Few CRE Companies Achieve AI Goals: Here’s Why

The commercial real estate (CRE) sector, known for its measured pace of technological adoption, is showing signs of accelerated integration of artificial intelligence (AI), potentially ushering in a new era of efficiency and strategic decision-making.

A recent survey conducted by JLL reveals a shift from initial AI experimentation towards more strategic applications aimed at fundamentally reshaping value creation within the industry. The findings suggest CRE firms are not just exploring AI for cost reduction, but are increasingly viewing it as a pivotal tool for revenue generation and gaining a competitive edge.

The JLL survey polled over 1,500 senior decision-makers across various sectors within the CRE investment and occupier landscape. It indicates that while still in nascent stages, AI is gaining significant traction in technology budgets. The focus is evolving from solely process optimization to leveraging AI for strategic growth initiatives.

According to the report, 88% of investors, owners, and landlords have initiated AI pilot programs, often pursuing an average of five different use cases concurrently. Occupiers are also rapidly adopting AI: over 90% are now running corporate real estate AI pilots. This represents a significant leap from just 5% engaging in such pilots a mere two years prior, highlighting the rapid pace of adoption. However, the path to full integration is not without its challenges.

Only 5% of respondents reported achieving all their program objectives, while nearly half have managed to reach two to three goals. This suggests that while enthusiasm is high, many AI initiatives remain in the experimental phase, struggling to deliver tangible growth. This underscores the complexity of AI implementation and the need for a more holistic approach.

Yao Morin, chief technology officer at JLL, notes the historical skepticism towards technology within the CRE industry makes the high adoption rate particularly striking. However, the fact that only a small percentage feel they’ve achieved all their objectives aligns with observations across other industries navigating the complexities of AI implementation.

The primary obstacle to achieving these goals is a constant recalibration of expectations. Companies are moving beyond simple task automation and operational efficiencies, and are now attempting to tie AI directly to revenue growth.

For example, some firms are integrating AI to enhance investment risk models, basing crucial investment and portfolio decisions on AI-driven analysis. This fundamental shift in operational methodology requires significant organizational changes.

Morin emphasizes that achieving revenue gains through AI requires more than just technological implementation. Companies must rethink their entire operating model and organizational structure to fully capitalize on the potential cost savings and revenue opportunities.

Despite prevailing economic uncertainties, companies are significantly investing in AI. JLL’s survey reveals that over half of the surveyed investors have secured substantial budget increases for AI-related initiatives in the last two years. The largest portion of this investment goes towards strategic advisory services for technology and AI, with respondents citing AI as the sole driver behind their increased budgets. The next priority is enhancing cybersecurity, data security measures, and the necessary infrastructure for AI integration.

Morin highlights a surprising finding: contrary to expectations that companies would initially focus on low-risk, easily achievable tasks, the survey indicates a shift towards addressing complex business challenges using AI. This signals a growing sophistication and a willingness to move beyond initial skepticism, focusing instead on leveraging AI for competitive advantage and solving pressing business problems, rather than merely automating simple operations. This points to a maturation of understanding and a desire to harness AI for transformative, rather than incremental, gains within the commercial real estate landscape.