Do you remember “The Brady Bunch” two-part Hawaii special? While this month’s column has almost nothing to do with it, sometimes good things come in twos (or more) —just like my thoughts on artificial intelligence. And thankfully, unlike the Brady Bunch’s vacation, AI doesn’t feature an enchanted tiki that brings bad luck.
This month, we’ll focus on establishing context and setting a baseline of what commercial real estate owners, operators and investors need to know about AI. In short, you don’t need to know everything about it, but you should at least understand how important AI is for our industry.
For one, 45 percent of commercial real estate executives plan to increase their investments in AI and other technologies in the next 12 months. A wave of change is coming, and AI is soon going to cease being a competitive advantage and quickly become a must-have capability. Second, AI’s potential-use cases are so valuable that I think we’ll see them proliferate and advance our industry in meaningful ways in the next decade.
The good news is we don’t need to understand how AI works (we can leave that to the tech specialists), we just need to understand how to apply it. In other words, we just need to spend a little time understanding some examples of AI in practice from other industries, pick the ones that could translate well to commercial real estate, and be patient with our technology and data teams as they bring those visions to reality.
On that point, the job for technologists is going to be especially tough. Not because the technology and capabilities haven’t been proven elsewhere, but because commercial real estate data isn’t what it needs to be. As someone else put it, “data silos have become the scourge of the 21st century,” and indeed, the scourge of real estate, which is still largely paper-based. As an industry, it’s urgent that we address our data capabilities to help smooth the path forward for AI.
Do we agree yet that AI is an important innovation for real estate? Good. Next, let’s discuss the issue I see with our collective understanding of what AI is to ensure we’re on the same page.
A range of words might come to mind when you think of AI: Science fiction, scary, confusing, threatening, promising, exciting or maybe something else. In reality, AI is fairly simple—machines (electronic, mechanical or a combination of the two) that demonstrate the ability to mimic human intelligence and pattern recognition by “thinking,” learning, reasoning, problem-solving or all of these things combined. Common forms of AI include machine learning, robotic process automation, natural language processing, deep learning, speech recognition, bots and more.
The interesting thing about AI and real estate is that the industry doesn’t need as many AI or data scientists as you might think—although they are important! It needs more people who deeply understand commercial real estate, what AI can do for it and how data science can help.
In combining the functional understanding of these topics, we can make progress on applying them and take advantage of AI to better operate an individual asset or entire portfolios, decide where to invest or sell, help people be more efficient by doing more valuable work, and, ultimately, transition the industry into the current decade.
We all need to be smarter about these concepts and how they will impact our industry, so next month I will discuss how these topics can and are already being applied to commercial real estate use cases. Make no mistake, if you’re still in real estate in five years, you will know more about AI—and data science—at that point than you will have learned today from my columns thanks to the momentum building around it. But to get there, we must all continue learning together.
John D’Angelo is a managing director with Deloitte Consulting and leads the real estate industry sector for Deloitte Consulting in the U.S. With over 33 years of experience as a management consultant to the global real estate industry, John has helped some of the biggest names in real estate leverage technology and use data to optimize and transform their operations.