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.

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 In theory, AI offers the ultimate leapfrogging opportunity: companies with outdated systems could bypass incremental upgrades and jump directly to AI-powered solutions.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 divergence occurs amid resource constraints. 65% of organizations report experiencing CRE tech budget pressures over the past two years, forcing difficult prioritization decisions precisely when AI investment demands are high.

These budget pressures, compounded with operational challenges, have impacted decision-making. More than half of companies report longer tech procurement decision-making periods compared to pre-COVID timelines. This leads to a paradox where organizations need to move quickly on AI initiatives while internal processes have become more cautious. 

Two factors are driving this slower pace: persistent talent gaps that limit organizations' ability to evaluate and implement new technologies and increasingly stringent ROI expectations that require more extensive business case development before approval. 

Nonetheless, facing similar pressures, organizations with successful technology programs achieve considerably more with their AI efforts. These companies have the foundational capabilities—mature data infrastructure, established change management processes, experienced teams—that AI success requires.

Conversely, over 60% of companies must address fundamental technology issues, such as duplicated functionality or dormant systems, before fully leveraging AI capabilities. They face a double burden: catching up on the fundamentals while competing in AI innovation.

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. 

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. 

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.