The short answer
You can trust AI to read a lease under three conditions. First, every extracted value must carry a citation to the page and clause it came from, so any number can be verified in seconds. Second, the system must resolve document chains explicitly, because reading the original lease without the second amendment produces confident wrong answers. Third, low-confidence extractions must route to human review instead of being silently accepted. Without those, AI reading legal documents is speed without accountability.
The real risk is not reading, it is inventing
Modern AI models read commercial lease language well. The failure mode that matters is fabrication: an answer that sounds like a lease term but appears nowhere in the document. The defense is structural, not behavioral. If the system is built so a value cannot be stored without its source text, fabrication has nowhere to live.
Amendments are where confident answers go wrong
Ask a model about a lease and it will answer from whatever document it was shown. The dangerous case is the five document tenancy where rent was restated twice and the option was deleted in amendment three. Trustworthy abstraction treats the chain as the unit of truth: later documents override earlier ones field by field, and silence leaves terms standing. That logic has to be engineered, not hoped for.
Where humans belong
Not transcribing base rent from a clean lease. Reviewing the rider with two crossed-out sentences and initials in the margin. A system that knows the difference, and routes accordingly, gets you the speed of software with the judgment of a reader, each where it belongs.
Common questions
What should I ask a vendor before trusting their abstracts?
Ask to click a value and see its source. Ask how the system handles an amendment that changes a term. Ask what happens when extraction confidence is low. The answers tell you whether accountability is built in or bolted on.
Bring one lease. Watch it get read.
A thirty minute walkthrough with your own document: the abstract, the citations, and the dates it finds.
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