Dancor Construction has developed more than 100 industrial projects over more than two decades, but it wasn’t until its work pipeline recently began to run dry that founder and owner Sean Ford wondered how else to look for business. He turned to artificial intelligence.
Ford said his Brampton, Ontario-based company had missed out on winning business in its own city. Dancor has since explored various data-driven commercial real estate platforms and even partnered with developer KED Ltd., short for Kingsway Employment District, several months ago to work on a business park in Sudbury because of KED’s ability to leverage AI to develop projects.
KED’s founder, Dario Zulich, said in an interview that when he gets an inquiry from a potential customer, “I use AI to put together a custom-made package for them quickly.” He asks prospective clients “questions like how high do you need your clear heights or column spacings, and then I pop the questions and answers into the software and then get rapid estimates that I can in turn send to my prospects.”
As more businesses talk about the need to adopt AI in their operations, practical examples are emerging that show current use. Some large commercial real estate firms are integrating technology into making proposals, while others are creating investment strategies. Property companies also say they use AI to identify properties likely to hit the sales market.
The use cases are important because quick integration of the fast-changing technology has not been easy for a sizable portion of businesses. While real estate services firm JLL reports that 88% of investors, owners and landlords have started piloting AI programs, 27% of respondents to Deloitte’s 2026 Commercial Real Estate Outlook survey reported challenges with AI implementation, “including technical issues, lack of expertise, or resistance to change.”
One firm reporting some success with AI is institutional investment giant BGO, based in Miami Beach, Florida, and owned by Toronto-based financial services giant Sun Life. It's using a proprietary AI-powered algorithm to assess how real estate assets likely will perform in about 2,500 U.S. areas. Co-CEO John Carrafiell said the AI algorithm helped calibrate BGO’s apartment investment strategy in the Miami area.
“Our analytics highlighted second-tier submarkets outside of Miami as poised to surge,” Carrafiell said in a recent market commentary shared with CoStar. He added that BGO divested from a $1 billion industrial portfolio in Southern California’s Inland Empire — despite having a population of about 4.5 million and two decades of robust local conditions — because the algorithm signaled that returns were about to decelerate.
“Getting out ahead of that — before others saw it — was key,” he said. “Just like being early into a market creates value, being early out can protect performance.”
But not all firms believe AI is the solution to all industry challenges. Some executives say that as more companies use AI, the competitive advantage disappears, while others say that AI doesn’t fit all uses and that market experience can do as well or better.
Monitoring market changes
Even so, Hazelview Investments, a Toronto-based firm that manages investments, developments and properties across North America, Europe and Asia, uses AI models to pick up on real-time market signals to blend with the firm’s data on rents, vacancy, demand, resident feedback and customer satisfaction scores.
“When we evaluate an acquisition, we use our AI models to score each opportunity against assets we’ve actually owned and operated,” said Strachan Jarvis, co-head of private real estate investments at Hazelview, in an email. “That carries straight through into underwriting, where our assumptions are benchmarked against real outcomes, not market averages.”
Bryn Feller, a senior vice president and managing director in brokerage Northmarq’s Chicago office, said the combination of generative and predictive AI is “a game changer” for commercial real estate because together they significantly eliminate decision-making errors. Generative AI determines patterns to create content, while predictive AI analyzes historical data to identify patterns and probability.
Because AI has recalibrated how professionals interact with sets of data, whether their own or third-party information, Feller expects even more change in the next 12 months as the use of AI becomes increasingly commonplace throughout the industry.
“It’s shifting people’s decision-making, matrices and processes with regards to real estate decisions. AI went from a buzzword for a lot of organizations to being front and center,” Feller said in an interview. “They’re moving from checkers to chess and moving into this 4-D multi-timeframe kind of calculus that humans just can’t do to this degree of analysis.”
Artificial intelligence is also being leveraged to underwrite properties, Feller said, highlighting the importance of tenancy renewals in investors’ long-term projections. Feller said predictive AI in particular can assist them by presenting lease-renewal probabilities for any given tenant.
“We’re working a little bit with a platform called CFS,” Feller said, referring to a predictive analytics platform designed to convert available data into actionable leasing opportunities.
Feller added that “the number for the renewal probability is no longer just a guesstimate or the average investor’s own perception, but rather correlated data points, and this has massive implications for underwriting.”
Head start on listings
Former mortgage broker and private lending underwriter Laura Scarlett Martin, founder of technology consultant Digital Fusion Consulting, said it’s past the eleventh hour for brokerages to broaden their business practices. While client retention is paramount, she said, solely relying on repeat customers is an obsolete business model.
Brokers should harness data and information gathered from AI to identify properties that are about to hit the market.
“This technology can flag these things for you automatically and explain how and why things are undervalued by providing, for example, macro indicators and geospatial data that point to an asset’s value on the verge of popping off,” she said in an interview.
She added that “by getting really, really specific about your criteria and using a data platform, plus AI-enabled tools that map out oversupplied and constrained submarkets, you can feed your pipeline of on- and off-market candidates. There are some platforms that auto-search on- and off-market deals that match your criteria and aggregated comparisons and market data for you.”
Back on the development side, Cityscrape, an AI-powered platform designed to tracks changes to planning codes and policies, can help builders plan ahead and accordingly, said Noah Shechtman, a Cityscrape co-founder and director of development at Brightstone, a developer of low-rise housing in the Toronto region, in an interview.
He said the platform aggregates data in real time by analyzing municipal and regional sources, whether it’s scanning zoning bylaws, or government decisions or updates.
“If I know there’s a change when it’s first proposed, I can pivot elements of my project six months or a year before it’s implemented, whereas having to react to change always causes project delays,” he said.