REIT AI Adoption: Which Asset Classes Are Moving Fastest
Morgan Stanley's 2025 real estate technology report projects $34 billion in cumulative efficiency gains for the REIT sector from artificial intelligence over the next five years. Yet a survey of major REIT operators reveals that fewer than 20% have deployed AI in production across core operations.
The gap between projected value and actual adoption defines the current moment. But beneath the aggregate numbers, adoption rates vary dramatically by asset class.
Industrial and Data Center REITs: Leading the Pack
Industrial and data center REITs have adopted AI most aggressively, for a straightforward reason: their business models depend on processing large volumes of standardized data.
Prologis, the largest industrial REIT globally, has publicly discussed using AI for demand forecasting, dynamic pricing optimization, and customer logistics analysis. The company's scale — 1.2 billion square feet across 19 countries — creates datasets large enough to train effective machine learning models.
Data center REITs like Equinix and Digital Realty are natural AI adopters given their technology-native operations. These companies use AI for power capacity planning, cooling optimization, predictive maintenance, and customer workload forecasting.
The common thread: these asset classes generate structured, high-volume operational data that AI models can process effectively.
Multifamily: High Potential, Slow Adoption
Multifamily has the largest addressable market for AI (by unit count) but among the lowest enterprise-wide adoption rates. The challenge is fragmentation: the multifamily sector includes everything from REITs managing 100,000+ units to small operators with 50-unit portfolios.
Where AI is gaining traction in multifamily: revenue management (dynamic pricing based on demand, seasonality, and comp data), maintenance request triage (AI routing and prioritization of work orders), leasing automation (chatbots, virtual tours, application processing), and operating expense optimization (energy management, vendor procurement).
What's holding adoption back: legacy property management systems that resist integration, fragmented data across multiple platforms, and the operational complexity of managing a diverse resident base.
Office: Bifurcated Adoption
Office REIT AI adoption mirrors the broader bifurcation of the office market. Class A office operators in major CBDs are investing in AI-powered building management — smart HVAC, occupancy sensing, tenant experience platforms — as a competitive differentiator in a market where tenants have abundant choices.
Class B and C office owners, many facing existential questions about asset relevance, have been slower to invest in technology. The irony is that these operators arguably need AI-driven analytics most — to identify repositioning opportunities, optimize marketing to targeted tenant segments, and model conversion scenarios.
Retail: Cautious but Accelerating
Retail REITs have been cautious AI adopters, reflecting the sector's broader uncertainty over the past decade. But adoption is accelerating in specific use cases: foot traffic analysis and tenant mix optimization, predictive analytics for lease renewal probability, omnichannel integration (analyzing how digital traffic correlates with physical visits), and site selection for new development (identifying underserved trade areas).
What's Blocking Broader Adoption
Data quality. AI models require clean, structured, comprehensive data. Most REITs have data scattered across multiple property management systems, spreadsheets, and legacy databases. The data integration challenge often exceeds the AI implementation challenge.
Talent. REITs traditionally hire real estate professionals, not data scientists. Building internal AI capability requires either hiring new talent or partnering with external technology providers.
Integration complexity. Enterprise AI deployment requires integration with existing property management, accounting, and reporting systems — a technical challenge that many REITs are not equipped to manage internally.
ROI uncertainty. While the aggregate ROI projections are compelling, individual use case ROI is harder to quantify, making it difficult to build internal business cases for investment.
The Operating Partner Model
The adoption barriers point toward an alternative model: rather than building AI capabilities internally, REITs partner with AI-native operating partners who bring the technology, domain expertise, and integration capability.
At Build, we operate exactly this model for development-oriented workflows. Our agentic AI platform integrates with REIT operations to execute site selection, due diligence, underwriting, and market analysis — without requiring the REIT to build an internal AI team.
The $34 billion efficiency opportunity is real. The question for REIT operators is not whether to pursue it, but which adoption path gets them there fastest.