Using data, AI, and cloud to transform real estate

Many industries have reached an inflection point with hybrid and remote work, emerging advanced technologies like AI and cloud computing, and increased demands for sustainable frameworks to mitigate emissions. According to Sandeep Davé, chief digital and technology officer at global firm CBRE, the commercial real estate industry is no stranger to these changes and challenges. Delivering the best outcomes and optimizing operations means forging clear digital strategies for business transformation that are focused on the root of client and business problems.

“Through our ethos, we don’t chase the shiny object, whether it’s AI or any other technology,” says Davé. “We aren’t saying, well, what can I do for the purposes of doing AI, but what is the business problem that I’m trying to solve?”

While developing a foundational strategy for transformation that is based on enabling the core business is key, advanced technologies like AI and machine learning are powerful tools that can unlock efficiencies across the entire real estate lifecycle, Davé says. AI/ML are incredibly powerful tools to become data-driven from analytics tools that can predict asset failures and market movements to infusing efficiencies across operations.

“There are new and different ways in which real estate is viewed, transacted, managed, and all of that gets enabled through data and technology,” says Davé.

Beyond operational improvements, advanced and smart technologies can also help reduce emissions. According to a 2019 International Energy Agency global status report, the real estate industry contributed 39% of global carbon emissions attributed to both construction and the life cycle of the asset. As a result, sustainability initiatives have become a priority for a firm of the scale of CBRE, says Davé.

“At the time of managing the building, there are many solutions that offer instant gratification, stick sensors up, light up a building, and they all work well if all you need to do is to light up a building. But in order to meet the scale and the global net-zero targets that our clients have set, our solutions need to be at portfolio scale and need to be multidimensional.”

Becoming data-driven remains imperative for any organization looking to keep up with the varied and changing needs of clients adapting to changes in the market and technology landscape.

“The industry finds itself in the midst of two of the most dominant trends of our time, from return to work to sustainability,” says Davé. “We’ve seen a step change in technology in terms of what cloud and AI gives us, and all of that, I think, is going to also drive tremendous change. And we’ll continue to push the bounds of technology in the service of our clients’ real-world challenges.”

This episode of Business Lab is produced in partnership with Infosys Cobalt.

Full transcript

Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.

Our topic is digital transformation and how clear strategy and emerging technologies such as cloud computing and AI can help transform industries including commercial real estate and propel adoption of sustainability goals.

Two words for you: inspiring transformation.

My guest today is Sandeep Davé, the chief digital and technology officer at CBRE.

This podcast is produced in partnership with Infosys Cobalt.

Welcome, Sandeep.

Sandeep Davé: Thank you. Thank you for having me on the show.

Laurel: Could you talk a bit more about your role at CBRE and what it means to specialize in digital strategy and business transformation at the world’s largest commercial real estate company?

Sandeep: Sure. And for your audience, maybe perhaps I can give a little bit of a context around CBRE and what we do. So as you said, CBRE is the world’s largest commercial real estate services, brokerage, and investment firm. And essentially in that capacity, we help our clients across the entire lifecycle of real estate from investing in an asset to leasing space, to designing it, to building it out, and managing it on an ongoing basis.

We are a global company, Fortune 130, operating in over 100-plus countries. We process over 450 billion in transaction volume. We’ve managed over 7 billion square feet, so it’s a sprawling operation. And in my role as chief digital and technology officer, I oversee all aspects of technology for the company, from digital strategy, tech enablement for each one of our business lines, our venture investments and partnerships, and our traditional technology infrastructure as well.

Now, with respect to your question around what does it mean to drive digital strategy or business transformation in commercial real estate. Like every industry, technology is impacting commercial real estate. It’s a moment of tremendous change. Real estate decisions are getting microsegmented. There are new and different ways in which real estate is viewed, transacted, managed, and all of that gets enabled through data and technology. And I work with the leaders in the company to navigate these changes.

Laurel: So what does technological transformation then look like for a global firm like CBRE?

Sandeep: Sure. CBRE is a 100-plus year old company and in an industry that was traditionally slow to adopt new technologies. And so we took the transformation in two parts. And in fact, even before I discuss those two parts, I’d like to just step back and set some context.

I’ve spent time in many industries, commercial real estate, financial services, and I have seen two models of digital transformation emerge. There’s no model that’s right or wrong. But in one model, a company goes out there and declares themselves to be a technology company and the decisions that they take are aligned with them becoming a technology company. They set up software P&Ls, they set up their own venture investment capabilities, and the pursuit is towards those goals. Whereas we are very clear about who we are, which is that we are the largest and best commercial real estate services company, investment brokerage company in the world. But we also realize that data, insights, technology is going to be critical to deliver the best outcomes for our clients. So in that context, our transformation is based on enabling the core business.

So for that purpose, we had to do two things. The first was to build a foundation. First few years we were focused on building a global talent pool that I’m very proud of. We’ve migrated 100% to the cloud, agile, and over the years we’ve built an enterprise data platform that is meaningful to our transformation. With that foundation in place, we focused on the second part of our transformation, which is to have a clear digital strategy for each line of businesses and therefore focusing on the most pressing problems for our clients. Finally, given the breadth and scale of our operations, we sit on tremendous amounts of data and we’ve made meaningful strides in creating a data advantage for the company.

Laurel: Enterprises are always looking for an advantage, especially with data. Could you outline what that does mean for CBRE, like opportunities and benefits working with all of this data?

Sandeep: Sure. Very interesting space and I spent a lot of time in consumer financial services in other consumer industries, and the one difference that was stark to me as I started working in commercial real estate is that this industry was not facing a big data problem, at least not then when each of the buildings were not centralized, but it was more of a siloed data problem. So by no means am I trivializing the data challenges that I faced in financial services. There were many, but the one thing that I had a benefit of was transaction after transaction of structured data that allowed me to slice and dice and understand my customer’s behaviors. Whereas, what happens in the commercial real estate industry is that data is very siloed, but yet anchored around a property. And even the definition of the property is different depending on who you ask. Whether you are assessing or valuing the entire building versus I’m trying to lease one floor in that building, what I call property changes.

So what we had to do first and foremost was to break down those barriers and we created an enterprise data platform that, against a standard taxonomy, ingests data from 300-plus different data sources and now manages billions of data points. Having sat on that foundation, we now have the ability to generate tremendous insights for our clients. So we are able to, our 350-plus clients, provide them a range of insights at a portfolio scale to a single building level, operational insights, financial insights, occupancy, energy, health and safety risks, so on and so forth. And that same foundation now allows us to unlock efficiencies at a larger scale and apply ML models to generate deeper insights and drive greater transformation.

Laurel: So from the automotive industry to healthcare to corporate banking, AI has emerged as a powerful tool across supply chains and industries, and it’s changing how enterprises operate and what services they can offer to clients. So in real estate and asset management, what does AI look like and what decisions, especially in the C-suite, are made using this technology to drive better business outcomes?

Sandeep: Your question is spot on, which is that how do we use the technology to drive data business outcomes? And through our ethos, we don’t chase the shiny object, whether it’s AI or any other technology. We aren’t saying, well, what can I do for the purposes of doing AI, but what is the business problem that I’m trying to solve? And we have actually been focused on AI/ML, even before the public awareness soared post the release of ChatGPT.

But we look at this in the spectrum of unlocking efficiencies to enable differentiation and across the entire lifecycle of real estate, there is a play in both of those–unlocking efficiencies and enabling differentiation. The way the decisions around how property investments are made, how market movements are tracked, are very data-driven now. The decisions around how space is managed is very data-driven now.  

Laurel: So could you give an example of how AI and cloud computing could be used to build smart buildings, structures, and technology capabilities?

Sandeep: Sure. Using an example is great because this is such a wide field, both commercial real estate and the application of AI/ML in commercial real estate. In the area of smart buildings, we are focused on enabling three outcomes for our clients: energy, efficiency, and experience; which is how do they manage their energy usage, how do they get more efficient in everything that they do with respect to managing a property? And then what is the workplace experience for the employees in a building?

And let me just take an example of efficiency. There was a certain way in which buildings were managed previously. And with the application of cloud native global technology solutions, that we have that are infused with AI/ML, we are now able to manage facilities in a smarter manner, what we call Smart FM. We are able to look at occupancy and dynamically clean the environment rather than having people cleaning the environment on a regular schedule, we are able to save our clients a lot of money with respect to dynamic cleaning. We are able to detect anomalies in how we manage buildings and assets, which can then further reduce the false alarms and the number of truck rolls that need to happen with respect to managing a building. So there are so many different ways in which we infuse AI/ML.

Laurel: That’s really interesting. So according to a 2019 International Energy Agency global status report, the real estate industry contributed 39% of global carbon emissions. Could you offer us an example of how smart technologies, like what you’re talking about now, could boost operational efficiencies and then also help reduce emissions and improve sustainability?

Sandeep: Yeah, absolutely. I think there are two ways in which we look at this space. As you indicated that 39% of carbon emissions are contributed by real estate, and so therefore the industry has a huge role to play. Part of those emissions are at the time of construction itself, and the remainder is for the life cycle of the asset. Right at the time of construction, we’ve built capabilities where we are able to design and redesign based on a certain energy emission target for a building. We are able to select our suppliers based on a certain energy emission target for the building.

And then at the time of managing the building, there are many solutions that offer instant gratification, stick sensors up, light up a building, and they all work well if all you need to do is to light up a building. But in order to meet the scale and the global net-zero targets that our clients have set, our solutions need to be at portfolio scale and need to be multidimensional.

And so therefore what we do is we have the ability to ingest data from various different sources, from sensors, and are able to harmonize that and land it against a standard taxonomy. And then we are able to assess that in many different ways. We are able to bring together different aspects of looking at energy and looking at occupancy and managing the building based on the occupancy in the building. Those interventions, for example, at one of our clients recently, meant we were able to stand up those interventions at 25-plus buildings. And that led to a reduction in peak usage energy for them and also reduction in reactive maintenance work orders, reducing truck rolls, and supporting their energy goals.

Laurel: So you also are talking about this on a portfolio level. And CBRE’s own corporate responsibility and environmental social and governance or ESG goals are as follows: scale to a low-carbon future, create opportunities for employees to thrive through diversity, equity, inclusion initiatives and to build trust through integrity. How is CBRE using emerging technologies like artificial intelligence and machine learning to then become more efficient and also meet those ESG goals?

Sandeep: I think a lot of the ESG problem is a data problem. Today, if you talk to most who are trying and most are grappling with this problem right now, what they’ll say is that do they have a clear line of sight of what their, for example, scope 1 and scope 2, scope 3 emissions are? Are they able to capture the data in a reliable manner, audit it in a reliable manner, and then report against it? While they report against it, can they also manage usage? Because if you are able to look at the data, then you will know where corrective actions are required. Building on the foundation of the data platform that we’ve built on, which is 100% cloud native, by the way, we can then, on top of that, apply these technologies where we can apply ML models to detect anomalies. We take a digital twins perspective to map our data against the buildings and manage the end-to-end lifecycle of that real estate process.

Laurel: So looking into the future Sandeep, which I know is difficult, but how do you anticipate the commercial real estate industry transforming in the next five years as emerging technologies, hybrid ways of working, and calls for greater sustainability initiatives become more prevalent?

Sandeep: There’s always a risk of predictions to a five-year out prediction at a time when the pace of change is dizzying. However, since you’ve asked, maybe I’ll share three points.

The first is, let’s take an example of financial services. The first is related to just insights. If you take the wealth management industry, information used to be an advantage. Now information is a commodity. Insights are an advantage. Insights that were an advantage yesterday are less of an advantage today, but the wealth manager’s job is more important than ever. They’re now closer to the client than ever before and they’re giving more meaningful advice than ever before. We are seeing very similar changes in commercial real estate today, whether that’s investment or leasing decisions, which are getting microsegmented and extremely data-driven. What matters to one client is not going to matter to another client. One client may be most focused on energy goals for their investments. The other client may be focused on labor analytics or gentrification metrics. So how we see the industry moving towards those microsegmented decisions that are data-driven. So that’s one area of transformation that I see in the industry and how deeper data insights are going to be really relevant in terms of enabling that.

Second, if we take any outsourcing industry, which is also part of what we do, whether that’s in BPO [business process outsourcing] or technology outsourcing, they all started with augmentation. You need 10 people. I’m going to give you 10 people. But overtime labor arbitrage moves. You run out of steam quickly and you move to delivering outcomes. And once you have to deliver outcomes, then the model has to be that “I have the best processes, I have the best technology, I have the best data and the best insights, and therefore I am confidently telling you that I’m in a better position to deliver better outcomes for you.” But at the same time, deliver better margins for the company. That’s exactly what we are doing in our outsourcing business, and that’s a transformation that I see happening in the industry to move away from staff org, if you will, to outcomes based.

And third, I think it’s a pivotal moment from many different ways. The industry finds itself in the midst of two of the most dominant trends of our time from return to work to sustainability. We’ve seen a step change in technology in terms of what cloud and AI gives us and all of that I think is going to also drive tremendous change and we’ll continue to push the bounds of technology in the service of our clients’ real-world challenges.

Laurel: That is certainly comprehensive. Sandeep, thank you so much for joining us today on the Business Lab.

Sandeep: Thank you. Thank you for having me.

Laurel: That was Sandeep Davé, the chief digital technology officer at CBRE who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review overlooking the Charles River.

That’s it for this episode of Business Lab. I’m your host, Laurel Ruma. I’m the director of Insights, the custom publishing division at MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can also find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.

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