How Real Estate Lawyers Are Adopting AI: Insights from ABA RPTE

The Orbital team was in Chicago recently for the American Bar Association’s Real Property, Trust and Estate section conference: two days of CLEs, hallway conversations (plus, the occasional deep-dish pizza side quest) with a few hundred attorneys who specialize in real estate, wealth, and family legacy work. RPTE has a reputation as one of the more substantive events on the legal calendar, and it lives up to it. The event is small enough that you actually get to know people, focused enough to draw real specialists, and collaborative in a way the bigger conferences rarely manage to pull off.

At Orbital, we build purpose-built AI for real estate legal practice, and while our customers include some of the largest real estate law firms in the country, attending industry events like RPTE is one of the genuine privileges of our work. As former real estate attorneys ourselves, we get to listen to practitioners talk through what they’re seeing, share what we’re seeing back, and learn from people who are thinking about these same problems from a different angle. The best conversations we have all year tend to be the ones that happen at conferences like this one, and RPTE was no exception.

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The first dedicated AI session in the conference’s history

This was the first year ABA RPTE included dedicated AI programming on the agenda, and the turnout reflected it. Attendance was strong, and the questions during the Q&A were unusually concrete. Which tools? Which workflows? Which ethical guardrails? Eighteen months ago, AI discussions at legal conferences were full of abstract worry about whether the technology could replace lawyers, or how to avoid implementing it altogether. In Chicago, the conversation has clearly moved on.

One simple question from the speaker set the tone for the rest of the presentation: how many of you are using AI in your day-to-day practice today? Out of roughly a hundred attorneys in the room, five hands went up. Five.

The number is striking, but the room’s reaction is the part worth lingering on. There was curiosity in the room, not skepticism. The other ninety-five hands belonged to people who were leaning forward, taking notes, and clearly thinking about how to close the gap. The mood was a mix of genuine excitement about what AI could do for the practice of real estate law and a sharp awareness that the law firms moving first are going to set the pace. We’ve been seeing the same dynamic in our own customer conversations here at Orbital for a while. The will is there. The adoption is what’s catching up.

Could Failing to Use AI Become a Competence Risk for Lawyers?

Then came the line that got the room’s full attention. The speaker’s argument: failing to use AI is now approaching the line of malpractice in transactional work.

The major productivity software programs attorneys already rely on, think Word, Outlook, and Adobe, have had AI quietly built into them for some time. State bars are increasingly taking the position that staying current on relevant technology is part of an attorney's basic duty of competence. Major institutional clients are starting to identify categories of legal work they are no longer willing to pay full rates for, on the grounds that the work could now be done with AI assistance.

Refusing to use AI in 2026 isn’t just hard to defend, it’s beginning to directly conflict with an attorney’s professional obligations.

It was a pointed way to open a session, and it shifted the energy in the room immediately.

Why Senior Partners Are Leading AI Adoption in Real Estate Law

One of the more counterintuitive observations from the conference was about who in the room was leading the AI conversation. It wasn’t the junior associates. It was the partners. The 25- and 30-year practitioners. The people you might assume would be the natural skeptics of a technology that promises to compress hours.

There’s a reason for this, and it has nothing to do with the stereotype of the tech-savvy partner. Senior practitioners have spent their entire careers building leverage, through associates and paralegals and templates and process and institutional knowledge. They are oriented toward a particular question: how do I do more of what only I can do, and less of everything else? When a tool fits that mental model, they recognize it before anyone else, because they’ve been thinking about exactly this problem for thirty years.

The framing that kept returning across the AI sessions was not that AI will replace junior lawyers. It was that AI is the next form of leverage on the table, and that the firms moving early would have a meaningful advantage over the firms that didn’t. ‍

How AI Helps Lawyers Identify Errors in Complex Real Estate Documents

The example that came up more than once was firms running their own form agreements through AI tools, sometimes documents that had been in use for twenty or thirty years, and discovering inconsistencies nobody had ever caught. Sometimes a defined term meant one thing in section two and something slightly different in section eleven. Sometimes a provision on page sixty contradicted a provision on page eighteen. The point being made was that no matter how careful the original drafter was, and no matter how many sets of eyes had been over the document since, there’s a kind of consistency check that humans simply aren’t built to perform on a hundred-page document.

The point isn’t that AI is smarter than lawyers. It’s that AI is tireless in a way humans cannot be. A lawyer reading a hundred-plus-page document on a Friday afternoon will likely not remember on page eighty the exact wording of the provision they read on page twenty, and that isn’t a flaw in the lawyer. It’s a feature of being human. What AI offers in transactional practice is the ability to absorb the kind of full-document, page-by-page consistency work that no human reader was ever going to do well in the first place.

Summarization is the entry point, yet the full opportunity is bigger

A lot of the public conversation about AI in legal starts with summarization. AI to digest a single long lease, pull the key terms out of a hundred-page agreement, or summarize a stack of diligence documents one at a time. That's real, and it's usually where firms get their first felt productivity win. The value shows up immediately, and the use case is intuitive enough that even attorneys who have been skeptical of AI can see the appeal.

But summarization is the entry point, not the ceiling. One of the most useful framings we heard at the conference came from a speaker citing a workplace study that found the typical knowledge worker spends only about 25% of their time on the work they were specifically hired to do. The rest is what she called "work about work," meaning the surrounding administrative burden that must happen for the real work to start. In a real estate transactional practice, that includes digesting third-party reports, reconstructing transaction timelines from email, creating first drafts based on firm precedent, preparing standard closing correspondence, building closing checklists, and abstracting leases for diligence. The bigger productivity gains come from tools that understand how those pieces relate to each other.

That ratio of roughly one part judgment to three parts admin is the actual ceiling on how much excellent work a transactional attorney can produce in a day. AI is positioned to change that ratio. When AI takes on the first drafts, the diligence work, the closing correspondence, and a meaningful share of the surrounding admin, what's left on the attorney's desk is the work that actually requires a lawyer. That isn't a productivity gain. It's a different practice altogether.

Why Practice-Specific Legal AI Outperforms General-Purpose Tools

Of all the practical advice that came out of the conference, the sharpest and most actionable point was about how firms should think about purchasing AI.The conventional approach right now, and the one many firms are trying, is to buy a single legal AI product and roll it out across the entire firm. One tool, one license structure, everyone uses the same thing. The pushback from the speakers was direct: the work product of a litigator and the work product of a transactional attorney are different enough that a tool optimized for one will be only mediocre at the other. Brief writing, motion practice, and discovery review have a fundamentally different shape than purchase agreements, leases, title work, and closing correspondence. A general-purpose legal AI is, almost by definition, never quite the right tool for any specific practice, because the patterns it’s tuned for are an average of all of them.

The recommendation was that firms should think about AI procurement at the practice-group level. Each group buys what fits its workflow, with appropriate training, supervision, and licensing attached.

This is exactly the vision and focus we’re committed to at Orbital. Our customers, including some of the largest, most prestigious real estate practice groups in the country, have made it alongside us. Real estate transactional practice is different. It has its own document patterns, its own risk surface, and its own workflows. The smallest inconsistencies, like a transposed buyer and seller, a Delaware LLC in the recitals and a Texas LLC in the signature block, or a defined term that drifts between sections, are exactly the kind of risks that compound quietly across a real deal. Generic legal AI handles this kind of work passably. AI built specifically for real estate transactional work handles it purposefully. That difference is the entire game.

What we took home

The Chicago conversation confirmed what we’ve been seeing in the market for some time. The question among real estate attorneys has fully moved from whether to integrate AI to how quickly it can be done responsibly. Clients are setting new expectations. The ethics rules are catching up. The tools are getting sharper every quarter.

And while just five hands went up out of a hundred in that initial session. By the end of it, the rest of the room was clearly calculating how soon they could be among them. That’s the part of the trip we kept coming back to on the flight home. The shift is real, the practitioners are ready, and the next year is going to define what excellent real estate legal practice looks like for years to come. Real estate legal practice deserves AI that’s built for the work, and we look forward to continuing to help shape that future.