Key Takeaways
- AI is projected to generate 330 million square feet of additional U.S. CRE demand over the next decade, but gains are uneven across sectors, markets, and scenarios.
- Industrial real estate is the strongest beneficiary, driven by automation, supply chain reconfiguration, and AI-enabled throughput and efficiency.
- Office faces near-term recalibration as AI prioritizes efficiency over headcount, but demand stays net positive and becomes more durable over time.
- AI doesn't uniformly boost or reduce CRE demand. It changes the path of growth, risk, and dispersion, making flexibility and asset quality the primary risk mitigants.
- More than 50% of corporate leaders cite data quality as the single biggest barrier to AI adoption in CRE operations.
Cushman & Wakefield's May 2026 analysis, “AI Impact on CRE: The Next 10 Years,” is the first global, multi-sector, scenario-based assessment of AI's impact on CRE.
The report doesn’t try to predict how AI technology will evolve, but how businesses will respond to it and how their responses will impact the CRE environment.
And that impact could be significant.
The study projects that AI adoption will add approximately 330 million square feet of U.S. CRE demand over the next decade. That's a 12.2% increase over pre-AI net absorption forecasts through 2035.
This increase reflects Cushman & Wakefield’s predictions that the industrial sector will need an additional 298.5 million square feet (msf) due to AI, and office space demands will increase by 24.4 msf. In addition, they predict multifamily homes will grow by 94,400 units, and retail space will add 6.7 msf.
“AI will be disruptive, and there will be some displacement, but it will also create new businesses, which we are already seeing in the data,” said Kevin Thorpe, Chief Economist at Cushman & Wakefield. “Ultimately, AI is an additive to real estate demand. Productivity gains don’t shrink the economy; they expand it. Companies produce more at lower cost, margins improve, wages rise, and that broader growth flows through to space demand across every sector."
Four Scenarios, Not One Forecast
Cushman & Wakefield models AI's impact across four internally consistent scenarios.
- Baseline (50% probability): AI adoption is gradual, yet additive over time. The economy expands steadily as productivity moderately increases. Some sectors see more gains than others.
- Productivity-led expansion (15% probability): Faster AI adoption drives stronger revenue growth. New firms and occupations emerge, favoring flexible, high-quality space. Property values rise and rent grows.
- AI Bust (25% probability): AI demand isn’t as high as projected. Overinvestment and financial-market disruption lead to an economic downturn. Office vacancy rises sharply.
- AI Displacement (5% probability):0 AI substitutes for labor more than expected. Revenue growth lags productivity. Office vacancy stays structurally high throughout the forecast horizon.
How Each Sector Is Affected
AI affects each property sector through different economic channels.
Office is the most scenario-sensitive sector. Near-term outcomes reflect recalibration and elevated churn as AI-driven efficiency slows hiring. Demand stays net positive over time but becomes increasingly polarized by quality, flexibility, and location.
The industrial sector is the clearest beneficiary across all four scenarios. Automation, reshoring trends, and supply chain optimization are expected to drive an additional 298.5 million square feet of absorption through 2035.
Retail will likely be less impacted by AI than it was by e-commerce disruption, depending on which scenario plays out. Consumer income growth and operational efficiency matter more in the Retail sector than direct AI labor impacts.
Multifamily benefits indirectly through job and income growth. As homes become less affordable, demand for rental properties will rise, particularly in areas where AI industry jobs are clustered.
Flexibility and asset quality are the biggest risk mitigants regardless of which scenario plays out.
For investors and tenants tracking where AI demand is concentrating today, Taazaa's analysis of AI's current impact on CRE markets covers the operational and investment implications across property types.
What It Means for Tenants and Investors
The research draws a clear distinction between how AI affects tenants and investors, because the mechanisms and timeframes are fundamentally different.
For tenants:
- Office tenants should prioritize shorter lease commitments and flight-to-quality assets that support collaboration and technology integration.
- L&I tenants should focus on automation-ready layouts, power capacity, and location efficiency.
- Retail tenants are influenced more by household income and consumer behavior than by AI labor displacement.
For investors:
- Office investment risk is highly scenario-dependent. Quality, location, and conversion optionality are the key variables.
- L&I assets remain comparatively resilient across all four scenarios, supported by income durability and long-term demand visibility.
- Across all sectors, capital markets act as an amplifier: where productivity gains translate into absorption, pricing improves; where monetization is delayed, risk premiums stay elevated.
The AI Impact for Property Owners Today
For existing properties, AI is already having a transformative impact.
JLL's research frames this as a shift from narrow AI tools toward agentic systems that plan, coordinate, and execute across multiple steps. “Buildings have so many complex data points coming in,” said Neil Murray, CEO of JLL's Real Estate Management Services. “The companies that figure out how to harness this wealth of information will be at a distinct advantage.”
AI is already delivering measurable results across four operational domains:
- Investment analysis: Portfolio analysis and scenario modeling that previously took weeks now runs in near real-time.
- Property management: AI agents handle tenant communications, maintenance dispatch, and work order management at scale.
- Lease and contract processing: Lease abstraction and CAM reconciliation are compressed from hours to minutes.
- Market research: Brokers redirect capacity from data aggregation toward client relationships and strategic work.
Automated valuation models in real estate are also moving beyond residential applications into commercial portfolios, where AI's ability to process lease terms, tenant credit quality, and capital expenditure requirements at scale is changing transaction speed and portfolio monitoring in ways that were impractical two years ago.
The Data Barrier
JLL's research identified data quality as a significant roadblock to AI success. More than 50% of corporate leaders in JLL's Future of Work survey identified it as the single biggest barrier to AI adoption in CRE.
Most CRE organizations sit on enormous volumes of property, transaction, and operational data in disconnected systems and inconsistent formats. The organizations pulling ahead treat data infrastructure as a prerequisite, not a parallel workstream.
Taazaa's work with Innago, one of the fastest-growing property management platforms in the U.S., illustrates exactly what structured data infrastructure unlocks. By consolidating Innago's fragmented data sources into a single, queryable data warehouse, Innago achieved 150% business growth. The data foundation wasn't a side project. It was the enabler of every subsequent operational and product decision. That same dynamic applies directly to CRE organizations building AI capability at scale.
Preparation Over Prediction
AI is unlikely to reshape commercial real estate through a single, predictable channel. Like electrification and the creation of the internet AI’s effects will unfold through uneven adoption, delayed productivity realization, and big compositional change.
For CRE investors and tenants, three priorities follow from this:
- Prioritize adaptable, high-quality assets over favorable near-term metrics.
- Treat data infrastructure as a strategic investment, not an IT cost.
- Start building AI operational capability now, before it becomes a baseline expectation.
To build AI-powered CRE solutions that address both the demand intelligence and operational transformation challenges, contact Taazaa. We develop custom software and AI systems for commercial real estate organizations navigating this transition.
Frequently Asked Questions
Will AI increase or decrease demand for commercial real estate?
If Cushman and Wakefield’s baseline scenario plays out, the net effect is an increase of approximately 330 million square feet of U.S. CRE demand over the next decade. Industrial will be the strongest beneficiary, office will need to recalibrate before recovering, and multifamily and retail will benefit downstream from AI-driven economic expansion.
Which commercial real estate sector benefits most from AI?
Industrial is the clearest beneficiary across all four scenarios, with nearly 298.5 million square feet of additional projected U.S. absorption through 2035. Automation requirements, supply chain reconfiguration, and data-center-adjacent infrastructure are feeding demand, with a growing quality divide between AI-ready and AI-lagging assets.
What does AI's impact look like for office real estate specifically?
Office is the most scenario-sensitive sector. In the baseline, vacancy remains elevated and declines slowly. In the upside case, absorption turns positive sooner as new AI-driven firms create demand. In downside cases, vacancy stays structurally high. The consistent finding across all scenarios is a rising premium on quality, flexibility, and collaboration-focused assets.
What is the biggest barrier to AI adoption in CRE operations?
Data quality is the biggest barrier. More than 50% of corporate leaders in JLL's Future of Work survey identified it as the primary obstacle. Most CRE organizations have large volumes of property and operational data sitting in disconnected systems. Organizations that address data infrastructure before deploying AI tools consistently outperform those that treat it as a parallel workstream.
How should CRE investors position for AI's impact on asset values?
Focus on pricing dispersion, asset selection, and timing rather than sector-level bets. Office investment risk is highly scenario-dependent, making quality, location, and conversion optionality the key variables. L&I assets remain comparatively resilient. Across all sectors, flexibility and quality act as the primary risk mitigants because the distribution of outcomes is wider than in a pre-AI environment.
