Why Everyone's Using AI and Almost No One Trusts It With Real Money

Executive Summary: A May 2026 survey from First American Data & Analytics and DealGround found that 66% of commercial real estate professionals now use AI weekly or daily — but only 5% trust it enough to inform an actual deal decision. That 61-point gap between adoption and trust is the real story in CRE technology right now, and it showed up again a few weeks later at Realcomm 2026, where data quality was named the single biggest obstacle to scaling AI. The takeaway for owners, operators, and lenders isn't that AI in CRE has stalled — usage is clearly accelerating. It's that the industry has quietly split AI tools into two tiers: ones you use, and ones you believe. Closing that gap requires solving a data problem, not a smarter-model problem.

The Number Everyone in CRE Should Be Talking About

The "CRE Industry Pulse Check" report, based on a survey of 255 professionals across brokerage, lending and capital markets, development, and asset management, found that AI has fully embedded itself into daily CRE workflows. 66% of respondents use it weekly or daily, with over 42% using it every single day. That's not an experimentation number. That's a habit.

Then comes the other number. Just 5% of those same professionals said they trust AI enough to inform a real deal decision. More than half — 53% — said they use AI strictly for support and deliberately exclude it from final decision-making. Another 17% said they'll use it, but only with heavy manual verification against source documents.

Put plainly: CRE professionals have decided AI is useful for drafting, summarizing, and first-pass analysis, and simultaneously decided it is not to be trusted with underwriting, valuation, or transaction structuring. Those two conclusions are not contradictory. They're rational, given what most AI tools are actually built on.

Realcomm 2026 Said the Quiet Part Out Loud

A few weeks after the First American/DealGround data landed, Realcomm 2026 brought thousands of CRE technology leaders together in San Diego, and the AI conversation there converged on the same conclusion from a different angle. Across sessions, the recurring message was that successful AI initiatives depend on data quality, governance, and disciplined execution — not on which model or vendor you pick. Poor data quality was repeatedly named the biggest obstacle standing between CRE organizations and measurable AI value, regardless of whether the goal was cost reduction, operational efficiency, or new revenue.

That's two independent signals — a 255-person industry survey and a room full of CRE technology executives — landing on the same root cause within a single quarter. When the adoption curve and the trust curve are both moving, but in opposite directions, the problem sitting between them is rarely the AI. It's what the AI was trained or run on.

Why "Use It But Don't Trust It" Is a Data Problem, Not a Model Problem

Here's what the 53%-support-only and 17%-heavy-verification numbers actually describe: a shadow workforce of analysts, lease administrators, and asset managers manually double-checking AI outputs against the original lease, amendment, or estoppel before anyone will sign off on a number. That verification tax doesn't show up in any AI vendor's ROI deck, but it's very real, and it scales with portfolio size.

The reason that tax exists is straightforward. Most AI tools deployed in CRE today are general-purpose document or language models pointed at whatever data happens to be sitting in a data warehouse, a rent roll spreadsheet, or a PM system field that was manually keyed in eighteen months ago and never revisited. The model can be excellent and the output can still be wrong, because the underlying data was never verified against the lease document itself — the actual, executed, legally binding source of truth for every obligation, option, and encumbrance in a portfolio.

This is precisely the gap Prophia was built to close. Rather than treating lease data as something to accept from wherever it currently lives, Prophia's approach pairs purpose-built AI with human validation to extract terms directly from the source lease — then makes that verified data the foundation everything else runs on, whether that's an stacking plan, a portfolio dashboard, or a chat-based AI assistant. Across the 650M+ square feet Prophia represents, that source-document discipline is what turns "AI said so" into "AI said so, and here's the clause it's citing."

Deal Complexity Is Rising Right When Trust Matters Most

The timing here isn't neutral. Industrial leasing volume hit 490.6 million square feet in the first half of 2026, up 27.1% year-over-year, and the standout leases of the year share a defining trait: terms running roughly double the local market average, often paired with above-market rents. Tenants aren't optimizing for flexibility anymore — they're paying a premium for certainty on clear heights, power access, and location, and locking it in for longer.

Longer terms mean more escalations, more renewal options, more co-tenancy and exclusivity clauses compounding over more years before anyone revisits the document. Every one of those terms is a place where a data entry error, a missed amendment, or an unverified AI extraction turns into a real financial miss — the kind that doesn't surface until a rent bump gets missed or an option deadline lapses unnoticed. Prophia's own portfolio reviews have surfaced millions of dollars in lease discrepancies buried in exactly this kind of fragmented, unverified data. Rising deal complexity doesn't lower the bar for data accuracy. It raises it.

What Actually Closes the Gap

The organizations narrowing the trust gap aren't the ones buying a flashier model. They're the ones doing three things in order:

• Fixing the source data first. Before layering AI on top of a portfolio, they audit and verify lease-level data against the executed documents rather than trusting whatever was previously keyed into a system.
• Making AI outputs citable. Every extracted term should trace back to a specific clause and page — not a black-box confidence score. That's what turns "heavy verification" from a mandatory step into a spot-check.
• Treating governance as a feature, not a compliance exercise. Realcomm's own takeaway was that scaling AI responsibly requires visibility into what data feeds which models and who's accountable for accuracy — the same discipline Lease Abstraction work has always required, just now extended to AI pipelines.

None of this is about slowing AI adoption down. It's about giving the 53% of professionals currently keeping AI at arm's length a legitimate reason to move it closer.

Key Takeaways

• A May 2026 survey found 66% of CRE professionals use AI weekly or daily, but only 5% trust it enough to inform real deal decisions — a 61-point trust gap.
• 53% use AI for support only and exclude it from final decisions; 17% use it only with heavy manual verification against source documents.
• Realcomm 2026 independently confirmed the same root cause: data quality, not model capability, is the primary barrier to scaling AI value in CRE.
• Industrial leasing volume is up 27.1% year-over-year in H1 2026, with lease terms running roughly double the historical average — raising the financial stakes of any unverified data error.
• Closing the trust gap requires verifying data against source lease documents first, making AI outputs citable back to specific clauses, and treating data governance as core infrastructure.
• Organizations that fix the data foundation before scaling AI are the ones moving AI from a support tool to an actual decision-making input.

The Trust Gap Is Closing — Just Not the Way Most Vendors Expected

The next eighteen months of AI in CRE won't be won by whoever ships the flashiest copilot. They'll be won by whoever gets a CFO, an asset manager, and a lender to look at an AI-generated number and not feel the need to pull the lease and check it themselves. That requires an unglamorous, document-first discipline that most AI roadmaps skip in favor of shipping features. The firms that get this right — treating verified lease data as infrastructure rather than an afterthought — are the ones whose AI tools will graduate from the 53% "support only" bucket into the 5% that's actually informing decisions. Everyone else will keep paying the verification tax indefinitely. If you want to see what a verified, source-cited data foundation looks like in practice, Prophia's customers are a good place to start, or you can request a demo directly.