On a recent trip to Toronto, I caught up with Yao Morin, the global CTO of JLL, a commercial real estate company. We discussed JLL's early adoption of generative AI technology, despite the real estate industry's traditionally conservative approach to new technologies. Yao explained how JLL has implemented AI tools like JLLGPT to process vast amounts of unstructured data in multiple languages, making it easier for employees to access and utilize information from contracts, research reports, and other documents.
For Yao, the technical aspects of implementing AI in a large organization included managing the challenges of data access control, the importance of user feedback, and the need for accuracy in specialized tools. She emphasized the significance of building trust in AI systems by providing clear references to data sources and implementing human-in-the-loop verification processes. We also discussed the potential future developments in AI, such as world models that understand causation and physical properties, which could be particularly relevant for facilities management and other real-world applications.
In our discussion, Yao shared her views on the importance of community-driven development and focusing on automating small, repetitive tasks rather than replacing entire job roles. She also explained the unique challenges of working with "wide data" in commercial real estate and the potential for AI to unlock synergies between different business lines by organizing and connecting diverse data sources. Our chat concluded with Yao's vision for the future of commercial real estate technology and her focus on solving scalable data collection and integration problems.
Key insights
1. Early adoption of generative AI can provide a competitive advantage, even in traditionally conservative industries.
2. Building trust in AI systems through transparency and human-in-the-loop processes is crucial for successful implementation.
3. Focusing on automating small, repetitive tasks can lead to significant productivity gains without threatening job security.
4. Community-driven development and user feedback are essential for driving AI adoption within an organization.
5. Solving data collection, cleansing, and integration challenges is key to unlocking the full potential of AI in data-rich industries.