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. 

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 In theory, AI offers the ultimate leapfrogging opportunity: companies with outdated systems could bypass incremental upgrades and jump directly to AI-powered solutions.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 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.