Generative AI is already embedded in legal work. Lawyers are using it for research, for drafting, for summarising evidence, for reviewing correspondence. The question of whether the profession should engage with these tools has been overtaken by events. It already has.
The real issue is narrower, and rather more uncomfortable: most lawyers using AI do not have a defensible method of verifying what it produces.
Two patterns recur in practice. Some lawyers refuse to engage at all, on the basis that the technology is imperfect. Others engage too readily, accepting outputs at face value because they are fluent, well-structured, and persuasive. Neither position holds up to scrutiny. The first is increasingly impractical; as AI becomes embedded in the tools lawyers already use, opting out ceases to be a realistic option. The second is professionally dangerous, for reasons this article sets out to explain.
The problem is not that AI is sometimes wrong. The problem is that it is often convincing when it is wrong.
The only workable position sits between those two extremes: use the tools, but subject their output to a structured verification process. What follows is a framework for doing precisely that.
Why verification matters
AI outputs are probabilistic. They are generated by pattern recognition across enormous datasets, not by reference to a verified database of law. That distinction is worth reflecting on, because it explains something that catches lawyers out regularly: an answer can be coherent, well-phrased, and entirely incorrect. More subtly, it can be incomplete in a way that distorts the legal position without being obviously wrong.
From a professional standpoint, three categories of risk arise immediately:
- Fabrication: authorities that do not exist, or that are misquoted;
- Distortion: real authorities deployed for propositions they do not actually support; and
- Omission: relevant qualifications, exceptions, or counterarguments that the model simply does not surface.
Each of these risks engages core professional duties. A lawyer is not required to be infallible, but she is required to act competently, to exercise independent judgement, and not to mislead the court or the client. AI does not displace those obligations. If anything, it makes it easier to breach them, because the output arrives in a form that looks authoritative whether or not it is.
The question, then, is not whether an AI output appears correct. It is whether reliance on it can be justified.
The framework
What follows is not complicated, but it requires lawyers to resist the temptation to treat plausibility as a proxy for accuracy.
1. Identify the asserted sources – Start with what the output is actually relying on. If a proposition is said to be supported by a case, a statute, or a regulatory provision, isolate that authority. If no authority is cited at all, that is itself a signal.
2. Check primary sources – Locate the authority itself. Not a summary of it; not a secondary explanation. The primary text. For UK law, that will typically mean legislation.gov.uk for statutes and BAILII or the official court transcripts for case law. Confirm that the authority exists, and that the relevant passage says what the AI claims it says. This sounds elementary. It is also the step most frequently skipped.
3. Verify appellate status and treatment – A case does not become reliable authority simply because it exists. It may have been overturned in subsequent proceedings. Check whether the decision remains good law, and whether later courts have narrowed its scope. A proposition drawn from a first-instance decision carries very different weight if it has been overturned or overruled. This is not an optional refinement; it is part of verifying the proposition itself.
4. Test for completeness – Assume the answer is incomplete. The question is how. Are there recognised exceptions to the rule as stated? Is the proposition fact-sensitive in ways the output does not acknowledge? Is there a line of authority pointing the other way? AI outputs tend to present the most straightforward articulation of a rule. Legal reality is rarely that tidy.
5. Check contextual fit – Even a correct proposition may not apply to the problem at hand. Verify the jurisdiction and the factual alignment between the cited authority and the present case. A technically correct statement of law, applied in the wrong context, is still wrong.
An illustration
Suppose an AI tool produces a case said to support a proposition on misrepresentation. The case exists. The passage cited appears relevant. Many lawyers would stop there.
A competent verification process requires more. At minimum, it requires:
- Locating the full judgement, not merely the passage the AI has selected;
- Confirming that the proposition is actually established on the facts, not merely argued by one party;
- Checking whether the decision has been narrowed, overturned, or overruled; and
- Assessing whether the factual matrix of the cited case aligns with the present one.
Without those steps, the answer is not verified. It is merely plausible. And plausibility, in legal work, is not enough.
The right frame of mind
One might object that this framework asks lawyers to do what they ought to be doing already. That objection is fair, and it rather proves the point. AI does not create new professional obligations; it creates new opportunities to fall short of existing ones. The speed and fluency of the output make it easy to mistake confidence for correctness, even for careful practitioners.
AI can accelerate legal work. It can surface starting points quickly, suggest structures, and reduce the time spent on first drafts. Used with appropriate discipline, it saves time that can be spent on the parts of legal work that require a human mind.
But it does not assume responsibility for the output. The lawyer does.
A useful test is this: if you cannot explain how you verified an AI-generated proposition, you ought not to be relying on it. That is not a conservative position. It is the minimum required to use these tools in a way that is professionally defensible.
AI will continue to integrate into legal practice. The advantage will not lie with those who adopt it fastest, but with those who learn to use it properly. That starts with proper verification.
