Corporate real estate firms are enthusiastically embracing artificial intelligence, but few are achieving the goals they expected from the technology, according to the results of a survey of more than 1,000 real estate leaders across 16 global markets.
The number of companies running corporate real estate (CRE) AI pilots has exploded from 5% to 92% in just three years, noted Jones Lang LaSalle (JLL) in its 2025 global real estate technology survey.
“Now AI, once a subset of the technologies explored by only a handful of CRE teams, dominates nearly all real estate tech innovation discussions. The speed of this pivot has been unprecedented,” declared the report by JLL Global Research Director for Real Estate Technology Yuehan Wang.
However, it added, the industry is still in the early experimentation phase, with most organizations learning what works before scaling to full implementation.
While some companies proactively embrace the technology, based on genuine conviction, the report continued, a considerable portion of CRE teams implement AI not by choice, but by C-suite mandate, viewing AI adoption as a competitive necessity.
“This strategic gap translates directly into execution challenges,” Wang wrote. “While 92% are piloting AI, only 5% report having achieved most program goals. Though implementation is widespread, most initiatives remain experimental with limited scaling.”
“This isn’t just a story about technology maturity,” he added. “It’s about strategic choices, organizational capabilities, and systematic approaches that separate the 5% achieving real results from the 95% still searching for their breakthrough.”
Better Than a 5% Solution
Donatas Karciauskas, CEO of Exergio, an energy management company headquartered in Vilnius, Lithuania, agreed with JLL that many companies don’t see results. “However, that’s not a failure of AI, but rather a sign that most organizations still haven’t integrated it into their energy systems or use it on a surface level,” he said in a statement.
“When algorithms work with live data instead of static reports, they start improving the building hour by hour,” he continued. “It always leads to less waste and steadier conditions for the people inside.”
“Each site we manage generates tens of thousands of data points every day — temperature, flow, pressure, CO2, and occupancy — giving algorithms the context to adjust systems continuously,” Karciauskas explained. “And our success rate is significantly higher than the 5% mentioned in the report. The secret is simple: we just have to use AI thoughtfully.”
He added that the data-driven approach routinely cuts HVAC energy waste by 20% to 30% and saves more than €1 million annually in large commercial sites, all achieved solely through software.
“Most property management companies don’t have the technical infrastructure or expertise to implement AI effectively,” contended Minna Song, co-founder and CEO of EliseAI, a developer of conversational AI platforms for housing and health care operations in New York City.
“These businesses are not tech startups,” she told TechNewsWorld. “They’re operational businesses that need turnkey solutions, but we’ve seen too many try to deploy general-purpose AI tools that weren’t built for real estate’s specific workflows and compliance requirements. These horizontal solutions might handle one task well, but don’t integrate into the full chain of work.”
“Companies are looking for the best use cases for GenAI, and there is a lot of experimentation at play right now,” Kristen Hanich, director of research at Parks Associates, a market research and consulting company specializing in consumer technology products, in Dallas, told TechNewsWorld.
She pointed out that one of the main challenges companies face is related to data structure and cleanliness, which are immensely important for the reliability and validity of general AI. Another key challenge is that certain use cases people might assume are low-hanging fruit for GenAI, like lease abstraction, may not be in practice, and that hallucinations can cause operational and legal issues, she added.
“Embedding GenAI to specific workflows has a lot of potential for the right use cases, but it does take a specific approach to designing systems — virtualized workflows that are well-mapped and well understood, carefully trained models, and such — to create the reliability and consistency that companies need,” Hanich said.
“For those using public AI models, there is also the risk that data may be leaked,” she added. “We have seen companies get around this by leveraging private models instead.”
Why AI Shortcuts Don’t Work
“The explosion in AI pilots isn’t just hype — it’s driven by the promise of faster data integration and real-time decision-making,” said Ahmed Harhara, engineer and founder of HoustonHomeTools, a data platform that helps residents understand neighborhood-level environmental and housing risks, in Houston.
“The challenge is that many companies jump into AI without structured data pipelines or clear validation methods,” he told TechNewsWorld. “They expect AI to ‘leapfrog’ existing gaps, but models are only as good as the data lineage behind them. Without systematic data quality control, AI outputs become unreliable, especially in high-stakes fields like real estate or infrastructure.”
The JLL report noted that the promise of technological leapfrogging — where organizations skip intermediate steps to adopt cutting-edge solutions — has long captivated business leaders facing technology gaps. In theory, AI offers the ultimate leapfrogging opportunity, it maintained, allowing companies with outdated systems to bypass incremental upgrades and jump directly to AI-powered solutions.
“However,” it warned, “our research exposes a sobering reality. Rather than leveling the playing field, AI adoption is widening the gap between technology leaders and laggards, with companies that already run successful technology programs pulling further ahead in AI outcomes.”
Organizations can’t leapfrog the necessities of a successful AI implementation. “What is really required is a mindset change because this starts to change your business model, how you market, how you sell, how you negotiate contracts, how you find tenants,” said Daniel Burrus of Burrus Research, a provider of strategic advisory services, in Milwaukee.
“AI requires a really different way to look at your organization and a new way of thinking,” he told TechNewsWorld. “It’s not just two people in the organization that have to think differently. If you’re going to do something that’s big and throughout your business, you need to have everybody’s mindset shifted, and that isn’t done by a turn of a switch.”
AI Can’t Fix Bad Data
AI doesn’t improve weak digital foundations; it amplifies them, added Jason Chen, founder and technical director of JarnisTech, a printed circuit board maker, in Shenzhen City, China. “A company with poor data that’s stuck in antiquated technology can look forward to faster subpar results with AI models,” he told TechNewsWorld.
“The truth is that companies expecting AI to fill technology voids over the past decade are misguided,” he said. “Instead, AI works best with clean data that’s connected and up to date. In other words, there’s no such thing as leapfrogging digital maturity. You must build it.”
“AI is not a fix-all tool,” added Pasquale Zingarella, CEO of Invest Clearly, an online real estate investment platform based in Dover, Del. “It is a fast-moving resource that needs oversight.”
“You can’t dump exponential resources like AI onto legacy systems, data, and processes and expect gold bars to fall from the sky,” he told TechNewsWorld. “If not implemented effectively, it can result in inaccurate and unreliable outputs, which could expose organizations to risk.”
