A legal department’s AI experiment can become indispensable before anyone formally approves it. A prototype gains users, access to live contracts and a backlog of feature requests. Then its creator goes on vacation, and a question emerges: Who is responsible for keeping it running?

That scenario illustrates a risk for legal teams testing new technology. A proof of concept may begin as a temporary exercise, but small decisions can turn it into an operational dependency. Preventing that drift requires teams to define what they want to learn—and what they will do with the answer—before the test begins.

A broad goal such as exploring what AI can do with contracts is unlikely to produce a clear decision. A more useful pilot might ask whether a tool can identify specific provisions well enough to help procurement prepare for legal review, or whether lawyers can verify its answers quickly enough to reduce overall work.

Those questions test different capabilities. A system may identify provisions accurately but save little time if its citations are difficult to check. Users may like an interface while misunderstanding its ratings. A promising tool may also rely on documents the organization cannot consistently obtain.

Testing should reflect those complications rather than the favorable conditions of a demonstration. Scanned agreements, missing amendments, exceptions buried in exhibits and time-pressed users can reveal weaknesses a polished demo misses.

Legal teams should also assess errors by their consequences. Missing a routine notice provision is not necessarily equivalent to overlooking a material liability exception, even if both count as inaccuracies. The time spent preparing documents, checking answers, correcting mistakes and escalating problems belongs in the evaluation as well.

Each pilot should have an end date, a decision owner and agreed options for what happens next. The evidence may support purchasing a product, building a narrow capability internally, pursuing an integration or improving data before proceeding. It may also justify stopping.

Those rules are easier to agree on before a team has invested heavily or executives have promoted the initiative. Retiring a prototype need not mean discarding what the organization learned.

Pilot results can also sharpen pricing discussions. If a vendor claims to reduce review time, legal and procurement teams can examine whether savings remain after verification, reduce outside counsel work or affect time to signature. They should also account for the customer’s investment in data cleanup, training and revised processes.

Before a successful experiment expands, the organization should make graduation an explicit decision: assign an operating owner, define permitted uses, establish support and document how errors will be handled. The evaluation should remain available after the original project team moves on.

The aim is not to keep promising tools out of use. It is to ensure that a pilot produces enough evidence for someone to decide whether—and how—the business should rely on them.