Keep a Useful Record of Your AI Experiments

A team can spend a great deal of time trying AI tools without learning much that survives the experiment. Someone remembers an impressive answer. Someone else remembers a frustrating failure. Neither memory tells the next person what was tested or whether it fits their task. A short experiment record creates a more useful result: a traceable account of the assignment, conditions, output, and decision about what to do next.

Write the question before running the experiment. “Can AI help us?” is too broad to evaluate. “Can this approved tool draft a first version of a standard customer update from verified order notes?” describes a bounded task. State the current approach and the difficulty you are trying to reduce. The experiment should test a specific improvement, such as less rewriting or a clearer handoff, rather than reward output simply because it was generated quickly.

Record the material used in the test without creating an unnecessary copy of sensitive information. A reference to an approved sample and its version may be enough. Note whether the sample represents routine work or a difficult exception. Results from a clean demonstration may not transfer to incomplete, inconsistent, or unusual inputs. If a tool is only useful when the source material is exceptionally tidy, that condition belongs in the findings.

Save the instructions that produced the output. Include relevant settings or workflow choices when they affect the result, but avoid collecting technical details that nobody will use. The record should allow another authorized person to understand the test. If the instructions were revised several times, preserve the version that produced the reviewed answer and summarize the important changes. Otherwise, the team may credit the tool for an improvement actually caused by a better assignment.

Evaluate the answer against criteria written in advance. Consider factual accuracy, completeness, tone, required human effort, and the consequences of a mistake. A draft that takes seconds to generate can still create more total work if it needs extensive checking. Capture actual observations from the test rather than estimates presented as measured results. It is acceptable to say that the sample is too small to support a broader conclusion.

Include unsuccessful attempts. A record containing only the best answer gives a misleading picture of reliability. Note recurring omissions and the conditions under which the tool struggles. Distinguish a fixable instruction problem from a task that may be unsuitable for the proposed approach. The goal is not to prove that an experiment succeeded. It is to help the business choose whether to continue, modify, limit, or abandon the use case.

A compact record could contain six fields: the question, the sample used, the instructions, the observed result, the review effort, and the next decision. Add a date and owner so the record can be interpreted later. If a field repeatedly provides no useful information, change it. The log should support learning rather than reward paperwork. Its value becomes visible when a colleague can understand the test without scheduling a meeting with the person who ran it.

Close each record with an owner and a next decision. That might be a second test using harder examples, a limited pilot with mandatory review, or a decision to keep the existing process. Store the record somewhere the relevant team can find it, alongside related experiments. A useful log is concise enough to maintain and specific enough to challenge. Over time, it becomes a body of organizational learning rather than a collection of enthusiastic screenshots.