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.

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. 

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. 

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.