Beyond experimentation: what ILTACON 2026 revealed about the future of real estate AI

"The deeper AI moves into substantive practice areas, the more that practice-area context matters. Commercial real estate is perhaps the strongest example."

After many years of hearing about how great ILTACON is, this year was my first time attending. Beyond the usual fun of Nashville, this conference provided a very insightful lens on where legal AI is heading next.

For the last couple of years, one of the dominant ideas in legal AI has been simple: get the technology into lawyers’ hands and let them experiment. Sensible, to be sure. Firms needed to launch exploratory use cases and understand what AI could and could not do in real-world legal work. But after a week of conversations at ILTACON 2026, including the real estate AI master class that Lauren Hirt and I presented, it’s clear that the experimentation stage is over.

Access to AI is becoming ubiquitous. Simply having AI and being an early adopter of it will not differentiate a law firm for long, any more than having a Blackberry and being an early adopter of mobile telephony did. Pretty soon, everyone has a Blackberry too. Long-term, durable competitive advantage will come from how effectively firms apply their new technology to the work that truly matters. For me, that leads to a straightforward view: the future of legal AI will be increasingly vertical, not horizontal. Sophisticated clients choose practice area specialists for consequential work. Why should the technology those lawyers use be any different?

Moving beyond experimentation

Early AI tools were generalists. A frontier model will write you a poem, draw you a picture, summarize a document, or offer you gardening advice. All good and useful; indeed, I run my personal token allocation dry on most days. But real estate clients don’t want their lawyers writing poems, drawing pictures, or offering gardening advice. They hire real estate transaction lawyers to do real estate transaction work. And that work isn’t summarizing deposition transcripts. 

Real estate counsel need tools that go far beyond generic summarization. They need AI coworkers that understand the context, complexity, and workflows of commercial real estate transactions. For instance, can the AI tool work across title analysis, surveys, leases, legal descriptions, and transaction documents? Can it understand how information in one source affects another? Can it reflect the way experienced real estate lawyers review matters and exercise judgment? Those are becoming more meaningful measures of legal AI than whether a lawyer can successfully prompt a frontier model, or whether they can find a way to forcibly wring individual value out of a generalist legal AI tool.

From point solutions to connected legal workflows

Another theme I heard at ILTACON was the move beyond isolated use cases. Task-targeted tools can make individual tasks faster. But in complex practice areas, more value is created when those capabilities connect across an entire matter. Orchestration across tasks and parties generates new value that benefits all parties in the transaction.

Real estate deals make that especially obvious. A single deal can involve multiple parties, large volumes of documentation, shifting commercial positions, moving deadlines, and asset-specific information. The information needed to understand the transaction rarely lives in one place. That creates an opportunity for real estate specific AI to support a more connected, whole-of-deal workflow: bringing documents, issues, decisions, and transaction context together while keeping lawyers and their clients at the center of the process. The prior focus on “which task can (or can’t) AI automate?” is giving way to a more holistic view of “how does AI help the entire transaction move forward?”

Why domain-specific AI matters in real estate

Both frontier models and general legal AI platforms will continue to play an important role, particularly for work that is (to one degree or another) common to all legal practices. Basic case law and statutory research sit here, for example.

But the deeper AI moves into substantive practice areas, the more that practice-area context matters. Commercial real estate is perhaps the strongest example. Real estate legal work involves more than document text, cases, or statutes. Lawyers need to interpret and apply complex real property doctrines that vary across state jurisdictions, reconcile title information with surveys and plans, understand legal and commercial relationships, know what practices are “market” in different states and cities, and assess how all of these things affect the underlying asset.

A general-purpose AI system can extract and summarize information from a document. But domain-specific real estate AI needs to understand what that information means within the transaction. That distinction is particularly important in areas such as title and survey review, lease analysis, due diligence, and drafting.

Encoded know-how could become your real differentiator

One of the most interesting themes from our master class at the conference was a key point I learned from one of my business school professors, Vijay Gurbaxani. As he often puts it, “Software is encoded know-how.”

Much of a law firm’s expertise does not sit neatly in a database. Nor is it obviously implicit in end-state transaction documents. Rather, it exists in how experienced lawyers approach a matter: what they look for, what they escalate to clients as a “business issue,” and how they structure or frame a position. AI creates an opportunity to make that know-how more explicit.

Firm-configured workflows can help encode the processes and standards that define how a practice group operates. Each has its own culture, pathways, and mores. If technology can help apply that expertise more consistently across teams and matters, AI becomes more than a productivity tool. It becomes part of how institutional knowledge and firm culture is preserved, scaled, and transmitted to the next generation.

I suspect that will become an increasingly important source of competitive differentiation.

What ILTACON reinforced for Orbital

Our conversations at ILTACON reinforced why Orbital has taken a specialist approach to AI for real estate law. Real estate is our only focus, and Orbital has been shaped by more than 250 years of combined real estate legal expertise, of which I am proud to be a part.

That matters because consistency and predictability do not come simply from pointing a general-purpose model at a set of real estate documents. They come from deeply understanding how real estate lawyers actually work: the documents they review, the issues they care about, the workflows they follow, the judgment they apply, and the physical asset underlying the transaction.

The first phase of legal AI was about experimentation. The next will be about differentiation. Broad platforms will have an important role, firms will build some capabilities themselves, and specialist AI products will go deeper into individual practice areas. Advantage will come from choosing the right technology for the right legal workflows, keeping judgment at the center, and measuring AI by the value it creates, not simply the time it saves.

Justin Lischak Earley is Principal, Legal Content & Evangelism at Orbital, with deep experience across commercial real estate law, title insurance and legal technology, and leadership roles with ACREL, ACMA and other leading industry bodies. For more on how technology entered legal practice and what will differentiate firms as AI becomes embedded in day-to-day work, tune in to Justin's ILTA podcast.