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AI and CRE: It All Starts with Data
A popular buzzword in commercial real estate is technology. Specifically artificial intelligence. To read articles on the topic, AI is being touted as “transformational,” “disruption,” and “strategic.” However, according to a recent article from CBRE, AI can’t really do much without “the robust foundations of data.” In other words, no matter how advanced an AI system is, it won’t be helpful for predictive modeling or decision-making with crummy data.
The article explained that there are quality standards the data should meet. Specifically:
- Accuracy. If an AI model is fed incorrect data, the outcomes will also be incorrect. The article pointed out inaccurate property valuations fed into an AI system would mean imprecise results.
- Representative. Quality data should be up-to-date and directed toward “the problem you’re trying to solve,” the article said. At the same time, that data should be diverse—“detailed and broad enough to capture the nuances of the problem.”
- Management. This calls for effective data management practices, like storage, cleaning, normalization and integration.
The article went on to say that data makes up one of four key foundations when relying on AI. The other three factors include:
Infrastructure
In other words, hardware that packs a punch, combined with scalable cloud solutions. This requires an upfront investment. The right infrastructure in place means that AI models can be trained efficiently and deployed at scale, with the necessary support for data handling, where data can be stored, processed, and accessed efficiently,” the article said.
Integration
Successful AI requires “seamless integration with existing systems and workflows across an organization,” the article said. The goal is to boost current processes and networks rather than disrupt them. Said the article: “This is true from a technology perspective, where AI systems can leverage data from various sources, and from a human perspective, as effective integration aids in incorporating AI into a colleague’s day-to-day role.”
Flexibility
Technology advances rapidly, and AI is no different. Because of this, “the data foundations they are built on must be able to adapt and scale accordingly,” the article said. AI also needs to adapt to changing business needs.
The article noted that additional factors to consider are people, processes and prompting (the “interaction between users and AI tools”). Furthermore, implementing AI should go beyond a one-size-fits-all approach, with strategies supported by “strong communication, formalized processes and human validation. “
