-351
(2018)
Abstract
The connected computational power of all devices in the world already exceeds the collective ability of humans to process the data. In general, societies, economic markets and national governments are both fascinated and disturbed of the rise of machines as the speed of evolution does not follow the rhythm of policy making and needs of users. At the same time, the real estate industry has just begun its journey to digital transformation. It faces a dichotomy as people adopt Artificial Intelligence (AI) as consumers, but fear what influence it could have on their jobs and privacy. A significant opportunity for corporate real estate (CRE) as it acts in between those forces to increase human experience, enable a work-life blend and support transformation as jobs will be enhanced through AI. At the same time, it is a humbling mission as AI will impact all levels of human needs. A significant proportion of those are fulfilled in and around properties. This paper outlines the key enablers and opportunities for the real estate industry to adopt AI as a productivity and transformational business power. It intends to deepen the understanding of key trends and give a fundamental background to reduce complexities and nuance the term AI. Therefore, key challenges are contextualised for CRE players to proactively strategise about partnerships, operations and value drivers for business entities. Ethical as well as social considerations are reflected as a part of worldwide debate on how machines can coexist with the human species.
Keywords: Artificial Intelligence (AI); smart buildings; advanced analytics; machine-learning; robotics; process automation; virtual reality
The full article is available to subscribers to the journal.
Author's Biography
Michael Ewert directs JLL’s Business Intelligence & Business Solutions in EMEA, assisting clients increase efficiency and reduce costs of their portfolio leveraging JLL’s big data and analytics platform ‘Red’ and Center of Excellence. He is leading various innovation initiatives to adopt JLL’s portfolio services and technology to the changing ecosystem, and identify the best solutions to enable digital transformation. His focus is on establishing leading partnerships and building products that use Artificial Intelligence (AI), IoT (Internet of Things) and Big Data globally to deliver deep insights and advanced analytics for JLL’s clients. Michael holds a B.Sc. in Management from the European Business School, Oestrich-Winkel and a LL.M in Real Estate Law from the University of Muenster.
Dominika P. Brodowicz is an Assistant Professor in the Innovative City Department, Warsaw School of Economics. She holds a PhD degree from School of Spatial Planning, College of Engineering & Built Environment at Dublin Institute of Technology. She is also an alumni of the U.S. Department of State SUSI programme at New York University and postdoctoral researcher in the CityScienceLab, a collaboration with MIT Media Lab at HafenCity University. Her research and working experience concerns sustainable development, green and smart cities and real estate market. Currently she is involved in advisory services for the City of Warsaw including works on ‘Warsaw 2030’ strategy and ‘Revitalization – Common Cause’ project.
Przemyslaw Pospieszny holds a PhD degree from Warsaw School of Economics where he defended his thesis in applied machine learning. Additionally, he holds a PgD in Knowledge Management from Dublin Institute of Technology and MSc in Business Computing (major in Statistics) from Poznan University of Economics. Since 2007 he has worked as Business Analyst and IT Project Manager in major international corporations on implementation of Big Data and Data Science platforms. Currently he leads JLL’s Data Lab in EMEA, a R&D unit that focuses on applicability of data science and Artificial Intelligence for commercial real estate companies. His interests concentrate on applicability of data science and machine learning for urban data, smart buildings and software engineering.
