Why Human Review Is Essential Before Using Generative AI Output
Hello! I am Noriaki Asato, Representative Attorney at Legal Agent.
Generative AI can prepare contract drafts, organize research, and identify issues at remarkable speed. It can also produce a confident, polished answer that is factually wrong, inconsistent with company policy, or unsuitable for the transaction at hand. That combination—speed and apparent confidence—is why human final review is indispensable in legal work.
AI can be convincingly wrong
Generative AI may invent a statute number, case citation, date, figure, or proper name. It may also cite a real rule that does not apply to the relevant facts. Legal teams should therefore treat AI output as a potentially useful draft, not as verified authority.
Any legal proposition should be checked against the primary source. Contract language should also be reviewed word by word where a small change can materially affect the result, including confidentiality periods, liability caps, termination rights, and ownership of intellectual property.
AI does not know the company's full context
An answer that is generally reasonable may still be wrong for a particular company. The model may not know the company's internal approval rules, risk tolerance, negotiation history, strategic priorities, or relationship with the counterparty.
For example, a model may recommend negotiating a liability cap because it appears low. Whether that point should actually be escalated depends on the size and importance of the transaction, available alternatives, insurance coverage, and the company's commercial objectives. Those are business and legal judgments that require people who understand the matter.
What human reviewers should check
The depth of review should reflect the risk of the work, but the following points are a practical baseline:
- verify statutes, cases, figures, dates, and names against primary sources;
- confirm that the analysis matches the company's position and the governing jurisdiction;
- check whether an external message reveals confidential information or uses an unnecessarily aggressive tone;
- review high-impact provisions such as liability, termination, data use, and intellectual property more closely; and
- require more rigorous review where the transaction value or sensitivity of the data is high.
A workable division of responsibility
A useful operating model is: AI prepares; people decide. AI is well suited to summarizing documents, creating issue lists, comparing clauses, and drafting a first version of an internal memo. People should decide which issues matter, how strongly to negotiate them, whether the legal authority is reliable, and whether the final result should be sent or signed.
The same principle applies when AI is used to check AI-generated text. A second AI pass may identify omissions, but it does not replace verification by a responsible person. The final reviewer should be able to explain the source, the reasoning, and why the proposed action fits the company's circumstances.
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