AI's blind spot: CRE investors' top priorities aren't delivering the biggest returns

• AI returns are strongest in less prioritized places: Investors want AI to drive outperformance and strengthen capital deployment capabilities (49%), yet asset and property management deliver the strongest gains in efficiency (66%) and decision quality (57%).
• Access to AI is now the baseline. Agentic workflows and data set leaders apart: While 83% of investors have advanced at least one AI initiative, only 15% have developed custom agentic workflows across all AI deployment areas. Those that include custom agentic workflows are more than twice as likely to report broad benefits. Among investors whose data isn't AI-ready, 59% struggle to realize benefits, against 21% of those whose data is.
• Talent is the most cited source of competitive edge by investors. Yet fewer than one in four are using AI to develop it. Among those whose advantage is built on relationships and proprietary deal access, only 22% are deploying AI for relationship management. There is a gap between what investors claim sets them apart and where they’re actually putting AI to work. Lasting ROI will depend on capabilities competitors can’t copy by buying the same tools.
   

CHICAGO, Nov. 11, 2026 – Global real estate investors are running into an AI ROI mismatch, with the technology’s biggest payoffs misaligned with their top priorities despite rapid adoption across the industry. According to JLL’s (NYSE: JLL) 2026 Global Real Estate Investor AI Survey, 80% of investors report tangible AI benefits, yet 43% struggle to achieve their goals.

As enterprise LLMs have made deployment easier, the challenge for real estate leaders has shifted from accessing AI to integrating it into the business where it can create lasting value. In 2025, 88% of respondents were only testing or planning AI programs while today, 83% have pushed beyond test stages in at least one area, and 40% report scaling or embedding AI across all activity areas, with the median investor running seven use cases across five business functions, spanning capital deployment, asset development and property management. Despite this increased adoption, only 15% of investors have built custom agentic workflows across all deployment areas.

Investors need to rethink where they’re deploying AI to maximize returns. While investment outperformance and capability building in capital deployment remain the industry’s highest priorities (49% each), those areas are producing relatively limited results. In contrast, asset and property management, consistently ranked among the lowest priorities, delivers the strongest measurable outcomes across efficiency (66%), decision quality (57%), capability building (50%), and generated outperformance (46%).

“We are reaching a stage where investors need to revisit their AI strategy area through a value retention lens,” said Alexandra Bryant, Global CEO, Value & Risk Advisory at JLL. “In certain areas where using AI will become table stakes, such as faster underwriting, it is unlikely that will provide lasting value or competitive advantage. However, when considering AI to drive effectiveness, for example in asset management, the same efficiency flows into net operating income and asset value, and the investor retains the value add against past performance.”

The pressure that comes with increased AI adoption is also driving significant organizational change. Currently, 69% of investors report feeling direct pressure from AI’s impact on competition, talent, costs and operational change, 88% have hired dedicated AI talent or an AI lead, and 81% have engaged external advisors to help develop in-house capabilities.

“Competitive advantage will increasingly depend on how well investors design and own their agentic workflows,” said Emilio Portes, Chief Product & Innovation Officer, JLL Capital Markets Services and LaSalle. “Building a custom agentic workflow is not the same as simply using models. It is as much an organizational decision as a technical one. Someone has to own the process, decide how the work should be done, and make sure the agent can reach clean, connected data, responsibly. It’s a new set of capabilities for investors.”

What is the main takeaway from JLL’s 2026 Global Real Estate Investor AI Survey?
 

The survey found that while AI adoption has scaled dramatically, with 83% of major investors advancing past test stages, true organizational maturity remains low. Only 15% of investors have built custom agentic workflows, leading to widespread competitive convergence as firms use identical tools on identical data.

Why are real estate investors struggling to achieve outperformance from their AI investments?
 

First, investment outperformance in capital allocation is harder to achieve by nature: it means beating both competitors and the market, and it takes time to prove. At the same time, most investors apply similar general-purpose tools to the same purchased data and use cases, so their gains tend to be generic and similar. Outperformance depends on what an investor can do that others can’t. Differentiation in data and tooling is the key. But outperformance isn’t the right goal everywhere either. In asset and property management, where firms measure against their own past performance, efficiency gains stay with the firm and are a lasting return.

What is the difference between enterprise LLM adoption and agentic workflows in real estate?
 

Enterprise LLMs provide individual productivity tools for tasks like drafting and document review, but they do not embed institutional knowledge into operations. In contrast, agentic workflows integrate clean, connected data, context, and governance directly into redesigned business processes. Individual use keeps knowledge with the expert; agentic workflows institutionalize knowledge into lasting firm capabilities.

How does data readiness impact an investor's ability to realize AI benefits?
 

Data readiness is critical for value realization. Among surveyed investors whose data is not AI-ready, 59% struggle to realize meaningful benefits, compared to only 21% of investors who established clean, governed, and connected data foundations.

What three actions does JLL recommend for investors entering the next phase of AI adoption?
 

Investors must first evaluate AI investments through a value retention lens and refocus AI strategy around ROI that could last against competition, area by area. Next, organizations need to develop the capability to build proprietary agentic workflows and make the right build-buy decisions. Finally, leaders should steer AI budgets toward their firm’s unique competitive moats, such as proprietary operating history and relationship networks.