AI Adoption Reshapes CRE Strategies for North American Firms

Practical AI Use Goes Mainstream

It’s no longer just talk—practical AI applications are hitting the ground for commercial property outfits across North America. According to CoStar News, firms including developers, investors, and brokers are embedding artificial intelligence into nearly every stage of their operations, from underwriting deals to hunting for acquisitions. The shift comes as the industry seeks to close gaps in sourcing, analysis, and execution. JLL reports that 88% of CRE investors, owners, and landlords have at least piloted some form of AI, underlining the depth of buy-in.

But the transition isn’t seamless. Deloitte’s 2026 Commercial Real Estate Outlook found that 27% of CRE businesses still report challenges rolling out AI — citing technical complexities, lack of in-house expertise, or resistance to changing established workflows. Yet, as case studies mount, these hurdles are less about whether to use AI and more about how to make it work at scale.

AI Platforms Fuel CRE Pipelines

BGO, an institutional investor with $1B in industrial divestments in the Inland Empire, uses proprietary AI algorithms to determine optimal hold periods across 2,500 US submarkets. In Toronto, Dancor Construction turned to AI-fueled partnerships, notably teaming with KED Ltd. for business park development after traditional deal flow dried up. KED’s founder, Dario Zulich, said AI enables him to rapidly generate client proposals tailored to precise requirements—think clear heights and column spacing—driving conversion speed.

Hazelview Investments, meanwhile, deploys AI models to triangulate new acquisitions with internal data ranging from market rents to resident satisfaction scores, feeding results directly into their underwriting process. According to Strachan Jarvis, Hazelview’s co-head of private real estate investments, this approach benchmarks new opportunities against actual performance, replacing market averages with empirical outcomes.

Predictive and Generative AI Accelerate Underwriting

Brokers aren’t missing out on the AI wave. Bryn Feller of Northmarq’s Chicago office highlighted the power of combining predictive and generative AI. Predictive tools, like the CFS platform Northmarq uses, offer statistically driven renewal probabilities for tenants, moving beyond gut-feel estimates toward data-backed underwriting. Feller notes that this capability is pushing the industry from simple heuristics to “multi-timeframe” analysis that far outpaces traditional human methods. The same data appetite is reshaping office demand patterns, especially in fast-growing tech hubs where AI firms are expanding footprints.

On the prospecting side, brokers are leveraging AI-driven platforms to identify properties likely to come to market. Technology consultant Laura Scarlett Martin argues that relying solely on repeat business is obsolete. Instead, AI flags assets poised for sale based on macro trends and geospatial indicators, auto-feeding pipelines with both on- and off-market deals, sometimes using criteria far more granular than conventional search platforms can handle.

Why It Matters

The competitive landscape in CRE is now defined by who can leverage AI most effectively. BGO’s early exit from the Inland Empire—prompted by AI analysis ahead of broader market awareness—illustrates the advantage of acting before macro trends are visible to rivals. As co-CEO John Carrafiell put it, timing exits early protects performance just as much as entering surging markets does.

For underwriting, using AI-generated renewal probabilities and market signals directly impacts pricing and risk management. Per Deloitte’s 2026 survey, 73% of CRE firms see AI as crucial for advanced analytics or market signal detection. Hazelview’s system of benchmarking against real asset performance, rather than market averages, also exemplifies the pursuit of alpha through more granular due diligence. And on the development front, platforms like Cityscrape, which aggregates zoning and regulatory changes in real time, let developers like Brightstone’s Noah Shechtman pivot project plans months ahead—reducing delay risk in an industry where time lost is value lost.

The upshot: while some argue that broad AI adoption will commoditize competitive advantage, the clear trend is toward AI becoming the baseline competency for CRE professionals, not just a differentiator for early adopters.

What’s Next

With rapid improvements in both generative and predictive AI, Northmarq’s Feller expects the next 12 months to bring even more industry change. Growing sophistication in underwriting—driven by platforms like CFS—will push decision-making beyond basic market averages toward individualized risk analysis for each asset and submarket. As AI models get better at harvesting and interpreting granular data (like tenant behavior or policy shifts), even mid-sized and small firms will face pressure to integrate tech—either to keep pace or risk being left behind.

In development, more platforms will aggregate live municipal and policy data, making project feasibility assessments an ongoing process rather than a one-time deal screen. CRE pros who resist the shift are likely to find themselves outflanked as algorithms, not instincts, start making the better calls on both sides of the transaction table.