What the industrial real estate acquisitions role looks like when AI handles the production work

Commercial real estate has always been slow to adopt new technology. AI tools for acquisitions teams are changing that, not because the industry suddenly became more progressive, but because the efficiency gap between firms using these tools and those that are not is becoming too large to ignore.

Cole Chernow, Senior Analyst on the acquisitions team at Clear Height Properties, has spent the past three months integrating AI into the daily workflow of industrial real estate acquisitions across markets including Chicago, Indianapolis, Columbus, Cincinnati, Minneapolis, and Dallas/Fort Worth. He has a clear-eyed view of where the tools are useful and where they stop.

What AI has absorbed on the acquisitions side

In the last several months, Claude has taken over deal intake, comp formatting, investment memo first drafts, and pipeline management across 70 to 150 active deals at any given time. Each of those tasks used to sit on an analyst's plate. Each required time that was not going toward underwriting, market analysis, or deal evaluation.

The shift has been significant. But it has also sharpened Chernow's thinking about what is left when the production work is gone.

The part AI cannot touch

Brokers bring deals to firms they trust to close. Owners return calls to people they know. Lenders commit to operators they have seen perform. Equity partners back teams whose judgment they respect. Every one of those relationships is built over time, through consistent follow-through and direct human interaction. None of it transfers to a language model, and none of it gets faster just because the intake process did.

In industrial real estate acquisitions specifically, the relationship layer is the business. Finding off-market deals, sourcing debt, raising equity, and getting to the closing table, all of it runs through a network of people who have decided to work with you. That is as true in the Chicago industrial market as it is in Indianapolis, Columbus, or Dallas/Fort Worth.

Chernow's read on where the acquisitions role goes in the next five years: the valuation and analytical production work gets automated. What remains is market judgment, big-picture economic thinking, and the relationship infrastructure that makes deals possible.

"The job will switch from more analytically focused to more relationship driven," he said.

The professionals who position themselves there, while staying current on the tools, will have a real structural advantage.

Two tracks at the same time

For Chernow, that means running two priorities in parallel right now. The first is getting very good at AI tools for commercial real estate, not just because it is interesting, but because the efficiency gap between people who use these tools well and those who do not is only going to grow. The second is building and maintaining the broker, lender, and equity relationships that put deals together, because that is the part of the job that compounds over time in ways no software will replicate.

The analysts who figure out both sides of that equation early, the tools and the relationships, are going to be in a strong position. The ones who lean too far in either direction are not.

What this means for the industry

The conversation around AI in commercial real estate tends to focus on disruption: which jobs change, which tasks disappear, which roles get smaller. That framing misses the more useful question, which is where human judgment becomes more valuable as the production work gets automated.

In industrial real estate acquisitions, the answer is clear. Market knowledge, deal instinct, and the relationships that bring opportunities to the door are not automatable. They are the core of the job. AI does not change that, it just removes the parts that were getting in the way.

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