- AI adoption in real estate has moved from experimentation to active use, with almost two-thirds of survey respondents now actively deploying it and most of those reporting productivity gains. Still, the industry’s collective understanding of where AI creates—and does not create—durable value remains incomplete.
- The more consequential question is no longer whether firms are using AI but which activities benefit from automation and where human expertise continues to create value that technology cannot deliver. That distinction is shaping investment strategy, operating models, and competitive positioning for firms across the industry.
- As AI increasingly supports analytical and operational tasks, the ability to exercise judgment, build relationships, and deliver differentiated service and experiences remains an important source of value across the industry.
“Everybody is talking about AI, but very few people are actually doing it well. It is painful and slow and expensive.”
Artificial intelligence was already a significant topic in last year’s Emerging Trends report, where it was described as moving from “buzzword to operational reality.” Twelve months later, the adoption data has advanced. A clear majority of respondents are now actively using AI in their operations, and an even larger share rank it as the single most disruptive force facing the industry through 2029, ahead of construction costs, geopolitical instability, and energy and power grid constraints. The numbers suggest that AI has crossed from early experimentation into broader deployment across the real estate business.
What the aggregate data does not capture is how unevenly that deployment is occurring. Some firms describe transformative results: internal tools built at a fraction of prior costs, analytical capabilities that did not previously exist, operational efficiencies that are freeing capacity for higher-value activities. Others describe failed pilots, organizational resistance, data quality problems, and the difficulty of moving from a promising demonstration to a production system that reliably improves outcomes. The candid assessment from one operator, “very few people are actually doing it well,” may be the most representative observation in this year’s interviews. See “AI in Institutional Real Estate: Adoption Without Impact” for reinforcement of this point.
Where AI Is Gaining Traction
The shift from last year centers on what firms are doing with the technology and how it is affecting their business models. The most-discussed development among interviewees is internal tool development. Several firms describe building custom applications using AI-assisted coding that replace vendor software at significantly lower cost. For example, one economy extended-stay operator reports that a team member with no formal coding background produced roughly 80 percent of a custom operational tool that had been quoted at $400,000 by an outside vendor. The internal build cost a fraction of that amount.
Whether that experience represents a scalable model or an exceptional case is debatable (the firms reporting similar results tend to have strong data infrastructure and executive commitment to the process) and internal development carries its own ongoing costs in maintenance, updates, and technical support that are not always apparent at launch. But the example illustrates a shift in the economics of technology development that has implications for the proptech ecosystem and for how real estate companies evaluate build-versus-buy decisions.
Investment analysis is another area where interviewees describe meaningful progress. One institutional manager reports using AI to process unstructured data—such as container volumes, consumer spending patterns, and municipal filings—to inform an investment thesis that diverged from consensus. The specific bet, in a market where sentiment was negative, performed well. Several firms describe scenario-modeling capabilities that allow them to assess the portfolio-level impact of macroeconomic shocks in days rather than weeks. These are compelling individual examples, though it is worth noting that the connection between AI-assisted analysis and sustained investment outperformance has not been broadly established across the industry.
“They are not accounting for some sort of reduction in headcount because of it. I just don’t think they know what the impact is going to be. Right now, and for the foreseeable future, AI is going to be a tremendous positive.”
Fundraising is where AI’s competitive implications may be most immediately visible. Institutional allocators are increasingly asking about a manager’s data infrastructure and analytical capabilities during due diligence. The inquiries go beyond whether a firm uses AI to how it uses it, what proprietary tools it has developed, and whether its technology investment is producing demonstrable results.
Whether AI capability is already a decisive factor in capital allocation decisions or simply an emerging consideration is a matter of perspective, but the direction of the conversation is clear, and firms that can demonstrate thoughtful technology investment are finding it easier to differentiate themselves in competitive processes.
The Implementation Challenge
The enthusiasm about AI’s potential coexists with a more sober set of observations about what it takes to deploy it effectively. Interviewees describe several recurring challenges.
Data quality is the most fundamental. AI systems are only as useful as the data they process, and many real estate organizations have spent decades accumulating property, financial, and operational data across fragmented systems with inconsistent formatting and limited integration. Cleaning, organizing, and connecting that data is expensive, time-consuming, and unglamorous, and it must be done before any AI application can produce reliable results.
Governance and cost control present related challenges. Token consumption, cybersecurity exposure, and the risk of employees using AI tools without adequate oversight are real concerns that several interviewees describe as underappreciated. The operational cost of AI is not limited to the subscription fee; it includes the organizational infrastructure required to deploy it responsibly.
Talent is perhaps the most persistent constraint. Several firms note that the real challenge is not finding people who can use conversational AI tools (that skill is already widespread) but finding professionals who can embed AI deeply into business processes, connect it to proprietary data, and use it to improve decision-making at the organizational level. That capability remains scarce.
“Eventually every company will need a dedicated in-house AI expert.”
The observation captures both the ambition and the difficulty. Becoming a technology-capable real estate company requires investment in talent, infrastructure, and organizational culture that goes well beyond purchasing software.
Where Human Expertise and Service Create Value
Against this backdrop of technological adoption, a consistent observation emerges from the interview data that is worth taking seriously. The importance of human presence, personal service, and the quality of in-person experience in real estate is not new. It has always mattered, particularly in hospitality and in properties that compete on tenant experience. What interviewees are describing is a reinforcement of that longstanding preference, at a moment when the increasing prevalence of digital interaction in other parts of daily life may be making personal service more noticeable and more valued by the people who use buildings.
“As data and AI make data more readily available, the market becomes more available. And so then the real skill and the human value of recognizing what is not priced in becomes even more valuable.”
This observation speaks to a broad pattern that appears across sectors and an expectation of personal service that is intrinsic to the product. Multifamily operators that have invested in on-site programming, community management, and hospitality-inspired service approaches describe stronger resident retention and, in some cases, rent premiums relative to conventionally managed properties. The relationship between these investments and financial performance is difficult to isolate precisely; many variables contribute to occupancy and rent growth, but the directional signal across multiple interviewees is consistent. That said, preferences for human versus automated interaction vary by customer, activity, and property type. The 2026 Emerging Trends report noted that younger renters often preferred automated interactions for routine services such as maintenance request and package notifications, suggesting that the value of personal engagement is context-dependent rather than universal.
In office, the discussion has shifted from how to compel attendance to how to create environments where in-person interaction is valuable enough that people choose to come in. “Hoteling did not work. Hot desking did not work,” reports a senior leasing executive at a Manhattan office REIT. “What works is giving people a reason to come in that they cannot replicate at home. And that reason is almost always other people.” Landlords and operators that are succeeding in this effort tend to invest in programming, food and beverage, flexible space design, and community management, amenities and services that are labor-intensive and fundamentally human. Live events, sports-adjacent development, and experiential retail (discussed further in Trend 8: Reimagining Places) represent a related category that is largely insulated from technological displacement.
The Practical Distinction
The opportunity for real estate professionals is to balance AI and human investment. It is to understand, with as much specificity as possible, which activities in their businesses benefit most from automation and which depend on human expertise, judgment, and personal interaction.
AI can increasingly assist with data processing, financial modeling, reporting, and analysis. But faster analysis does not eliminate the need for experienced professionals to interpret results, challenge assumptions, and make decisions in the context of market conditions that technology may not fully capture. Similarly, while automation can improve efficiency in property operations, personal service and relationships remain important in certain sectors and customer interactions.
The human premium in real estate extends well beyond customer-facing service. It includes the professional judgment that experienced dealmakers bring to evaluating opportunities, the market knowledge that allows investors and advisers to contextualize data that AI surfaces but cannot fully interpret, and the relationship-based dealmaking that underpins much of the industry’s transaction activity. Complex decisions about risk, capital allocation, and strategy still depend on informed judgment that comes from experience. Interpreting and challenging AI-generated analysis is itself becoming a valued skill, one that developers, operators, lenders, and advisers all need as these tools become more embedded in day-to-day workflows.
How individual firms allocate resources between those two domains—and whether they treat them as complementary investments rather than competing budget items—may prove to be one of the more consequential operating decisions of this cycle. The distinction between implementing technology and generating measurable value from it is not yet fully resolved across the industry, and executives who maintain clarity about that distinction will be better positioned than those who conflate adoption with advantage.
