Glossary

What is Pilot Purgatory?

The pattern is familiar in enterprise AI. A pilot is scoped on a clean sample, runs for a quarter, produces a promising result, and then stalls. The next step needs budget, a security review, and a decision that the result will hold, and nobody can give the third. So the pilot is extended, re-scoped, or restarted with a new vendor.

The term came from manufacturing technology adoption and is now used for generative AI programs. The model may not be the cause. The pilot was evaluated on data that does not match production: cleaner, smaller, older, or missing the exceptions that make the real process hard.

How is pilot purgatory different from a failed proof of concept?

  • A failed proof of concept produces a clear negative result and ends.
  • Pilot purgatory produces a positive result that cannot be trusted beyond the pilot, so the project neither ends nor advances.
  • The difference is evidence. A proof of concept answers “can it work”. Purgatory is the absence of an answer to “will it work here, on our data, next quarter”.

Where does pilot purgatory break in AI work?

A document-classification pilot scores well on a few thousand labelled files. The production archive holds more than a million files from a dozen sources, three scanning vendors, and a decade of template changes. Nobody measured the pilot on a sample drawn from that archive, so the score is a fact about the pilot set and nothing more. The steering committee asks whether it will hold, and the honest answer is that no one knows.

The way out is to evaluate the pilot on data drawn from production, under the acceptance criteria the business will use, and to record what was checked so the decision can be repeated when the data changes. In Syntitan that is Ready for Model: the same task runs on the data before and after preparation, on the same evaluation set, and the result is recorded with the data Version and conditions it used. Read against the acceptance criteria, that is part of the evidence a go or no-go decision needs.

CUBIG Shorts

Why AI Pilots Stall on Real Data

The entry above, in short form: the pilot passed on sample data and stalled on the data the business runs on.

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Why AI fails after deployment describes what happens when that step is skipped, and time to value is the metric purgatory quietly destroys.

Frequently asked questions

What causes pilot purgatory in AI projects?

Pilots evaluated on data that does not match production, acceptance criteria that were never written down, and no record of what was checked, so nobody can say whether the result will hold outside the pilot.

How do you get an AI pilot out of purgatory?

Draw the evaluation data from production, agree the acceptance criteria before the run, compare results before and after data preparation on that same evaluation set, and record the data version and conditions so the decision can be repeated.

Is pilot purgatory a technology problem or an organizational one?

Both. The organization cannot decide without evidence, and the evidence is missing because the pilot was not measured on the data and conditions the decision is about.