Resistance is futile: How corporate real estate companies can deploy artificial intelligence as a competitive advantage

Patrick Mcgrath, Kaushal Desai and Philip Junquera
Additional contact information
Patrick Mcgrath: Chief Information Officer and Head of Client Technologies, Savills, USA
Kaushal Desai: Data Scientist and business intelligence specialist, Savills
Philip Junquera: Technical Analyst, Knowledge Cubed, Savills

Corporate Real Estate Journal, 2019, vol. 9, issue 2, 121-129

Abstract: Corporate real estate (CRE) typically adopts new technology at a slower pace than other industries. Those CRE service providers that are, however, able to effectively integrate and adopt artificial intelligence (AI) and AI techniques such as machine learning (ML) into pre-existing data structuring workflows will enable new customer analytics and standards for service that will elevate customer expectations. Specifically, AI will emerge as a competitive advantage for CRE service providers that are able to overcome two primary obstacles for effective integration and adoption: the fear of automation displacing human workers, and access to byzantine data stores across the CRE asset class. This paper presents best practices for surmounting those obstacles and analyses a test project that deployed machine learning artificial intelligence (ML-AI). Early results of this project show that AI enhances the productivity of data-structuring workflows, enabling customer analytics derived from legacy (paper-based) contracts within CRE by quickly converting these contracts into structured digital datasets. Furthermore, well-designed ML-AI solutions can make an immediate impact on top-line revenue growth by unlocking valuable information previously buried in legacy contracts for key clients. The project itself employed an ML-AI solution to abstract paper leases 39.2 per cent more quickly than an entirely human-driven process; however, the productivity gain alone is not the main reason why this specific application of ML-AI was chosen for the programme. Abstracting (ie structuring) the data contained in leases is critical to the digitisation of CRE as an asset class. Once digitised, further ML-AI applications can glean greater insights from the abstracted data, unleashing a deeper understanding of the value in the marketplace for CRE service providers. Most lease contracts are still recorded in paper or PDF and require a human abstract to become structured data. Consider the distinction between CRE and financial markets: CRE incumbents use the equivalent of physical stock certificates. Lease abstraction is therefore necessary to move from the current analogue system to a digital one that enables a high-speed, frictionless, data-driven marketplace.

Keywords: artificial intelligence; machine learning; data structuring; competitive advantage; early adopter advantage (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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