Skills gaps have officially become the primary barrier to AI transformation in commercial real estate, surpassing budget constraints for the first time in 15 years of research. According to a new survey of 2,200 global executives, 36% of commercial real estate (CRE) leaders cite skills shortages in artificial intelligence, analytics, and emerging technology as their biggest obstacle to creating value, compared to just 30% who point to budget limitations.

This shift reveals a critical disconnect in enterprise AI strategy: companies recognize that AI will reshape their business, yet they lack the internal talent to act on that recognition. While 78% of CRE leaders believe AI will significantly impact their portfolio strategy within the next three to five years, only 15% have moved beyond pilot projects to actually transform their operations. The rest remain stuck in evaluation mode, waiting for clarity that may never arrive.

What's Holding Organizations Back from AI Action?

The gap between awareness and execution is wider than most executives realize. Despite widespread recognition of AI's importance, organizations face multiple obstacles that prevent them from moving forward. Understanding these barriers is essential for leaders trying to chart a path forward.

• Capability Shortage: Organizations lack employees with expertise in AI, data analytics, and modern technology systems needed to design and implement transformation initiatives.
• Cross-Functional Misalignment: Only 77% of all CRE organizations report effective collaboration between departments like real estate, human resources, IT, and finance, compared to 94% among AI-optimizing leaders.
• Technology Risk Uncertainty: Three of the top four risks threatening CRE portfolios are technology-related, including cybersecurity and data privacy concerns (47%), technology and AI disruption (41%), and uncertainty about how AI will affect space requirements (40%).

The irony is that many organizations have the financial resources to invest in AI but lack the people and processes to deploy it effectively. This has forced a rethinking of how companies approach capability building, moving away from the assumption that hiring alone can solve the problem.

How Are Leading Organizations Closing the Skills Gap?

The 15% of organizations actively transforming their portfolios share distinct characteristics that set them apart from their peers. These leaders have discovered that success requires more than technology investment; it demands a fundamental shift in how they think about talent and organizational structure.

• Expect Workforce Growth: Transformation leaders anticipate hiring and expanding their teams rather than shrinking them, signaling confidence in long-term business growth and the need for new capabilities.
• Build Resilience Into Strategy: These organizations embed flexibility and adaptability into their portfolio decisions from the start, preparing for multiple possible futures rather than betting on a single outcome.
• Adopt Long-Term Portfolio Thinking: Instead of making real estate decisions based on immediate cost savings, leaders take a multi-year view aligned with evolving workforce strategies and AI capabilities.
• Foster Cross-Functional Collaboration: Effective AI-optimizing organizations break down silos between CRE, HR, IT, and Finance, creating shared ownership of transformation outcomes.

Beyond these structural changes, leading organizations are experimenting with three distinct approaches to building capability without overextending their teams: partnering with external experts for immediate access to skilled talent, systematically building internal capabilities over time, and phasing investments based on where the organization currently stands in its transformation journey.

Why Is AI Adoption Moving Slower Than Expected in Some Markets?

While global CRE leaders recognize AI's importance, adoption timelines vary significantly by region and industry maturity. In India's office leasing market, for example, the picture is more cautious. A recent CBRE South Asia report found that 57% of office occupiers in India said AI has no material impact on their leasing decisions, despite 93% of surveyed occupiers implementing AI in some form.

The reason for this disconnect is straightforward: most Indian occupiers remain in early or exploratory phases of AI implementation. Roughly 59% are still testing proofs-of-concept and mapping systemic changes before committing capital or restructuring physical assets. This reflects a broader pattern where AI adoption outpaces real business transformation, particularly in markets where organizations are still building foundational AI capabilities.

For now, occupiers in India continue to prioritize high-quality workplaces for attracting and retaining talent, with premium office spaces remaining crucial despite AI's growing role in corporate strategy. Future real estate decisions will likely align more closely with AI strategies and workforce dynamics, but that shift is still in its early stages.

How Should Organizations Build Adaptive Capacity Without Certainty?

One of the most paralyzing aspects of AI transformation is the pressure to predict the future before acting. However, leading organizations have discovered that waiting for certainty is a luxury they cannot afford. Instead, they are building adaptive capacity through concrete, phased actions that allow them to learn and pivot as conditions change.

The framework for building this adaptive capacity includes several key elements. First, organizations should close skills gaps through targeted hiring, training, or partnerships rather than assuming they need to solve the problem all at once. Second, they should prepare for multiple workforce futures by building flexibility into their real estate and organizational strategies. Third, they should establish sensing mechanisms that signal when market conditions or business priorities have shifted enough to warrant a strategic pivot.

"The intention is there. The capability to act on it often is not," noted Flore Pradère, Research Director of Global Research at JLL, highlighting the gap between organizational awareness of AI's importance and the actual ability to execute transformation initiatives.

Flore Pradère, Research Director, Global Research at JLL

This adaptive approach is particularly valuable because it does not require organizations to have all the answers before they start. Instead, it creates a framework for continuous learning and adjustment, allowing teams to build confidence and capability as they move forward. The organizations that succeed will be those that can balance the need for strategic clarity with the flexibility to adjust course as new information emerges.

What Does This Mean for Your Organization's AI Strategy?

The shift from budget constraints to skills gaps as the primary barrier to AI success has profound implications for how organizations should approach their transformation roadmaps. It suggests that throwing money at AI initiatives without building the internal talent and cross-functional alignment to support them is unlikely to yield meaningful results.

For CRE leaders and other enterprise executives, the message is clear: the next phase of AI adoption will be won by organizations that can build and retain talent, foster collaboration across departments, and create operating models designed for an AI-enabled future. Budget remains important, but it is no longer the limiting factor. The real constraint is the ability to attract, develop, and deploy people who can translate AI opportunity into business value.

The good news is that this challenge is solvable. Organizations do not need to hire their way out of the skills gap; they can build capability through partnerships, systematic training, and thoughtful restructuring of roles and responsibilities. What they do need is clarity about where they stand, a realistic assessment of what capabilities matter most, and a commitment to building adaptive capacity over time rather than betting everything on a single transformation initiative.