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ommercial‑real‑estate owners are reluctant to embrace artificial intelligence, a hesitation rooted in persistent concerns about accuracy and trust. Proptech founders and investors, who admit that keeping up with AI’s rapid evolution is a daunting task, argue that owners lag because they struggle to pinpoint which AI solutions truly meet their most pressing needs.
Prasan Kale, co‑founder and CEO of Outcome, explains that AI is a methodology rather than a single product. “The industry has heard the word AI for the better part of the last three years,” Kale says. “It’s everywhere and it’s a big word. It’s not one thing. It’s many, many different things. It’s a methodology more than a product. But what’s happened in the hype cycle of AI is that owners felt, ‘Oh! There’s this magic wand called AI that can come in and solve the problem for me.’ That can’t be further from the truth.”
Some proptech firms attempted to “wrap” ChatGPT and sell it to owners, but those pilots failed because they relied on generalist AI rather than CRE‑specific, end‑to‑end workflow solutions. Kale, along with his Outcome co‑founder and CTO Sid Jain, previously launched Rise Buildings (later acquired by VTS) before bringing Outcome out of stealth mode last week.
Aaron Ru, principal at RET Ventures—a venture fund backed by more than 50 institutional real‑estate owners—highlights data ownership and centralization as critical themes. “How do you set an institutional data strategy to own more of your data?” Ru asks. “The downstream effects of owning your data is that it allows you to train your internal AI systems and leverage data points you’re collecting across multiple systems such as property management system, internet listing service, email, chat, etc.” He adds that consolidating leasing, renewals, maintenance and back‑office functions is essential for AI implementation.
Ru acknowledges that accuracy and trust remain major hurdles. “Many of our portfolio companies that have successfully deployed AI in a proptech environment have done so with co‑pilot‑type deployments, where they work in conjunction with humans to help mitigate this issue,” he says. He cites Lula, a portfolio company that uses a co‑pilot to streamline responses to customer inquiries during maintenance, reducing employee time per interaction and enabling the company to commercialize the solution for external clients.
L.D. Salmanson, co‑founder and CEO of Cherre, stresses that AI must be embedded in owners’ workflows and meet security and compliance standards. “The first thing is, does this do something valuable for the most part?” Salmanson asks. “The answer is yes in theory, but not actually. It’s cool only if this is in a big process, embedded in my workflow, and passes security and compliance. As a stand‑alone, I don’t need it. But if it can actually automate the monthly close, that’s value. I want that, if it is SOC 1 accounting compliant, and SOC 2 security compliant.”
Salmanson notes that only a few CRE owners can distinguish good proptech AI providers from the rest. “I wouldn’t even start talking to the company until I know that they’re at the level of enterprise maturity that can work with me,” he says. “I’m not even piloting, because even if I pass that gate, I’m never going to be able to deploy this in my organization. So it’s not even worth the conversation.” He emphasizes that owners intuitively know which processes are worth automating—those that cost millions or involve critical monthly close activities.
The next question for providers is whether a process can be credibly automated. “If an organization is asked how much judgment is being applied today by people in that process, if it’s very little judgment, it’s a very good candidate to be automated,” Salmanson adds. “If that process has one spike in judgment, that’s a good candidate for a human‑in‑the‑loop process. If there are multiple such spikes, it’s probably not the best candidate.”
Andrew Thompson, CTO of Orbital, a London‑based company that uses advanced OCR to structure and analyze complex real‑estate documents, argues that AI should be problem‑first, not technology‑first. “Don’t be a technology searching for a problem,” he says. “What are your business challenges? What are the competitive pressures at play? What are your clients asking you about? And then match that to AI.” Thompson notes that digitizing legal documents accurately requires specialized technical and legal knowledge. “We’re talking about documents that are sometimes tens or hundreds of years old,” he explains. “They’re poorly photocopied, and so our customers will have these documents that are sometimes unreadable with the naked eye. Before you even get to the magic of AI, you have to get the data into a format. And, in real estate, you have the complexity of these documents that you can often have a lease with 10 amendments amending the lease over multiple years. So there’s a relationship between all those documents, and just throwing a bunch of PDFs into a generic AI tool doesn’t cut the mustard.” Orbital’s platform is now used by lawyers, title insurers and other real‑estate professionals.
Asaf Raz, vice president of marketing at Agora, a Tel Aviv‑based investment‑management platform that uses existing AI engines to streamline processes like membership agreements and tax document analysis, stresses data quality over model quality. “The first issue for most of our customers, and also for us internally as a tech company, is that you can have the smartest model in the world, but have poor data,” Raz says. “That’s a big issue that we see on it all the time, and then the data becomes useless.” He also warns that AI adoption should focus on clear before‑and‑after workflows. “If you have a workflow to focus on, make it clear before and after its use,” Raz advises. “A lot of people don’t do that, and then they get tied up in everything that happens with AI, and it just doesn’t give them what they want.”
Grant Drzyzga, founder and CEO of Revela, a Detroit‑based property‑management and accounting platform, points out that owners need diverse AI models to address various operational and analytical tasks. “I think that everybody’s far behind the curve, quite frankly, us included,” Drzyzga says. “You have to bring in a very specific business problem to AI to solve it, because the things that these models can do, or the things that you can actually train your own home‑grown models to do, are limitless.” He emphasizes that legacy systems and fragmented software hinder AI. “The key to any AI implementation in a business is that the standard database is normalized and already relational, and is ready to have something analyze it ruthlessly, efficiently and quickly,” he says. “Getting to that point is the hardest part, which is a little bit strange to think about. It’s almost like you have to dig the foundation of the building, and that takes a really long time before you start building upwards. But, as soon as you have that foundation dug, you can build sky high with a lot of these tools out of the box. You just have to have the right data infrastructure to do it, or it will be siloed, and it’ll give you the wrong results if you’re not feeding it all the context that it needs to be successful.”
While operators are curious about AI, they remain skeptical due to high costs and the need for proof points. Larger operators are investing in data and AI teams, even as they face challenges building their own software.
David Weissman, managing partner at Greek Real Estate Partners, advises owners to demand measurable outcomes and proof of integration with existing systems. “You must ask for measurable outcomes—reduced downtime, faster response times, or cost savings—and proof of integration with systems like Yardi or MRI Software. Avoid platforms that rely on buzzwords instead of performance metrics,” he writes. “In the short term, what’s needed is predictive maintenance and smarter workflows, while in the long term, portfolio‑wide intelligence connecting energy, labor and logistics for true operational foresight.”
