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:

2. Portfolio optimization

Amid ongoing market challenges, CRE portfolios are challenged to be agile, fluid and liquid as a key component in reducing operational costs, making portfolio optimization the most important baseline expectation for business leaders over the next three years. Space planning and location strategy are shifting from a once-a-decade ordeal to a quarterly requirement – and for very large occupiers, a continuous assessment to right-size their footprint and manage costs. The breadth of data involved in these processes means that AI can bring significant efficiencies, and many CRE leaders are piloting its use in portfolio analysis, optimization strategy and capital planning. 

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.

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. 

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.