Commercial real estate’s technology problem is not simply an industry reluctant to change. For investors, the more consequential issue is that CRE’s complex, property-specific work has historically offered too small a market to attract broad software investment—leaving firms dependent on legacy systems just as generative AI creates an opening to rebuild critical workflows.
Generative AI could give commercial real estate a rare opportunity to modernize some of its most entrenched processes, particularly the data-heavy workflows that shape underwriting, reporting and investment decisions.
AI Opens A New Entry Point
Josh Herrenkohl, a senior managing director at FTI Consulting, says AI’s ability to acquire and manipulate information is helping it find a place in CRE platforms. Its greater potential, however, may be in allowing firms to rethink how work gets done rather than simply layering new capabilities onto aging technology.
“It gives you an opportunity for a blank slate in many respects if you can build each of the steps of the workflow and automate it,” Herrenkohl tells GlobeSt.com.
For investors, that could have practical implications. The industry’s most complex work often still depends on spreadsheets, manual processes and institutional knowledge held by a small number of people.
A Small Market For Big Tech
CRE has long been characterized as a technology laggard, but Herrenkohl says that description misses the economic realities that have constrained innovation. “On a relative basis, CRE is very small relative to the spend and investment in technology,” he says.
Software developers typically pursue sectors with larger customer bases and greater potential returns. CRE, by contrast, has not always offered enough scale to justify the specialized investment required to build tools that meet the industry’s needs.
Herrenkohl notes that there are only a handful of companies ruling the CRE industry, and things haven’t moved swiftly. He thinks the functionality and capability of CRE technology have not evolved much.
Real Estate Is Not A Generic Use Case
Building CRE technology is difficult for reasons that go well beyond the size of the addressable market. Software developers must understand the mechanics of real estate ownership, investment and operations—not just the underlying technology.
Related Post: Real estate tech firms boost efficiency
CRE has similarities to private equity, including on the capital side. But the property layer introduces another level of complexity. Leases, regulatory requirements, reporting obligations, depreciation and investment strategies such as 1031 exchanges all require specialized treatment.
Herrenkohl says that not only do you need the know-how, but you also need to be willing to make the investment.
This combination creates a high barrier to entry. A vendor without deep CRE expertise can struggle to build a useful product. At the same time, a vendor with the necessary knowledge still must decide whether the likely return supports the cost of developing and maintaining a specialized platform.
Legacy Systems Are Hard To Replace
Those constraints help explain why outdated applications have remained so deeply embedded in the industry. Herrenkohl says that you see some really antiquated, poorly architected, don’t-work-well applications for the industry. But they continue to proliferate and be the standard bearers because everyone uses them.
Replacing a core system is difficult even when users recognize its shortcomings. When most of an organization, and much of the industry, relies on a legacy platform, switching can introduce operational risk, retraining costs and potential disruption to established processes.
Herrenkohl notes that even if something came in from the outside and maybe 98% of the industry is working on a legacy platform, it’s a difficult switch.
The immediate value could come from using AI to automate discrete tasks, simplify data handling and make complex processes less dependent on manual work and individual expertise.
For CRE investors, the technology story is therefore less about whether the industry will suddenly become a fast adopter. It is about whether AI can help firms reduce friction in the processes that influence how assets are analyzed, managed and reported, without requiring them to replace the systems they already depend on.