A corporate real estate practitioner’s guide to establishing a sustainable AI programme

Tony Gill and Edward Chick
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Tony Gill: Managing Director, Gill Advisors
Edward Chick: Co-founder and Chief Operating Officer, CorsicAI

Corporate Real Estate Journal, 2019, vol. 9, issue 2, 144-158

Abstract: Real estate professionals have always relied on a few predictable metrics to give structure and provide the basis for decision making. Today, artificial intelligence (AI) is permeating all economic sectors, and its impact will be transformational. Much has been written about AI, and pundits are weighing in on how it will affect corporate real estate (CRE) in fundamental ways. Within the industry, there is an ever-expanding range of case studies highlighting individual applications and how their adoption will dramatically increase efficiency and insight, but from a macro perspective, how can the concept of AI be explained in straightforward terms to a practitioner? In this paper, the author, who in 2014 contributed a piece to Corporate Real Estate Journal about the mounting influence of performance metrics,3 elevates the discussion by explaining how these pockets of data can be algorithmically synthesised to create greater levels of insight. It becomes the basis upon how organisations can create a plan for integrating AI by explaining the basic concepts, outlining the components of a data strategy, explaining the need to maximise user experience and discuss the rapid expansion of the Internet of Things (IoT). Given the pace at which this transformation is unfolding, it is critical that real estate professionals consider something like AI not as much as a ‘buzzword’ but part of a core strategy to enhance the way decisions within their own sub-sectors of the industry are made.

Keywords: artificial intelligence (AI); big data; data strategy; algorithms; data science; machine learning; disruptive technology; analytics; data repository; predictive analytics; data analytics; PropTech (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:aza:crej00:y:2019:v:9:i:2:p:144-158

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