Artificial intelligence (AI) is quickly changing how commercial real estate companies evaluate opportunities, analyze data and make investment decisions. But the biggest impact may not be replacing the people doing the work. It may be giving those people the ability to do more of it – and to focus their time on the decisions that matter most.
That was a central theme of the “AI-Driven Strategies for Acquisitions and Underwriting” session at CREDA Conference this week in Denver, where industry leaders discussed how they are putting AI to work across acquisitions, underwriting, asset management and day-to-day operations.
Michael Beckerman, the session moderator and founder of CREtech, noted that technology adoption has historically been slow in commercial real estate. AI, however, is changing the conversation.
“I’ve never seen this level of interest and engagement in our industry over the last two years because of AI,” Beckerman said. “I do believe it is a game changer at some point. We’re not there yet.”
For companies beginning their AI journey, the panelists emphasized that waiting for the technology to mature is not necessarily the best strategy. Instead, companies should begin experimenting, learn what works and build from there.
“Don’t wait,” said Sahil Sagar, CIO and chief AI officer at Graystone. “The learning curve is changing quite drastically.”
Start with the problems you want to solve
For Mark Rasmussen, senior vice president of investments at Link Logistics, Blackstone’s largest portfolio company focused on operating industrial properties, AI adoption starts with identifying practical problems.
Link has launched approximately 50 AI initiatives, ranging from automating routine processes to using AI for more sophisticated analysis. The company groups those efforts into three areas: robotic automation, cognitive engagement and cognitive insights.
Some applications are relatively straightforward – automating monthly reports, processing RFPs or lease abstracting. Others involve using AI to analyze large amounts of data and identify potential strengths or weaknesses among tenants and markets.
“We’re really just empowering everyone in our company to try to experiment and figure out a way to solve their day-to-day problems,” Rasmussen said.
That bottom-up approach is also playing out at UDR, where Dan Campbell, vice president of transactions, said ideas often originate with employees who see an opportunity to improve a process.
UDR has the scale to test an idea at one property, expand it to a market and potentially roll it out across the country. AI is now making it easier for employees to take that process a step further by developing early versions of solutions themselves.
AI Can Dramatically Expand the Deal Funnel
One of the clearest examples discussed during the session came from UDR’s acquisitions team.
The company receives approximately 200 emails and 20 phone calls a day related to potential deals. Before AI, the team had to make decisions about which opportunities warranted the time required for a full underwriting.
Now, AI can conduct a first-pass analysis of a much larger number of potential acquisitions, allowing the team to evaluate opportunities before deciding which ones deserve deeper attention.
“For us, being able to run real evaluations on ten times the number of deals is the KPI that works for us,” Campbell said.
The result isn’t less human involvement. Instead, it puts human expertise later in the process, where it can have greater impact.
Rasmussen described a similar application involving market data. His team was able to assemble information on existing vacancies, how long buildings had been vacant, new construction and planned construction, then turn that information into an interactive tool.
“What we’re seeing is the most transformational tool for us,” he said, is the ability to augment the information analysts already have and use it to evaluate opportunities in new ways.
The project took an afternoon rather than the week Rasmussen had expected, freeing analysts to spend their time underwriting and evaluating other opportunities.
Buy the Intelligence, Build the Context
For organizations thinking about whether to build, buy or partner on AI solutions, Sahil offered a simple framework.
“Now you can buy the intelligence. What you would never buy is the context,” he said.
The major AI models can provide powerful general intelligence, but they do not inherently understand an individual company’s processes, data, history or investment approach. That organizational context can become a competitive advantage.
His recommendation: use commercially available AI models while building and maintaining the company-specific context that makes the technology useful.
That also means paying close attention to data security. Graystone uses AI models within its enterprise environment rather than relying on publicly available tools, keeping sensitive information within its controlled systems.
For all the enthusiasm about AI, the panelists were clear about what the technology cannot replace.
“We still have humans in the loop,” Sahil said. “It has to be an augmented technology for now, not a deterministic technology, especially the generative models.”
That means establishing guardrails, validating data and ensuring that AI-generated conclusions are reviewed before they influence important decisions.
For commercial real estate – an industry built on relationships, judgment, negotiation and finding creative solutions – that may be one of AI’s most important implications.
The technology can make the funnel wider, the analysis faster and the information more accessible. But ultimately, people still have to decide which opportunities matter, which risks are worth taking and how to turn information into a successful deal.
This post is brought to you by JLL, the social media and conference blog sponsor of the CREDA Conference 2026. Learn more about JLL at www.us.jll.com or www.jll.ca.
