The future of AI in CRE

Over the next five years, most companies (90.1%) expect to carry out corporate real estate activities, such as workplace strategy, occupancy management and lease administration, in a Hybrid 2.0 approach. Few CRE teams (5.3%) plan to continue relying mostly on manual work, while even fewer (4.6%) aim to fully automate most CRE activities.

To help organizations and CRE leads navigate this approaching future, this article examines the current implementation status of AI in CRE, along with the obstacles to its successful adoption, and advocates a systematic approach to getting the most from this technology.

The JLL Global Future of Work survey is a biennial survey which has been produced since 2011. It explores the evolving world of work and the key priorities, challenges and strategies of more than 2,300 corporate real estate decision-makers, as well as the emerging trends within organizations all over the world. 

The latest 2024 Future of Work edition offers fresh insights, which we are examining in a series of articles exploring key topics, from technology to design and ESG.

The weight of expectation on new technology inevitably leads to disillusionment when people run into difficulties implementing it successfully. This is not necessarily due to problems with the technology itself but because of unrealistic expectations and practical issues which appear as companies begin their AI pilots. Such failings may be due to incoherent strategies, impractical use cases, insufficient digital infrastructure or poorly organized data.

The FOW survey reveals that CRE is now close to this disillusionment phase. Survey responses show a gap between C-suite enthusiasm and on-the-ground implementation. For example, almost 70% of business leaders say they have an AI strategy in place for their CRE function and are piloting use cases. However, only 33% of senior managers report that they have an AI strategy in place. This strategy and implementation gap also exists between global and local functions.

Taking a systematic approach to realize the promise of AI

A well-structured AI strategy for CRE is necessary to prevent companies from falling into the trough of disillusionment. CRE leaders must move beyond the hype and adopt a systematic approach that emphasizes usefulness in realizing AI’s full potential.

JLL suggests a four-stage approach which will allow CRE professionals to cut through the hype and focus on the tangible and practical benefits of AI, to identify meaningful uses of the technology, to build a business case for using AI in the CRE function and to secure strong support from business leaders.

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.

The bulk of AI investments today go into enterprise-level implementations or transforming core business functions, which means CRE teams have to be proactive in demonstrating that investing in AI for the CRE function will empower it to better support the wider business.

This means showing how AI can deliver new insights, enhance decision-making, streamline data collection and analysis, enhance the workplace experience, drive operational efficiency and create energy savings. Working through the iterative process above will help them understand the business case and support it.

In building the business case for AI in CRE, professionals should motivate leaders and functional partners to think about the workplace of the future that supports and enables Hybrid 2.0—the blend of manual and automated processes. They must show how AI could empower a future-fit CRE team and how it will impact different functions across the organization.

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.