The future of AI in CRE

Key Highlights

• Corporate real estate (CRE) is already actively embracing artificial intelligence (AI), with 90% of companies planning to integrate AI to support human experts in CRE over the next five years.

• However, the peak of expectation in the AI hype cycle is approaching. Many companies have initiated AI pilots and training without a comprehensive strategy or roadmap for AI in CRE functions, risking disillusionment.

• CRE leaders must now move beyond the hype and adopt a systematic approach to realizing AI’s full potential. JLL suggests a four-stage approach that focuses on AI’s practical benefits, identifies meaningful uses, builds a business case for its application in CRE and secures strong C-suite support.

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. 

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.

Approaching the peak of expectations

The “hype cycle” chart produced by consultancy Gartner shows how enthusiasm for, and adoption of, a technology develops over time. As the hype around a new technology accelerates, people come to believe in it as a “silver bullet” solution which will work seamlessly to solve any problem. CRE is approaching the peak of expectations with regard to AI: 89% of leaders believe AI can help them solve major CRE challenges.

There is more to this than just positive sentiment: nearly two-thirds of companies have started piloting use cases for AI in CRE and implemented training plans to boost staff skills. Furthermore, as a bottom-up action, 73% of CRE professionals are early adopters, using AI to augment their day-to-day work. At the same time, on the supply side, an ecosystem of more than 700 PropTech companies is working on AI solutions for real estate.

Standing on the brink of disillusionment

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.

Many companies have been riding the wave of hype and begun AI piloting and training without a comprehensive strategy or roadmap for AI in CRE functions. The failure to take a systematic approach is likely to lead to failure and disillusionment with AI over the next few years.

Beyond the hype:

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.

1. Debunk myths, recalibrate expectations, create understanding

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:

Enterprise-level AI productivity tools: Such tools include ChatGPT-style large language model (LLM) tools, AI plugins for customer relationship management (CRM) systems and AI copilots for communications. These use cases are part of the overall enterprise-level AI business strategy and part of IT’s remit in most companies.

CRE-specific AI solutions: These are use cases dedicated to CRE activities. There are available products ranging from capital projects planning to portfolio management, workplace experience management and day-to-day building operations such as energy and utility management. These are areas where the CRE function is best positioned to explore AI use.

JLL analyzed over 300 real estate AI-powered technology solutions and found that AI is mostly used as a module in the product tech stacks to enhance problem-solving. Its role can be broadly categorized in four ways:

• Core capabilities, e.g., image recognition and generation 
• Enhanced analytics, e.g., more accurate time series predictions 
• New user interfaces, e.g., chat-based interfaces for data and insight query
• Automated processes, e.g., data injection and standardization

The best CRE-specific AI solutions are designed in a way that prioritizes user-friendliness and easy onboarding, integrates seamlessly with existing systems and is able to cope with data deficiencies while offering data generation opportunities. In summary, they empower CRE professionals to address their nuanced challenges.

2. Identify meaningful uses and prioritize through an iterative process

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.

CRE professionals need to consider where their top priorities are, existing work patterns, systems already in place, availability and capabilities of products in the market and of course the cost of implementation and current budget. This process, focused on usefulness, will identify the most meaningful AI use cases from tangible possibilities and prioritize them according to the company’s specific conditions:
 

• Location strategy: market positioning, trend analysis, cost analysis, optimized financing, streamlined site selection
• Portfolio optimization: data standardization and reporting, risk assessment, space forecasting, scenario planning, stress-testing portfolio strategies
• Design, fit-out and construction: fast design iteration, supply chain optimization, cost estimation, dynamic schedule optimization, construction site monitoring
• Lease administration: standardized lease abstraction, automated document management, compliance monitoring, automated auditing
• Sustainability strategy: energy analytics and modeling, dynamic energy sourcing, scenario-based decarbonization roadmap, automated HVAC systems
• Workplace and occupancy: Floorplan digitization, occupancy sensing, space utilization pattern detection, dynamic occupancy planning 
• Operations and maintenance: Predictive maintenance and cleaning, inventory analysis and procurement optimization, automated recordkeeping
• Employee experience: personalized environment control, intuitive room and desk booking with automated calendar updates

3. Take a proactive approach in building the business case for AI

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.

4. Secure strong C-suite support for AI initiatives in CRE

In another JLL survey conducted last year—the JLL Global Real Estate Technology Survey—we demonstrated that making decisions about technology adoption by a company’s CRE function is a complex process involving a range of stakeholders. Nearly half of respondents report that IT and innovation decision-makers are involved; one-third also report the involvement of facilities management, 31% sustainability and 30% finance teams.

In this web of stakeholders, C-suite support is a key element to success. The technology survey found that companies are three times more likely to have a successful CRE tech program if business leaders were engaged with it and took part in tracking progress, rather than simply being presented with the results.

In the case of AI, C-suite support is particularly crucial. It helps balance competing budget demands, secure necessary resources and provide approval for the upfront investment required. Moreover, this support ensures organizational patience during the initial experimentation phase, which is essential before the return on investment becomes evident.

Turning barriers into steppingstones

Respondents to the FOW survey identified a number of barriers to implementing AI in CRE, but these challenges can be recast as opportunities to enhance the digital capabilities of CRE over the long term.

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.