AI’s Payoff for Real Estate Investors Comes From What the Model Can See

Every Monday, Blackstone’s investment committee meets to decide what to buy. 

Nearly every deal on the agenda now arrives with two memos. One comes from the deal team. The other comes from a model trained on more than 14 years of the firm’s internal deal memos and debates, which Bloomberg last month called its artificial intelligence chief investment officer. It doesn’t get a vote, and yet it does get a voice.

Apollo’s Torsten Slok has spent the year warning that, outside tech, there are still no signs of AI lifting profit margins, and that data governance could delay the gains for years. Commercial real estate mostly bears him out. In Dealpath’s 2026 survey of 103 investment and technology professionals at institutional commercial real estate firms, 97 percent said AI is integrated into their investment process, but only 51 percent said it saves them time once they account for checking its work.

So the payoff isn’t missing. It’s uneven, and where it shows up tells you more than the averages do.

Fifty-five percent of professionals highly trust AI to summarize a diligence document. Just 35 percent trust it to score and rank deals, the lowest of any task we asked about. The difference is not the model. Every firm can license the same models now and gets the same upgrade on the same day.

The difference is what the model can see. A diligence summary needs one document, which holds everything required. Scoring a deal needs the firm’s buy box; the deals it pursued and passed on, and why; the comps it trusted last time; and what else is trading right now. 

A general-purpose model has seen none of it, so its score is a guess at someone else’s strategy, and the analyst can tell.

Blackstone’s model is trusted in the room for the opposite reason: It has read every argument the firm has had with itself for 14 years. Our survey shows the same pattern. Professionals at firms managing $40 billion or more rate their data the most AI-ready of any group, and extend AI the most trust on judgment work. They invested in their data first. The trust, and the payoff, followed.

Eighty-three percent of professionals rate their firm’s data infrastructure as mostly or fully ready for AI. Ninety percent say data quality or fragmentation has limited AI’s impact at their firm. Both can be true.

Most firms grade their data by the old standard: Is it accurate enough for a person to report from? An analyst knows which spreadsheet holds cap rates as text and which comps sit in a colleague’s inbox. A model knows none of that, so wherever the record is incomplete it guesses, and a person has to check the guess. 

That checking, which 94 percent of respondents do most of the time or always, is a verification tax, and it is where the promised time savings go.

The instinct, when AI underdelivers, is to add another tool. Seventy-one percent of the professionals we surveyed use general purpose assistants like ChatGPT directly. Each new tool sees its own sliver of the firm’s context, so each adds to the time spent checking rather than reducing it.

The firms getting a return are moving the other way: fewer tools, anchored where the deal record already lives, with the market connected to it.

Most firms won’t build their own AI chief investment officer, and they don’t need to. The principle is the same at any scale: The firm’s memory has to exist somewhere a model can read it. Kept together in one trusted place, that memory makes each AI output easier to trust, and it deepens with every deal the team works. It is also the one asset a competitor can’t license.

Every firm using AI pays one of two costs: the recurring cost of checking outputs built on fragmented data, or the one-time cost of building the memory. Budget season is a good time to ask one question of every AI line item: What will it be able to see? 

The firms that answer that well will spend the next decade putting AI to work on underwriting and investment committee decisions. The others will still be verifying its work.

Mike Sroka is co-founder and CEO of institutional real estate investment platform Dealpath.