Over the past decade, key CRE technologies have gradually reached maturity. Technologies such as environmental sensors, data modeling tools and predictive maintenance solutions are now adopted by over 80% of large occupiers, driving concrete value in physical workplace and employee experience.

Now AI, once a subset of the technologies explored by only a handful of CRE teams, dominates nearly all real estate tech innovation discussions. The speed of this pivot has been unprecedented. Just two years ago, under 5% of occupiers had plans to embed AI in CRE operations. Today, 92% of CRE teams have started piloting AI, or plan to start this year — a speed that outstrips even optimistic industry predictions.

This urgency is reshaping technology budgets. Real estate tech spending has been reorganized around AI initiatives, with the top 5 budget priorities all relating to implementing AI or preparing for its impact through upgraded cybersecurity and digital infrastructure.

However, this budget prioritization reveals as much about the challenges as the opportunities. The rush to invest in AI has notably outpaced strategic planning—comprehensive AI strategies for CRE remain absent in most organizations. 

While some companies proactively embrace the technology based on genuine conviction, a considerable portion of CRE teams implement AI not by choice, but by C-suite mandate viewing AI adoption as competitive necessity.

This strategic gap translates directly into execution challenges. While 92% are piloting AI, only 5% report having achieved most program goals. Though implementation is widespread, most initiatives remain experimental with limited scaling.

This raises a critical question: if achieving AI goals is challenging, how are we deciding where to focus limited resources?

Our research identified around 27 AI use cases across every aspect of the CRE value chain, from which occupiers are pursuing an average of five pilot projects simultaneously. 

Conventional wisdom suggests organizations should start with simple, low-risk applications—the 

so-called ‘low-hanging fruit’. Lease abstraction, for example, is widely considered an ideal GenAI application due to its document-heavy nature and limited embeddedness with other workflows.

Yet our research reveals a different approach emerging in CRE: despite facing limited resources and uncertain outcomes, CRE teams are prioritizing high-impact areas that directly address their most pressing business challenges. 

Three areas emerge as top priorities for AI pilot selection:

1. Real estate data-related workflows 

CRE teams work with complex datasets covering every aspect of building management, from energy consumption and employee satisfaction to payments, space utilization, indoor environmental data and more. However, such real estate data has historically been fragmented or inconsistent, impacting the depth and accuracy of portfolio-level insights. Occupiers are now looking at the groundbreaking capabilities of AI to tackle these challenges, exploring use cases for standardizing data and detecting anomalies, integrating different data sources, and automating data reports and presentations to enable a deeper, more holistic understanding of CRE operations. These initiatives may not generate immediate cost savings, but they create the data infrastructure necessary for all subsequent AI applications.

3. Energy management

93% of occupiers agree that sustainability, energy efficiency and decarbonization remain key drivers for technology adoption, with many increasingly turning to AI to accelerate progress. Energy management has been proven critical to both environmental compliance and cost-reduction measures. Current initiatives focus on use cases that can deliver long-term resilience for organizations, including AI for energy tracking and analytics, decarbonization roadmap planning and automated HVAC control. Unlike data workflows or portfolio optimization, energy management offers more immediate, measurable returns on AI investment. It is often considered as one of the most mature categories of AI use.

The promise of technological leapfrogging—where organizations skip intermediate steps to adopt cutting-edge solutions—has long captivated business leaders facing technology gaps. In theory, AI offers the ultimate leapfrogging opportunity: companies with outdated systems could bypass incremental upgrades and jump directly to AI-powered solutions. 

However, our research exposes a sobering reality. Rather than leveling the playing field, AI adoption is widening the gap between technology leaders and laggards, with companies that already run successful technology programs pulling further ahead in AI outcomes.

This divergence occurs amid resource constraints. 65% of organizations report experiencing CRE tech budget pressures over the past two years, forcing difficult prioritization decisions precisely when AI investment demands are high.

These budget pressures, compounded with operational challenges, have impacted decision-making. More than half of companies report longer tech procurement decision-making periods compared to pre-COVID timelines. This leads to a paradox where organizations need to move quickly on AI initiatives while internal processes have become more cautious. 

Two factors are driving this slower pace: persistent talent gaps that limit organizations' ability to evaluate and implement new technologies and increasingly stringent ROI expectations that require more extensive business case development before approval. 

Nonetheless, facing similar pressures, organizations with successful technology programs achieve considerably more with their AI efforts. These companies have the foundational capabilities—mature data infrastructure, established change management processes, experienced teams—that AI success requires.

Conversely, over 60% of companies must address fundamental technology issues, such as duplicated functionality or dormant systems, before fully leveraging AI capabilities. They face a double burden: catching up on the fundamentals while competing in AI innovation.

Companies that already have a successful CRE tech program display a much more systematic approach to integrating new tools. They define roadmaps with clear success metrics, change management and processes for stakeholder engagement – particularly securing sponsorship from at least one C-suite leader. 

This robust systematic approach is the key to implementing AI successfully. To establish this foundation, our research highlights four priorities for occupiers to act on: 

1. Ground expectations for AI with a multi-phase plan

The most effective AI programs balance quick wins that build confidence and momentum against longer-term foundational systems that require more effort and testing but ultimately drive greater business value. For example, an occupier might implement AI tools for optimizing energy consumption – which is straightforward to assess – alongside solutions to achieve more complex outcomes such as increasing portfolio agility amid market challenges. 

Some take comfort in seeing AI pilots fail, dismissing meaningful actions by claiming the technology isn't mature enough. But there's no going back—AI transformation will only deepen from here.