Surge in Corporate Real Estate AI Pilots but ROI Remains Elusive
Artificial Intelligence (AI) is rapidly transforming the landscape of corporate real estate, but the anticipated returns on investment (ROI) have proven elusive for many firms. A recent survey sheds light on the dichotomy between the enthusiasm for AI adoption and the actual results being achieved across the industry. This article explores the current state of AI in corporate real estate, the challenges faced by organizations, and the factors contributing to the significant gap in outcomes.
The surge of AI in corporate real estate
In the past three years, the adoption of AI technologies in corporate real estate (CRE) has skyrocketed from a mere 5% to an astonishing 92%, according to the 2025 Global Real Estate Technology Survey conducted by Jones Lang LaSalle (JLL). This shift highlights a growing recognition among firms that AI can drive efficiency and innovation in their operations.
Yuehan Wang, JLL's Global Research Director for Real Estate Technology, remarked on how AI, once a niche technology, now dominates discussions about real estate innovation. However, he cautioned that the industry remains primarily in the experimental phase. Most organizations are still figuring out effective implementations before scaling their efforts.
Interestingly, while many firms are championing AI adoption out of genuine interest, a substantial number are responding to mandates from their executive teams, viewing AI as a necessary tool to maintain competitiveness. This divergence in strategy creates significant execution challenges, leading to a stark contrast between the enthusiasm for AI and the reality of achieving meaningful results.
The reality of AI pilot programs
Despite the overwhelming number of firms piloting AI, only 5% report having met their program's goals fully. This disparity raises critical questions about the effectiveness and strategic alignment of these initiatives. According to Wang, the difference between those achieving results and those still struggling often boils down to strategic choices, organizational capabilities, and systematic approaches.
In this context, it becomes evident that simply implementing AI is not enough. Companies must approach AI thoughtfully and strategically to realize its full potential. This involves aligning AI initiatives with organizational goals and ensuring that the technology is integrated into existing workflows effectively.
Challenges faced by organizations
Many businesses encounter significant hurdles when attempting to leverage AI for real estate operations. These challenges include:
- Lack of Integration: Many companies use AI on a superficial level without fully integrating it into their operational frameworks.
- Data Quality Issues: AI relies heavily on data integrity. Poorly structured or unclean data can compromise the reliability of AI outputs.
- Inadequate Technical Infrastructure: Some organizations lack the necessary technical capabilities to implement AI effectively, leading to subpar outcomes.
- Misalignment of Use Cases: Companies often experiment with use cases that may not be suitable for their specific needs, resulting in wasted resources and ineffective strategies.
Success stories and best practices
Despite the challenges, there are notable examples of companies successfully integrating AI into their operations. For instance, Donatas Karciauskas, CEO of Exergio, shared how his firm uses live data to enhance energy management in buildings. By analyzing real-time data such as temperature and CO2 levels, Exergio has been able to reduce energy waste by 20%-30%, yielding significant cost savings annually.
Karciauskas emphasized that successful AI implementation requires a shift in mindset and a commitment to using AI thoughtfully. He stated, "When algorithms work with live data instead of static reports, they start improving the building hour by hour."
Moreover, Minna Song, co-founder of EliseAI, pointed out that many property management firms struggle to deploy AI effectively due to a lack of technical infrastructure. She noted that deploying generalized AI tools that do not cater to the specific workflows of real estate can lead to inefficiencies.
The importance of data integrity
Data integrity is paramount for AI to deliver reliable results. Kristen Hanich, director of research at Parks Associates, highlighted that a significant challenge for companies is ensuring that their data is clean and well-structured. This is crucial for the effectiveness of AI, as algorithms require high-quality data to produce meaningful insights.
Hanich also warned that certain use cases, such as lease abstraction, may not be as straightforward as they seem. The potential for AI "hallucinations" – errors that can have operational and legal ramifications – underscores the need for a careful, systematic approach in embedding AI into workflows.
The pitfalls of rushing into AI
Many organizations are drawn to AI by the promise of faster data integration and improved decision-making. However, as noted by Ahmed Harhara, founder of HoustonHomeTools, companies often leap into AI without adequate preparation. Rushing into AI without structured data pipelines or validation methods can lead to unreliable outputs, particularly in critical sectors like real estate.
The JLL report highlights that rather than leveling the playing field, AI adoption can exacerbate disparities between technology leaders and laggards. Companies already successful in technology are likely to gain even more from AI, leaving others behind.
Transformative potential of AI
Implementing AI effectively requires a fundamental shift in organizational mindset and processes. As Daniel Burrus of Burrus Research points out, AI adoption necessitates rethinking how businesses operate, from marketing and sales to tenant engagement and contract negotiations. This transformation is not instantaneous; it requires a collective shift in thinking across the organization.
Moreover, Jason Chen from JarnisTech adds that AI does not rectify poor data foundations; instead, it amplifies existing problems. Companies with outdated technology and weak data infrastructures face a daunting challenge in realizing the benefits of AI.
Ultimately, AI is not a panacea. It demands thoughtful implementation, robust data management, and a clear strategic vision to catalyze meaningful change in corporate real estate.
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