AI in Corporate Real Estate: Adoption Gains Momentum
Artificial intelligence is now a firmly established part of the corporate lexicon, but what are the trends in adoption and, more specifically, how could AI shape the CRE function?
4 minutes to read
Artificial intelligence (AI) is reshaping industries worldwide, with adoption accelerating across multiple sectors, from healthcare to finance. According to PwC, 52% of companies globally have begun deploying AI in at least one business function, while McKinsey estimates that AI could contribute $13 trillion to the global economy by 2030. Yet, adoption is uneven. Many companies remain in the exploratory phase, hampered by constraints such as insufficient data infrastructure, ethical considerations, and a scarcity of AI-savvy talent. AI is gaining traction in corporate real estate (CRE), but its integration reflects these broader adoption patterns, balancing optimism with operational realities.
The Shift in Sentiment
AI adoption within CRE is gaining momentum. While 65% of Knight Frank Corporate Real Estate Sentiment Index respondents rate current AI usage as “low,” optimism abounds, with expectations that this figure will drop to one-third by the end of 2025. This view aligns with broader enterprise trends, where 48% of companies predict high AI adoption within the next year, primarily targeting operational efficiency and predictive management.
However, optimism does not equate to ubiquity. CRE professionals face hurdles ranging from a lack of clean, actionable data to cultural resistance within organisations. As highlighted by McKinsey, successful AI integration requires a foundation of robust data infrastructure, skilled teams, and clear and compelling use cases. Without these, efforts risk stalling in the dreaded “pilot purgatory”.
Operational Efficiency: The Core Use Case
AI’s potential in CRE primarily revolves around operational efficiency. Predictive maintenance, enabled by AI algorithms, can anticipate equipment failures before they occur, reducing downtime and costs. Similarly, AI-driven space optimisation tools help identify underutilised areas, allowing companies to reduce their real estate footprint without sacrificing functionality.
A 2024 report by Deloitte underscores the ROI of such interventions, noting that predictive maintenance can cut repair costs by 25-30% and reduce downtime by nearly 50%. This outcome is particularly relevant as CRE leaders grapple with economic pressures to do more with less.
Lease and Transaction Management
Lease management is another fertile ground for AI. Automated systems can streamline lease abstraction, compliance monitoring, and renewal tracking—traditionally labour-intensive tasks. In the Knight Frank survey, this was one of the top-ranked use cases identified by CRE leaders.
Industry examples abound. IBM’s Watson, for instance, has been deployed in real estate to analyse lease portfolios and flag anomalies, freeing human teams to focus on strategic tasks. Yet, such systems require precise implementation to avoid pitfalls, including regulatory missteps in data-sensitive regions like the EU.
AI and the Workforce: Job Reducer or Enabler?
One contentious topic is the impact of AI on headcount. While some fear job losses, respondents to our most recent sentiment survey anticipate marginal changes in headcount due to AI adoption. This view is echoed in PwC’s “Future of Work” report, which suggests that AI is more likely to augment jobs than replace them, shifting roles toward higher-value activities.
For example, AI can automate repetitive tasks like invoice processing, enabling finance teams to focus on scenario planning and strategic advisory work. However, as the World Economic Forum emphasised, this transition requires robust upskilling initiatives as the workforce adapts to an AI-enabled environment.
Sustainability and Smart Buildings
AI also intersects with sustainability, another pressing priority for CRE leaders. Smart building technologies powered by AI optimise energy usage and reduce carbon footprints. The sentiment index reveals a growing interest in integrating AI-driven solutions to achieve sustainability certifications.
A case in point is Google’s St. John’s Terminal in New York, which leverages AI to monitor and adjust energy consumption in real-time. Such initiatives demonstrate how CRE can align with environmental goals while achieving operational efficiency. Yet, as the International Energy Agency highlights, scaling these solutions requires addressing upfront costs and ensuring access to renewable energy sources.
Barriers to Adoption
While the potential is evident, barriers to AI adoption remain significant. First, data quality and accessibility are major hurdles. AI thrives on structured, high-quality data, yet many CRE organisations lack the infrastructure to gather, clean, and analyse the necessary inputs.
Second, cultural resistance within organisations slows progress. Leaders often face scepticism from teams wary of AI replacing jobs or disrupting established workflows. Third, ethical and regulatory considerations, such as algorithmic bias and data privacy concerns, add complexity. This complexity is particularly true in jurisdictions with stringent laws, like the European Union’s GDPR, which imposes strict controls on data handling.
Finally, the cost of implementation can be prohibitive. While AI offers long-term efficiency gains, the upfront investment in technology, talent, and training can deter smaller players. A 2024 Capgemini report highlights that 45% of organisations cite cost as their primary barrier to AI adoption.
Looking Ahead
The future of AI in CRE is a story of both promise and pragmatism. As adoption accelerates, companies must tread carefully, balancing innovation with caution. Success hinges on establishing strong data foundations, investing in workforce training, and prioritising ethical considerations.
While AI may not revolutionise CRE overnight, its incremental integration reshapes the industry. Leaders who embrace this change thoughtfully stand to gain a significant competitive edge in managing portfolios and driving broader organisational success.
In an era where AI is becoming a competitive imperative, the choice is clear: adapt or risk falling behind.
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