JLL's latest "Global Real Estate Technology Survey 2025" reveals a surge in the application of artificial intelligence (AI) in building environments: 92% of corporate real estate tenants have already initiated trials of AI solutions, a significant leap from less than 5% three years ago. However, only 5% of companies reported that their initiatives achieved most or all of their expected results, highlighting a significant gap between the adoption rate of AI in real estate and the actual conversion of business value.
Key Findings: Imbalance Between Application Adoption and Value Realization
JLL's survey, covering over 1,500 senior decision-makers in commercial real estate (CRE) across 16 global markets, shows that pilot applications of AI in building automation control, energy efficiency optimization, workflow collaboration, and workplace management have become commonplace in the industry. Although almost all surveyed companies have conducted relevant pilot programs, and management generally views AI as a key driver for enhancing core competitiveness, the maturity of these projects is generally low – most remain in the experimental stage and are difficult to scale. Less than half of the companies successfully achieved two to three predetermined objectives. In terms of application scale, each company is simultaneously advancing an average of five different types of AI application scenarios, while JLL has identified 56 differentiated AI application models across the entire commercial real estate value chain.
Strategic Shift: Upgrading from Efficiency Tool to Growth Engine
The weight of AI in real estate technology budgets is continuously increasing, with 87% of companies increasing dedicated investments due to AI-related needs. Corporate executives are driving the transformation of AI applications from traditional efficiency-enhancing tools to core mechanisms that drive business growth and strengthen competitive advantages. Companies are no longer limited to low-risk technology trials but are instead focusing on high-value application scenarios to address urgent business challenges.
Implementation Bottlenecks: Insufficient Organizational Preparedness Hinders Scalable Results
JLL emphasizes that the level of organizational preparedness – especially data quality, IT infrastructure adaptability, and change management capabilities – is the primary factor hindering the realization of scalable value from AI. Currently, most companies' applications of AI remain superficial, or they struggle to effectively integrate algorithmic models with real-time building operational data. This directly limits the system's practical effectiveness in areas such as environmental optimization and resource waste reduction. Industry experts point out that compared to simply relying on the "smart building" concept, deeply integrating AI technology with existing automation systems often yields equivalent or even better results. This approach is particularly suitable for older buildings and property projects with a mix of buildings from different eras.
Industry Insights: Key Success Factors Beyond Technology
This report breaks the misconception that "success can be achieved solely through technology": the effective deployment of AI requires a customized digital transformation roadmap, a robust data support platform, and the ability to deeply embed AI into daily operational processes. Industry experts analyze that the gap between AI applications and business results is not due to inherent flaws in the technology itself, but rather stems from insufficient application integration, fragmented deployment, and a lack of clear strategic focus and scientific performance measurement standards.
Future Outlook and Action Recommendations
JLL and external industry experts agree that the next three to five years will be a critical window for determining whether the high adoption rate of AI in the real estate industry can translate into significant and quantifiable returns. Companies that are slow to act or fail to act during this period may risk losing their competitive advantage; while those that proactively build foundational capabilities—such as establishing high-quality data systems and building scalable digital infrastructure—are more likely to stand out in the AI transformation.
Overall, the real estate industry has rapidly embraced AI technology, but the current application maturity is still low, and large-scale deployment faces multiple challenges, resulting in only a few projects realizing their expected value. The successful implementation of AI in the real estate sector depends not only on piloting new technologies but also on comprehensive strategic planning, sufficient organizational preparation, and an integrated digital transformation path throughout the entire building operation process. The next few years will witness the extent to which companies narrow the gap between their AI application vision and actual business results.