Overview of AI Adoption in Corporate Real Estate
JLLâs Global Research Director Yuehan Wang presents a comprehensive analysis of AI uptake across corporate realâestate (CRE) firms. Drawing on the 2025 Global Real Estate Technology Survey of over 1,000 senior decisionâmakers in 16 markets, the report shows that AI pilots have risen dramatically from 5 % to 92 % of occupiers within three years. While the majority are still experimenting, only 5 % report achieving most program goals, highlighting a gap between widespread piloting and successful scaling.
Key Drivers and Priorities
Budget pressure is steering CRE teams toward highâimpact AI use cases rather than lowâhangingâfruit projects. The top priorities are: (1) realâestate dataârelated workflowsâstandardising fragmented datasets, detecting anomalies, and automating reporting; (2) portfolio optimisationâcontinuous spaceâplanning and footprint rightâsizing to cut costs; and (3) energy managementâusing AI for tracking, analytics, decarbonisation roadmaps and automated HVAC control. Energyâmanagement pilots are noted for delivering more immediate, measurable returns.
Speed of Change vs. Strategic Planning
The transition to AI has outpaced systematic planning. Although 92 % of firms are piloting AI, comprehensive AI strategies remain absent in most organisations. This rapid adoption has led to budget reallocations toward AI, cybersecurity, and digital infrastructure, but also to mismatches between investment speed and internal governance, talent gaps, and heightened ROI expectations.
Impact on Technology Gaps
AI is widening the divide between technology leaders and laggards. Companies with mature dataâinfrastructure and changeâmanagement processes achieve far greater AI outcomes. Conversely, more than 60 % of firms must first address legacy system duplication, dormant applications, and fragmented data before realising AI benefits. Budget pressures affect 65 % of respondents, extending procurement cycles and slowing decisionâmaking.
Lessons for Successful AI Initiatives
Four priorities emerge for building futureâfit AI programmes:
- Ground expectations with multiâphase plans that balance quick wins (e.g., energy optimisation) against longerâterm foundational work.
- Invest in talent both internally (training) and externally (hiring, partnerships); only 33 % of workforces feel adequately trained.
- Strengthen data and cybersecurity to protect information and modernise legacy systems; 81 % report at least three underperforming legacy applications, and 88 % allocate budget for upgrades.
- Align rollouts with corporate cycles such as IT overhauls, leadership changes, or capitalâplanning periods to secure resources and stakeholder buyâin.
Sustainability and Energy Management Focus
Energy management stands out as a mature AI category with clear sustainability linkages. Ninetyâthree percent of occupiers cite sustainability, energy efficiency and decarbonisation as primary technology drivers. AIâenabled energy tracking and automated controls are delivering tangible cost reductions and advancing climate goals, aligning with panâEuropean interests in sustainable housing and builtâenvironment performance.
Outlook and Strategic Imperative
The report warns that waiting for a âsecondâmover advantageâ risks competitive obsolescence. Firms that proactively experiment, learn, and embed AI into longâterm strategy will be better positioned for the next decade. The overarching message for a European audience is that AI, when paired with robust data foundations, skilled talent, and strategic alignment, can accelerate sustainable housing outcomes, optimise portfolio performance, and enhance energy efficiency across the CRE sector.