AI Adoption Accelerates in Corporate Real Estate, but Clear ROI Remains Hard to Achieve:
AI is easily attracting corporate companies. However, very few firms are using AI to achieve their goals. This is revealed in a report that included over 1000+ participants from 16+ global markets.
Over the past three years, CRE (Corporate Real Estate) AI pilots have grown from just 5% to 92%. This growth is highly unexpected, according to the JLL (Jones Lang LaSalle) 2025 survey. According to Yuehan Wang, Global Research Director at JLL, AI has now become a major focus of real estate tech innovation, whereas previously only a few teams were exploring it. The speed of the shift has been described as “unprecedented.”
But the report also tells us that the industry is still in the early experimentation phase—most companies are simply learning what works and what doesn’t, before full-scale implementation.
Some organizations are seriously adopting, but many CRE teams are implementing AI only due to pressure from the C-suite (top management), as AI is increasingly seen as essential for a competitive edge.
Result? Strategy gap → execution problems.
92% of companies are running AI pilots, but only 5% are achieving their AI goals. This means that adoption is widespread, but scaling and real results are still limited.
Wang explains that it’s not just a matter of technology, but of strategy, organizational capability, and a systematic approach—all of which make the difference—which brings real success to the 5% of companies, while the remaining 95% are still searching for breakthroughs.
Rising Above the Limited 5% Result:
Donatas Karciauskas, CEO of Exergio—which runs an energy management company based in Vilnius, the capital of Lithuania—agreed with JLL that many companies are not getting the results they expected from AI.
He believes this is not a failure of AI, but rather a sign that companies have not properly integrated AI into their energy systems, or are simply using it superficially.
He explained:
“When algorithms work with live data instead of static reports, they optimize the building hour-by-hour. This reduces waste and makes conditions more stable for building occupants.”
Karciauskas explained that their managed sites generate tens of thousands of data points daily—such as temperature, flow, pressure, CO2, and occupancy. This data gives the AI context to adjust the system in real-time.
And their success rate is significantly higher than the reported “5%.” The secret is simple: use AI thoughtfully.
They also add that an AI-based data approach reduces HVAC energy waste by 20–30% and that large commercial sites save over €1 million annually—all through software alone.
Minna Song, CEO of EliseAI, believes that most property management companies lack the technical infrastructure or expertise to effectively deploy AI.
According to her, many companies deploy general-purpose AI tools that are not designed for real estate workflows and compliance, leading to broken integration.
Kristen Hanich, research director at Parks Associates, explained that companies’ biggest challenge is dirty or unstructured data, which impairs the accuracy and reliability of AI models.
Another challenge: People think some use cases will be easy—like smooth abstraction—but they are complicated in practice, and AI hallucinations can also create legal problems.
They also believe that embedding GenAI into workflows can be powerful, but it requires well-defined workflows, carefully trained models, and proper system design.
Using public AI models also carries the risk of data leakage, so some companies have started using private AI models to reduce the risk of data leakage.
Why Quick AI Hacks Fail to Deliver Results:
“The boom in AI pilots isn’t just hype—it’s driven by the promise of fast data integration and real-time decision-making,” said Ahmed Harhara, a Houston engineer and founder of HoustonHomeTools.
“The problem is that many companies launch AI without proper data pipelines or validation systems,” he said. “They think AI will magically fill the gaps in their systems, but AI models are only as good as their data. If data quality isn’t systematic, AI results become unreliable—especially in high-stakes sectors like real estate or infrastructure.”
The JLL report highlights that “technology leapfrogging”—meaning the adoption of advanced systems directly without intermediate steps—has long appealed to leaders. While the theory is that AI can bypass outdated systems and provide direct modern solutions, the report warns, “the reality is quite different.
AI adoption is actually widening the gap between tech leaders and lagging companies. Companies that already have strong technology systems in place are achieving much faster results in AI.” Daniel Burrus (Burrus Research, Milwaukee) believes, “A mindset shift is essential for AI to be successful. It affects your business model, marketing, sales, contracts, tenants, and everything.”
“AI won’t work by just changing the thinking of two people,” he said. “If you want to scale AI across the entire company, the entire team needs to have a mindset shift. This doesn’t happen by flipping a switch—it’s a process.”
AI Can not Fix Bad Data:
“AI doesn’t improve weak digital foundations—it simplifies them,” said Jason Chen, founder and technical director (JarnisTech, Shenzhen). “If a company’s data is of poor quality and stuck on outdated technology, using AI will only yield fast and crappy results.”
“The truth is that companies that think AI will magically fill the technology gaps of the last 10 years are mistaken,” Chen explained. “AI works best when its data is clean, connected, and updated properly. You can’t achieve AI success by skipping digital maturity—you need to build a strong foundation first.” “AI is not a one-size-fits-all magic tool,” said Pasquale Zingarella (CEO, Invest Clearly, Dover). “It is a fast-growing resource that requires proper oversight.”
“You can’t dump a powerful resource like AI on old systems, old data, and outdated processes and expect gold bars to fall from the sky,” he explained. “If AI is not implemented correctly, it can lead to inaccurate and unreliable results – which can create risk for the company.”
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