AI Adoption Across Real Estate Is Splitting by Asset Class — and Data Centers Lead by 18 Months

By Hector Herrera | May 6, 2026 | Real Estate

AI adoption in real estate is not happening uniformly across the industry. A new analysis from Build Inc. finds that data center developers lead every other asset class by roughly 18 months in AI deployment maturity, followed by industrial, then multifamily, with office lagging significantly. The divergence reflects both the operational complexity of each asset class and a structural irony: the asset class leading AI adoption is the infrastructure that powers the AI tools everyone else is trying to use.

The Asset Class Breakdown

The Build Inc. analysis maps AI adoption across four major commercial real estate categories:

Data centers — 18 months ahead. Not surprising given that data center operators are building for AI workloads, competing on energy efficiency, and managing infrastructure at a technical complexity level that demands automated systems. AI is embedded in facility management, power optimization, cooling systems, and predictive maintenance as a core operating requirement, not an optional add-on. Data center REITs like Equinix and Digital Realty have been deploying AI operations tooling since 2023–2024.

Industrial — fast follower. Warehouse and logistics facilities have strong operational use cases: lease management, space utilization analytics, dock scheduling, and security. The tenant base — e-commerce fulfillment, third-party logistics companies — already uses sophisticated AI operations software, creating pressure for the real estate layer to match. Industrial has been the fastest-growing major commercial asset class by investment volume for several years, and that capital flow is accelerating AI adoption.

Multifamily — catching up. Apartment operators have clear AI use cases: lead qualification and leasing automation, maintenance request triage, rent optimization, and resident communications. Several proptech platforms purpose-built for multifamily AI (Knock, EliseAI, RealPage AI) have achieved broad adoption. The challenge is unit economics — AI tools need to produce measurable NOI (Net Operating Income) improvement to justify at the individual property level, and smaller operators face proportionally higher integration costs.

Office — lagging. Office's AI adoption problem is compounded by its structural occupancy problem. With hybrid work having permanently reduced average occupancy, the business case for deploying AI operations tooling is harder to make — you're optimizing an underperforming asset. Space utilization analytics are being deployed (knowing which floors and conference rooms are actually used), but broader AI integration is slower.

The Structural Irony

The finding that data center operators lead AI adoption by 18 months contains an embedded feedback loop that deserves attention. Data centers are the physical infrastructure — the GPU clusters, the cooling systems, the power distribution — that make AI models possible. The operators of that infrastructure are the most sophisticated users of AI in real estate because they have to be: operating at the efficiency level required to win hyperscale contracts demands AI-driven optimization.

That means the asset class funding AI adoption across every other industry is itself the leading edge of AI adoption in real estate. Every time a data center operator deploys AI-driven power management or predictive cooling maintenance, they're generating operational knowledge and tooling that will eventually diffuse to other asset classes — just with an 18-month lag.

What's Driving the Gaps

The divergence in AI adoption across asset classes comes down to three factors:

Operational complexity. Data centers have thousands of variables to manage simultaneously — power draw, cooling efficiency, rack density, redundancy. That complexity makes AI tooling not just useful but necessary. Office buildings are simpler environments where a less sophisticated BMS (Building Management System) gets you 80% of the way there.

Data availability. Industrial and data center operators tend to have dense sensor infrastructure already installed for operational and safety reasons. Multifamily and office operators are more variable in their sensor footprint, which limits what AI systems have to work with.

Capital allocation pressure. Data center and industrial assets are in high demand; capital is flowing in, creating budget for technology investment. Office is in distress in many markets, which creates pressure to cut costs rather than invest in new tooling.

What This Means for Investors and Operators

For real estate investors, the AI adoption gap is one lens on long-term asset class competitiveness. Data centers and industrial — the leading adopters — are also the asset classes with the most attractive near-term fundamentals. That correlation is not coincidental.

For office operators, AI offers a genuine opportunity to improve the case for their assets — better space utilization data, automated building operations, AI-assisted tenant experience — but the adoption curve starts from a lower base and the capital environment is less supportive.

For proptech vendors, the opportunity is clearest where adoption is fastest (data center, industrial) and where the pain is highest (multifamily operations, office utilization). The vendors who crack AI-driven NOI improvement for multifamily at smaller scale will have the largest addressable market.

What to Watch

Track REIT earnings calls for specific AI ROI figures rather than vague AI strategy language. The first major REIT to publish documented NOI improvement attributable to AI operations tooling will set the benchmark the rest of the industry gets measured against. That disclosure is most likely to come from a data center or industrial REIT in the next two to three quarters.