What to measure before calling an AI pilot successful

Agree on success before the demonstration

An AI pilot needs a question that can be answered with evidence. “Can it summarise documents?” is too broad. “Can it prepare an accurate draft from these approved records, with less total review effort?” identifies a useful comparison. Agree what counts as an error and what a reviewer must check.

Measure the current process on representative work. Include difficult cases and incomplete requests rather than selecting only clean examples. If the baseline is uncertain, make learning about the process an explicit pilot outcome instead of inventing a return-on-investment figure.

Measure the whole job

Record the time spent preparing inputs, reviewing outputs, handling exceptions and correcting mistakes. Include model usage, hosting and integration costs. A fast generated response can still create more work if someone must verify every unsupported claim.

Separate minor formatting issues from errors that change a decision. A single average accuracy number can hide a failure category that matters disproportionately. Review results by task type and consequence.

Keep an evaluation set

Retain a controlled set of examples with expected outcomes and the reasoning used to judge them. Add examples when the pilot encounters a new failure. Re-run them after changing prompts, source material, models or integrations. This provides a more useful comparison than recollections of whether the latest demonstration felt better.

Decide whether to continue

• Can users verify the result without excessive effort?
• Does the workflow fail safely when information is missing?
• Is the operating cost understandable at expected volume?
• Is there an owner who can maintain it?
• Does the improvement survive ordinary, messy work?

Choose among expanding, revising or stopping the pilot. Stopping can be a good result when it avoids a larger investment in an unsuitable design. Vida Technologies can help define these checks in the initial scope so the decision to continue rests on the organisation’s needs and observed performance.