Corporate real estate companies are rapidly adopting artificial intelligence, but only a small fraction are reaching the outcomes they hoped for. A new survey from Jones Lang LaSalle (JLL), which gathered responses from more than 1,000 real estate leaders across 16 global markets, reveals a striking contrast between enthusiasm and real impact.

JLL’s 2025 global real estate technology survey shows that AI experimentation in corporate real estate has surged from 5 percent to 92 percent in only three years. Yuehan Wang, JLL Global Research Director for Real Estate Technology, described AI as the new center of gravity for innovation conversations in the industry. According to the report, AI has shifted from a niche experiment to the dominant focus of CRE technology teams, although most organizations are still in the learning phase and far from full-scale deployment.

Many companies begin AI pilots out of conviction, but a significant number do so due to top-down pressure, viewing adoption as essential for competitive positioning. This mismatch between motivation and strategy often leads to inconsistent execution. While 92 percent of firms are piloting AI tools, only 5 percent say they have achieved most of their goals. The industry remains stuck in early-stage experimentation.

Wang noted that this is not simply a story about immature technology. The gap between the 5 percent achieving results and the 95 percent still seeking them is rooted in organizational capabilities, strategic discipline, and a systematic approach to implementation.

Beyond the 5 Percent

Some companies argue that AI failures are not due to the technology itself, but to the way it is used. Donatas Karciauskas, CEO of energy management company Exergio, said many organizations treat AI superficially instead of integrating it into live operational systems. When AI works with real-time data, he explained, it improves building performance hour by hour and reduces waste. Exergio’s systems collect tens of thousands of data points daily, allowing algorithms to adjust conditions continuously. Karciauskas said this approach routinely cuts HVAC waste by 20 to 30 percent and saves large properties more than one million euros per year, all powered by software alone.

Others point out that property management companies often lack the infrastructure and technical expertise to make AI effective. EliseAI co-founder and CEO Minna Song said many teams try to deploy generic AI tools that are not suited to real estate workflows or compliance requirements. These general-purpose platforms solve isolated tasks but fail to integrate across the full operational chain.

Kristen Hanich, director of research at Parks Associates, said companies are heavily experimenting but still struggle with basic challenges like data structure and data quality. Even use cases assumed to be simple, such as lease abstraction, can generate hallucinations that pose legal risks. Hanich noted that embedding GenAI in workflows requires careful system design and well-trained models. Companies using public AI tools also face data leakage risks, which drives interest in private models.

Why AI Shortcuts Backfire

AI adoption is rising because leaders hope it will enable faster data integration and real-time decision-making. However, many organizations begin pilots without strong data pipelines or validation processes. According to Ahmed Harhara, engineer and founder of HoustonHomeTools, companies often expect AI to compensate for outdated systems, although model outputs become unreliable without strong data lineage and quality controls.

JLL’s report cautioned against the idea of technological leapfrogging. While executives often hope to skip intermediate modernization steps and adopt advanced AI tools directly, the research shows that this rarely works. Instead of narrowing gaps, AI is widening the distance between tech leaders and laggards. Companies already proficient in technology pull further ahead, while those with weak digital foundations fall behind.

Daniel Burrus of Burrus Research emphasized that AI adoption requires a cultural and operational shift, not just a technology shift. He said AI transforms business models, marketing, sales, contracts, and tenant relationships. Meaningful implementation demands a company-wide mindset change, not a simple switch.

AI Strengthens What Already Exists

Experts agree that AI cannot repair weak foundations. Jason Chen, founder of JarnisTech in Shenzhen, said poor data and outdated systems only produce faster bad results when paired with AI. Clean, connected, and current data is essential and cannot be skipped. Pasquale Zingarella, CEO of Invest Clearly, echoed this view, noting that AI must be monitored carefully because it amplifies both strengths and weaknesses. Deploying AI on legacy processes without proper modernization can produce unreliable outputs and increase operational risk.

The industry’s rapid embrace of AI shows clear momentum, but the path to meaningful ROI depends on strategic focus, technical readiness, and organizational alignment. For now, most companies remain in the early stages, learning that AI is powerful only when the foundations beneath it are strong.

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