Using Artificial Intelligence to Control Corporate Legal Costs: A Case Study from the Real Estate Industry

Prolific reporting on artificial intelligence (AI) applications in business can be intimidating. Especially for those of us who lack hands-on expertise in the use of machines to perform cognitive functions.

For business leaders trying to control corporate legal costs I find that a concrete example can help to by-pass the technical stuff to make the P&L impact clear.


Take the real estate sector.

Specifically, consider the management of condominiums*.

We begin with a business problem that confronts all condo managers and their boards. In considering any action — or inaction — they must ascertain: What constraints are imposed by this particular condo’s governing documents?

From — a publication for real estate lawyers:

“CHICAGO–A typical client for Nicholas Bartzen, an associate with Levenfeld Pearlstein’s [a law firm] Community Association Group [a group of lawyers within the law firm that focuses on serving a specific kind of business client — condo managements and their boards], would be a condominium representative whose building has anywhere from four to 500 units and whose board has a question that needs to be responded to quickly.

“The answer can most likely be found within the condo’s governing documents but as Bartzen tells

“‘ The way these documents have been written is anything but uniform.'”

The problem:

  1. This law firm’s condo lawyers are frequently called upon to interpret a particular condo’s governing documents: Can we do this? Do we have to do that?
  2. Governing documents for condos vary in their terms — a lot.
  3. It’s too expensive to pay for a lawyer like attorney Bartzen to read — manually and line-by-line — these governing documents in these circumstances.
  4. But … attorney Bartzen needs to get his answers right. “Close enough” won’t do.

What’s an AI solution to this problem?

“To find the clauses that he needs within these documents, Bartzen turns to an application called Diligen, which uses machine learning — a type of artificial intelligence that self-corrects and learns as it receives more information — to scan the governing document. To be sure there are other applications on the market that offer similar AI-driven scanning services ….”

Why is this particular solution effective? According to Mr. Bartzen, it’s the specificity of the match between the technology and his problem:

” … Bartzen likes Diligen because he says it is aimed at his niche practice of law. Other applications that he vetted, he says, ‘didn’t have the algorithms that I need for my clients.'”

The business significance?  

First, economy: This lawyer won’t be reading these governing documents line-by-line every time he has a question about them.

Second, accuracy: There’s evidence that the machine can be more reliable than the human eye and brain when it comes to AI-assisted review of legal documents.

Though it’s early days (in the accumulation of empirical tests and their results) — in the test by a prominent Israel-based legal tech firm called LawGeex that I wrote about in March, AI beat humans 94% to 85% for accuracy.

Third, speed: In the Israel-based legal tech firm’s test the humans took an average of 92 minutes to do their work — versus 26 seconds for the AI platform. (And I read the bio’s of those attorneys — and they were impressive).


* A form of real estate ownership where several units are each separately owned, and so-called “common areas” outside those individual units are jointly owned and managed on behalf of the individual unit owners by a community association.

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