JLL survey finds real estate AI returns are missing top priorities

AI adoption has moved beyond testing

JLL said 83% of investors have advanced at least one AI initiative beyond the testing stage, compared with 88% of respondents who were still testing or planning AI programs in 2025. Forty percent now report scaling or embedding AI across all activity areas.

The median investor is running seven use cases across five business functions, including capital deployment, asset development and property management. The findings suggest that access to enterprise large language models has become a baseline capability, shifting the challenge toward how firms integrate AI into their operations.

Despite that progress, only 15% of investors have built custom agentic workflows across all AI deployment areas. JLL defines these workflows as processes that connect data, context and governance to redesigned business operations, rather than simply giving individuals tools for tasks such as drafting and document review.

The biggest gains are in lower-priority areas

Investors rank investment outperformance and capability building in capital deployment as their top priorities, at 49% each. However, JLL found that asset and property management deliver stronger reported results. These areas produce gains in efficiency for 66% of investors, decision quality for 57%, capability building for 50% and generated outperformance for 46%.

JLL said capital allocation outperformance is inherently difficult to measure because it requires beating both competitors and the market, and takes time to prove. By contrast, efficiency gains in asset and property management can be measured against a firm’s past performance and retained by the organization.

“We are reaching a stage where investors need to revisit their AI strategy area through a value retention lens,” Alexandra Bryant, JLL’s Global CEO, Value & Risk Advisory, said in the announcement. She said faster underwriting may become “table stakes,” while AI-driven effectiveness in asset management can flow into net operating income and asset value.

Data and proprietary workflows shape lasting value

Data readiness is a major dividing line in the survey. Among investors whose data is not AI-ready, 59% struggle to realize meaningful benefits, compared with 21% of investors with clean, governed and connected data foundations.

JLL also found that 69% of investors feel direct pressure from AI’s effects on competition, talent, costs and operational change. Eighty-eight percent have hired dedicated AI talent or an AI lead, while 81% have engaged external advisors to help build internal capabilities.

Talent is the most frequently cited source of competitive edge, yet fewer than one in four investors are using AI to develop it. Among investors whose advantage is based on relationships and proprietary deal access, only 22% are deploying AI for relationship management.

For users and the market, the findings point to a shift from buying general-purpose tools to building capabilities that competitors cannot easily copy. JLL recommends evaluating AI through a value retention lens, developing proprietary agentic workflows and directing budgets toward distinctive assets such as operating history and relationship networks.

“Competitive advantage will increasingly depend on how well investors design and own their agentic workflows,” Emilio Portes, JLL’s Chief Product & Innovation Officer for Capital Markets Services and LaSalle, said.