Firms keep asking what new rules apply. The more productive question is which existing ones already fit — and supervision fits almost exactly.
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A great deal of the anxiety about AI in practice comes from treating it as unprecedented. In one important respect it is not: firms have always had a framework for work produced by someone who does not carry ultimate responsibility for it. Associates draft, partners are accountable. Paralegals research, lawyers sign. The duty to supervise, and the principle that delegation does not transfer responsibility, are well developed and well understood. ABA Formal Opinion 512 addresses supervisory responsibility directly among the duties generative AI engages. Approaching AI output as work requiring supervision rather than as a novel category gives a firm a working answer immediately, and one its people already know how to operate.
Useful frames are useful precisely where their limits are understood.
The signing lawyer owns the work product regardless of what produced the draft. This holds exactly.
Firms already calibrate scrutiny to consequence and to the drafter's reliability. Also holds.
You cannot supervise what you do not understand — which is why the failure modes must be taught.
Where the analogy breaks. A junior corrected once improves; the tool makes the same error tomorrow.
A junior says "I could not find anything on this." A model produces something anyway, with equal confidence.
Fluency reads as reliability to a reviewer. With a junior, hesitancy is itself a signal; here there is none.
How firms operationalise it.
It converts an unfamiliar problem into one the firm already has machinery for.
A junior who found nothing says so. A model produces something regardless, in the same confident register as when it is right. Reviewers reading for hesitancy — the signal they have relied on for a career — will not find it.
Most firms that have thought about it conclude yes, at least for work that will be relied on, and several now require it as a matter of internal policy. The reasoning is practical rather than ethical: a reviewer who knows a draft is AI-assisted reads it differently, and reads for different failure modes. Some courts have also adopted standing orders requiring disclosure or certification regarding AI use in filings, which varies by court and needs checking case by case.
Allocation of responsibility within a firm is a matter for the firm and for the applicable conduct rules, and generalising is unwise. What the reported decisions suggest is that courts have looked at the whole chain — who used the tool, who reviewed, who signed, and what the firm's process was. Sanctions have fallen on individual attorneys and, in some matters, on more than one lawyer in the same filing. A firm relying on the idea that responsibility sits entirely with the most junior person in the chain would be taking a position the record does not obviously support.
Enough understanding of how the tools fail to know what to look for. That does not mean technical knowledge of how models work — it means knowing that citations can be wholly invented, that a real case can be cited for a holding it does not contain, that quoted text may appear nowhere in the source, and that none of this will be signalled by any hedging in the output. A supervisor who believes the tool "sometimes makes mistakes" in the way a search engine returns imperfect results has the wrong mental model and will review for the wrong things.
It is closer to that than to supervising an employee, and the comparison is instructive. With an outsourced provider a firm considers what the provider does with the material, what its quality process is, what contractual protections apply, and what independent checking is warranted. Those are precisely the questions that matter for an AI vendor — and firms with mature outsourcing diligence often find they already have most of the framework, applied to a different kind of supplier.
Describe how supervision works at your firm today. The Institute will help you extend it.