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?

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.

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. 

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.