Artificial intelligence is no longer just a talking point in commercial real estate—it has become a driving force, according to JLL’s Global Real Estate Technology Survey 2025. Nearly 88% of investors report that they have already launched AI pilot initiatives, each exploring an average of five use cases spanning the commercial property value chain. For the first time, five of the leading six objectives for technology investment now prioritize growth and competitive differentiation over traditional cost-cutting measures, reflecting a sharp shift from operational improvement to strategic advantage.
Despite robust investment, the industry faces a stark divide. While 87% of CRE organizations are increasing their technology budgets in response to AI’s potential, more than 60% remain “strategically, organizationally, and technically unprepared for scaled AI implementation beyond pilots,” according to JLL. This readiness gap is creating a growing divide between leaders and laggards as the AI landscape evolves.
“There’s this fear of being left out,” Geoff Kau, head of AI and corporate function technologies for JLL, told GlobeSt.com. Companies are racing to keep pace with innovation while trying to avoid falling behind as adoption accelerates.
To meaningfully capitalize on AI’s potential, firms must develop a much deeper understanding of the technology. While most hype centers on generative AI tools—such as large language models for communication, and platforms that create images, video or code—the field draws on a suite of technologies dating back to the late 1950s. Machine learning, deep learning, neural networks and expert systems have all provided critical capabilities for years, and companies must look beyond the latest trends to fully appreciate AI’s breadth.
Bridging the talent gap is another immediate challenge. According to Kau, half of the surveyed investors acknowledge that their teams lack expertise beyond a basic level.
“If today, they don’t have people beyond the IT stuff, then there’s a really long way to build a team that can execute on their AI ambitions,” Kuo said. In many cases, organizations must still rely on external advisors while building out internal capacity.
Risks also stem from inflated expectations. Amara’s Law, a principle named after Roy Amara, states that short-term effects of technology are overestimated while long-term impacts are underestimated.
“There’s probably a problem of being too ambitious with what can be done with the technology today. That’s creating a mismatch,” Kuo explained.
He notes that current limitations—such as so-called “hallucinations” from large language models—remain, but these challenges should become manageable over the next 18 to 36 months as technology matures.
A pragmatic approach is essential, particularly when working with LLMs. Organizations must scrutinize data quality, determine how much to trust external inputs and rigorously organize internal information to ensure timely, accurate outputs.
Ultimately, maintaining a clear focus on long-term strategic questions—such as improving portfolio returns and guiding investment or divestment decisions—will be pivotal as AI’s role in real estate continues to expand.
© Arc, All Rights Reserved. Request academic re-use from www.copyright.com. All other uses, submit a request to TMSalesOperations@arc-network.com. For more information visit Asset & Logo Licensing.