AI in Commercial Real Estate: Big Promise, Slow Adoption

Artificial intelligence is often framed as a game-changer for commercial real estate, promising greater efficiency and lower operating costs. While that vision is compelling, actual adoption across the sector has been slower than expected.

Recent industry survey data suggests that, on average, AI is used in less than a quarter of buildings within a typical property portfolio. Some organizations have fully embraced it, while others have yet to implement it at all. This uneven adoption highlights a broader challenge: many buildings simply aren’t equipped to support advanced technologies.

A major obstacle is outdated infrastructure. Nearly half of industry professionals report that older building systems, never designed with AI in mind, make large-scale implementation difficult. Cost is another concern. Many decision-makers are hesitant to invest heavily in AI without clear, proven returns. On top of that, a lack of in-house expertise continues to slow progress, with many organizations unsure about what solutions exist or how to apply them effectively.

For those already using AI, the most common application is energy management. Intelligent systems are being used to monitor and control heating, cooling, lighting, and overall energy consumption. Tools like automated building controls and smart metering systems help track usage patterns and optimize performance, often in real time.

Looking ahead, investment plans suggest a cautious approach. Nearly half of surveyed organizations intend to spend modestly on AI in the near future, while a significant portion has no immediate plans to invest at all. Only a small percentage are committing substantial capital. This indicates that many are still experimenting rather than fully committing to large-scale transformation.

Despite the slow uptake, there are signs of growing momentum. Interest in AI across the real estate sector has increased noticeably over a recent one-year period, with more companies planning to adopt these technologies. In some cases, early adopters have reported meaningful improvements, such as better tenant conversion rates, reduced vacancy periods, and lower operational costs, particularly when AI is applied to leasing processes or building operations like climate control.

Large-scale implementations have also demonstrated significant energy savings and reductions in emissions, along with improved equipment performance and occupant comfort. However, achieving these kinds of results often requires substantial investment and scale, which may not be immediately feasible for smaller property owners.

For organizations with limited resources, widespread AI integration will likely take longer. Still, optimism remains. Many industry professionals believe AI will steadily improve efficiency, even if it doesn’t completely transform the sector overnight. A smaller group expects more dramatic change, while only a few dismiss the technology as overhyped.

Overall, the industry appears to be in a transitional phase, interested, cautious, and gradually building the foundation needed to support broader AI adoption in the years ahead.