Company leaders’ often vague predictions of how AI will be adopted are giving way to more fully formed strategies in the commercial real estate industry, recent earnings calls and other communications show.
“This is precisely the moment when a disciplined, evidence-based point of view [about AI] matters the most,” Cushman & Wakefield CEO Michelle MacKay said Monday on a webinar discussing AI’s impact on real estate.
Here’s a sketch of what these company leaders have said recently about how they’re deploying the technology in their facilities management and other commercial real estate businesses.
Cushman & Wakefield: Navigating stormy seas
The company recently rolled out its AI Impact Barometer, which it calls a first-of-its-kind research tool that will help occupiers and developers understand AI’s effects across sectors and asset classes. The tool has already given Cushman & Wakefield several negative and positive signals, the company says.
“While it doesn’t have all the answers, we do feel … that we have enough information to lay out our current thinking and put forward a few scenarios … when it comes to making important real estate decisions,” Kevin Thorpe, Cushman & Wakefield chief economist, said during the AI’s impact on real estate webinar.
Negative signals include rapidly declining employment in white collar sectors vulnerable to automation, which is rapidly accelerating, and increasing vacancy rates in class B and C offices as automation reduces back-office employment.
“Make no mistake, AI will create winners and losers,” MacKay said during Cushman & Wakefield’s Q4 earnings call.
Like many others in the commercial real estate space, Cushman & Wakefield is focusing on implementing the technology to drive efficiency in operations.
In its assets services business, the company uses AI to generate insights within its proprietary platform and for space planning in its global occupier services business, MacKay said. In leasing, it’s using OneAdvise, which helps automate digital tour books, lease negotiations and benchmarks, she said.
“What that does is really create a very strong data lake for us to work with as we are cross-selling to our clients,” MacKay said.
Other use cases include the tracking of cross-selling opportunities, adjusting employees’ compensation, managing customer relations in its capital markets business and tracking contractual obligations in legal.
“How [is] AI … driving that flow of data and information?” MacKay said. “If you think about de-siloing an organization, it is one thing to do it structurally and organizationally. It is something else to have the data flow freely throughout the organization.”
CBRE: Digging through its data for treasure
Bob Sulentic, CEO of CBRE, said that his company is using AI in two areas: boosting efficiency and developing a knowledge advantage to differentiate its product offerings. The first use case includes the deployment of AI where its economic value clearly exceeds that of traditional efficiency levers, like offshoring. The company has been disciplined about understanding the trade-offs before pursuing efficiency-related AI investments, Sulentic said on the company’s Q4 earnings call.
The second use case has to do with CBRE’s real estate data, which Sulentic says is the most comprehensive of any company in the industry. While in the past the company has not been able to turn that data into a comparably large competitive advantage, it’s using AI to change that, he said.
“We are encouraged, in a balanced way, by both of these AI-related opportunities, with regard to the market-facing risks and opportunities AI introduces to our business,” Sulentic said on the earnings call.
The company thinks about risk in three broad areas, he said: transactional businesses, investment businesses where it creates and improves physical assets, and property and facilities management businesses, where it operates assets on behalf of clients.
The firm’s transactional and investment work is the most protected from AI disruption, he said. He noted that its brokerage business is “enabled, but not anchored to market data,” meaning AI can help its professionals work smarter through better data insights.
This same dynamic occurs in its real estate investment business, where clients engage CBRE to plan and execute complex transactions because of its professionals’ creativity, strategic thinking, negotiating skills, deep base of market knowledge and broad relationships. “None of this seems likely to be replaced by AI in the foreseeable future,” Sulentic said.
The third tranche of CBRE’s business, facilities and property management operations, is more exposed to AI disruption because it removes a lot of the labor that’s required to leverage the large amount of data that’s generated, creating both opportunity and risk, Sulentic said.
“AI can both enable and disintermediate the data and knowledge side of this,” he said. “We believe the scale and complexity of our client relationships is helpful in mitigating this risk. The labor intensive side will not be easy for AI to disintermediate on the market-facing side.”
By the end of 2026, Sulentic believes, there will be concrete evidence that CBRE has made real gains in terms of extracting the data it has, assimilating it and delivering it to its professionals in a way that it hasn’t before.
“That is being enabled by AI and that’s one of the areas we’re most encouraged today,” Sulentic said. “It’s going to save us money in terms of accumulating the data, buying the data, and it’s going to make our brokers more efficient in terms of using the data. We’re also using that same set of tools to meaningfully cut the cost of our research efforts.”
JLL: Disciplined data management gives shelter in rain
Efficiency is the main focus for JLL’s AI strategy, CEO Christian Ulbrich said on an earnings call. He noted that the technology has helped drive strong margin performance over the past two years as the company becomes more productive with its use of AI across its business lines.
“We were able to drive that revenue growth without adding any more head count to the company, and that is a clear outcome from us deploying AI in a successful way,” said Ulbrich, noting he expects that trend to continue.
The company has been using AI to identify opportunities for its brokers and provide its property management teams with tools to “really be on the spot for what the current pricing [is] for certain work and how to complete that work in an optimal way for clients,” he said. “It goes across the board, it’s very much also in our workplace management business.”
To leverage the technology, the company for the last decade has been investing in disruptive startups through JLL Spark, the firm’s global venture fund, and it’s been investing in its data platform, Ulbrich said.
“We have been successfully embedding technology and building proprietary data sets across our core services throughout this time,” he said on JLL’s Q4 earnings call.
“We spent a tremendous amount of money to get our data in order, which is not trivial in an organization which was built country by country over such a long period of time. But we turned the corner on that one, and that is really helping us in a meaningful way to provide our clients with insights which are not easy to get by by people who don't have that data ,” Ulbrich said.
He added that the firm will continue to invest in its own platform and develop new internal tools that its teams can use to better service clients; for the time being, JLL has “no intention to increase our investment into third party prop tech startups,” he said.
“The data is so crucial that we need to do it in-house,” Yao Morin, JLL chief technology officer, said in a 2024 Q&A interview with Facilities Dive. “A lot of the traditional commercial real estate companies develop technologies with the outsource model. We want to build and control the data. We want to protect our data. We want to make sure we build our own secret sauce, so we can stand out from a brokerage perspective and facilities management perspective, and our competitors cannot just copy or license [our approach].”
Ulbrich shared that sentiment on JLL’s earnings call when he highlighted that the company has large amounts of proprietary data that is hard for other companies to gather. “With that proprietary data, we can build tools, which enable our people to drive better outcomes for clients, which then leads to higher revenues per head for our transactional people, but also for other areas of our business,” he said. “The more scaled you are in a business, the better protected you are, because you have data.”
At the same time, the investments in its data platform and in AI are intended to give its professionals tools to offer more value to their clients and not replace what they do.
“The complexity of the commercial real estate asset class, the criticality of real time local market expertise, and fiduciary responsibilities involved create structural barriers [to disintermediating professionals],” he said. “The momentum is very fast on AI, but at the end of the day, there will be human interaction. And that human interaction will be executed by people who have the right data at hand and have the knowledge to deliver that service.”
