According to CNBC, a new JLL survey of more than 1,500 senior commercial real estate decision-makers reveals that 88% of investors, owners and landlords have started piloting AI, with most pursuing an average of five use cases simultaneously. The survey found that more than 90% of occupiers are running corporate real estate AI pilots, a dramatic increase from just 5% starting AI pilots two years ago. Despite this rapid adoption, only 5% of respondents said they have achieved all their program goals, while close to half reported achieving just two to three goals. JLL Chief Technology Officer Yao Morin noted that while the high adoption rate is surprising for an industry traditionally skeptical of technology, the low success rate aligns with patterns seen across other sectors.
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The Pilot Project Trap
What the survey numbers don’t reveal is that commercial real estate is falling into what I’ve observed across multiple industries: the pilot project trap. Companies are running multiple AI experiments simultaneously without the necessary infrastructure to scale successful ones. When organizations pursue an average of five use cases at once, they’re spreading resources too thin and failing to build the data governance frameworks required for meaningful artificial intelligence implementation. This approach creates what I call “innovation theater” – lots of activity but little transformation.
The Data Quality Crisis
Commercial real estate faces unique data challenges that most technology-first industries don’t encounter. Property data is notoriously fragmented across legacy systems, manual processes, and incompatible platforms. Unlike e-commerce or financial services where digital transactions create clean data streams, real estate deals involve complex documentation, subjective valuations, and irregular transaction patterns. This creates what industry insiders call the “garbage in, gospel out” problem – companies are feeding poor quality data into sophisticated AI systems and expecting reliable outputs. The result is predictable: impressive demos that fail in production environments.
The Talent and Culture Gap
The 95% failure rate reflects a deeper talent and cultural mismatch in the industry. Traditional real estate investing expertise doesn’t naturally translate to AI implementation leadership. I’ve seen this pattern repeatedly: companies hire data scientists without understanding how to integrate them into existing decision-making processes, or they expect traditional real estate professionals to suddenly become technology experts. The most successful organizations are creating hybrid roles that bridge these domains, but these positions are scarce and expensive to fill.
Investment Implications Beyond the Hype
For private equity funds and family offices investing in proptech, these findings should trigger a reassessment of due diligence processes. The market is flooded with AI solutions claiming to transform commercial real estate, but the JLL data suggests most implementations are struggling to deliver value. Smart investors are now looking beyond the technology itself to examine implementation capabilities, data readiness, and organizational change management. Companies like JLL and other major brokerages that can successfully navigate this transition will capture significant market share from slower-moving competitors.
A Realistic Path Forward
The 5% success rate isn’t necessarily a failure indicator – it’s a reflection of the early adoption phase. What separates the successful 5% isn’t better technology, but better change management. These organizations are focusing on specific, high-value use cases rather than spreading resources across multiple experiments. They’re investing in data quality before AI implementation, and they’re creating cross-functional teams that include both technology and real estate expertise. The companies that succeed in the next 12-18 months will be those that treat AI as a business transformation initiative rather than a technology project.
