The future of AI in CRE

JLL’s latest Global Future of Work survey confirms that AI is already being actively implemented in the business world. More than nine in ten C-suite leaders believe AI will change the way the workforce operates over the next five years. A similar proportion plans to accelerate investment in AI over that period.

This means a new hybrid work model (Hybrid 2.0) is imminent, blending manual and automated processes, with AI supporting human experts across business functions. In this emerging work style, CRE is no exception. 

Myths abound around AI and its implementations, such as “AI will take my job,” “AI can automatically tell me what to do” or “AI is not yet mature enough for any meaningful CRE use.” While containing kernels of truth, these myths portray AI as an abstract concept with mysterious theoretical capabilities. 

In reality, however, AI serves as a product feature with clear boundaries of what it can or cannot do. Companies need to ground their understanding of AI in the context of viable product offerings which deliver measurable values for CRE. This understanding will naturally debunk myths that have hindered AI adoption and recalibrate companies’ expectations in formulating actionable AI strategy.

For CRE, there are two primary categories of AI tools being used today:

In the hype phase, many companies mistake AI adoption as the goal, rather than a means to solve their challenges. This approach often leads to disillusionment.

An iterative process is a series of recurring cycles for analyzing internal and external factors, such as business pain points, organizational AI capacity, current IT systems and AI product availability in order to reset expectations, weed out impractical uses and refine the focus on sustainable and effective application.

Q: Which three of the following represent the biggest barriers to greater adoption of AI by the CRE function?

• Cost of implementation and budget limitations: Using the iterative process above to assess AI applications will generate data on cost and benefits, which can be used to support the use case where benefits outweigh the cost.
• Data quality and availability: AI solutions can be used to rework data processes and obtain high-quality data, which will in turn support the case for further AI implementation.
• Data and cybersecurity risks: CRE leaders need to work with IT and trustworthy external providers in order to produce a safe and compliant environment.

As companies adopt Hybrid 2.0, AI solutions’ enhanced capabilities may drive leapfrog innovation in CRE and the wider business. To harness this power, CRE must meet the challenge of AI implementation head-on, partnering with experts, identifying the best tools and solutions and learning from industry best practices to formulate their strategy.