• Corporate Real Estate (CRE) is at the dawn of an AI transformation. The number of companies running CRE AI pilots has exploded from 5% to 92% in just three years, but we are still in the early experimentation phase, with most organizations learning what works before scaling to full implementation.
• Strategic priorities drive AI pilot selection. Facing budget pressure, CRE teams are focusing their resources on high-impact areas like portfolio optimization, energy management and data-related workflows that align with C-suite business priorities, rather than pursuing ‘low-hanging fruit’ applications.
• Companies lagging in technology adoption face a widening gap in AI success. Despite unprecedented enthusiasm, many companies lack a systematic approach to AI implementation, widening the competitive gap between organizations who already have a successful tech program and those who are lagging.
   

Throughout 2025, Corporate Real Estate (CRE) professionals are awash with AI speculations—from bold predictions about transformation to skeptical warnings about overhype. Amid all the noise, decision-makers need to separate fact from fiction and have clarity on the true pace and payoffs of AI adoption in CRE. 

Based on insights from JLL's 2025 Global Real Estate Technology Survey (1,000+ senior CRE decision-makers in major industries across 16 markets), this analysis cuts through the hype to reveal where meaningful value is emerging, what separates successful AI implementations from expensive experiments and how the companies getting it right today are preparing themselves for waves of change we can't yet fully predict.

This isn't just a story about technology maturity – it's about strategic choices, organizational capabilities and systematic approaches that separate the 5% achieving real results from the 95% still searching for their breakthrough.

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:

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. 

2. Invest in AI talent and resources, internally and externally

Despite facing similar budget pressures, leading companies are better resourced in terms of AI skills and capabilities - and the greater the priority on nurturing innovation, the greater the return. Currently, only 33% of the workforce feel adequately trained on AI. Regarding sourcing AI capabilities, 70% of occupiers use multiple sourcing strategies: internal GenAI training, custom tool development, hiring AI talent and external partnerships with tech companies and service firms.

3. Strengthen data and cybersecurity systems

AI innovation must be supported by robust digital infrastructure that protects data and corporate systems. Retiring or upgrading legacy tech systems in CRE is an imminent challenge for CRE leaders to undertake without disrupting business functions or losing data. Such legacy systems represent key barriers to AI adoption, with 81% of companies reporting at least three existing systems that aren’t generating the expected results and 88% allocating budget to upgrade legacy technologies.

4. Align AI rollouts with corporate decision-making cycles

Technology adoption requires multi-level stakeholder buy-in and change management. Our survey respondents highlighted that the best time to adopt or change a technology system would be during other major changes to the business, such as an IT system overhaul, leadership restructuring, response to new regulatory requirements and/or during capital planning cycles. CRE professionals who align AI rollouts with planned organizational changes are best placed to secure resources and engage the workforce. 

Looking towards 2030 and beyond, the purpose of current pilots isn't just immediate ROI, but also providing critical learnings to inform a more encompassing, longer-term AI strategy for CRE.

Occupiers that wait idly for technologies to mature in the hope of a ‘second mover advantage’ risk competitive obsolescence as they miss the chance to experiment and understand how AI can deliver value for their unique operations. Rather, the true ‘second mover advantage’ lies in resisting AI hype while using the time to strategize, test carefully chosen AI use cases and nurture CRE teams’ capabilities. 

In the long run, AI’s most enduring value will belong to companies that build adaptive capacity for waves of change we can't fully predict yet. It's not just about being more efficient or growing faster – it's about developing the organizational DNA to continuously evolve as AI capabilities advance. 

The time to start is now.