Why AI Pilots Never Reach Production
The demo worked. The team still cannot explain what changes on Monday, who owns the exception cases, or what evidence would justify keeping the system.
The recognizable symptom
A pilot produces a persuasive example but no operating contract. The next step becomes another demo, another prompt revision, or another request for more data. Confidence rises while adoption stays at zero.
The mechanism underneath
Most pilots optimize the model response instead of the decision loop. There is no named user, no baseline, no review budget, and no stop rule. Without those, success cannot survive contact with ordinary work.
What we built to contain it
We freeze the question before the experiment: what decision is changing, what counts as a useful output, who reviews it, and what would make us stop? The artifact is a receipt-backed continue, redesign, pause, or stop decision—not a prettier demo.
Making a content rubric reliable: anchor to observable criteria and calibrate with a pilot set
A single subjective score is unreliable because it depends on the rater's mood and context. Instead, build a rubric from binary, observable criteria—each one a checkable fact about the output. For example, for a support article: (1) contains a clear step-by-step procedure, (2) no factual errors against a provided source, (3) matches the brand tone guide, (4) includes a call-to-action, (5) is under 500 words. Each criterion is scored 0 or 1, so the total is a count from 0 to 5. Now you need a minimum score that isn't your opinion. Run a pilot: take 20 outputs, have two independent experts score them with this rubric, and also have them independently label each output as 'production-ready' or 'not ready' based on their overall judgment. First, check inter-rater agreement on the rubric itself using Cohen's kappa; if kappa < 0.7, refine the criteria until agreement is high. Then, for each output, compute the rubric score (sum of criteria) and compare it to the expert label. Suppose you find that all outputs labeled 'ready' have a score of 4 or 5, and all 'not ready' have a score of 3 or lower. That gives you an empirical threshold: minimum score = 4. This threshold is derived from data, not from your personal preference. If the pilot shows overlap (e.g., some 'ready' outputs score 3), you need to either refine the rubric or accept that the threshold is ambiguous. The limitation: this method depends on the quality of the expert labels and the representativeness of the pilot set. If your experts are biased or the pilot set is too small, the threshold will inherit those flaws. Recalibrate whenever the task or the output distribution changes.
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