Roughly a quarter of executives say an AI error has reached their board or an external audience before anyone caught it, part of the wider verification gap between how fast AI output travels and how slowly anyone confirms it. Those errors did not sneak past anyone. They got past the analyst, the reviewer and the final read-through because nobody asked the one question that mattered. How likely is this particular output to be wrong?
Most organisations answer with more checking. That is the wrong lesson, or at best half of one, because checking harder cancels the point of the machinery being checked.
The arithmetic against verification
Verification means a person re-deriving what the machine produced. Do that for everything and the AI has saved you nothing; the work was simply done twice. So no organisation verifies everything. Each one verifies what it happens to distrust, and distrust gets allocated by instinct, recency and the seniority of whoever is asking. None of those tracks where the errors actually are.
Selective checking is the only affordable kind, and selecting needs a basis.
Unverified is the symptom. Unscored is the disease.
What a score changes
A score here is not the confidence a model reports about itself; that number is where the problem starts. A score is a track record. This model, on this class of question, in this context, has been right at this rate, and the rate comes from outcomes, not assertions. Attach that to every output and the checking question answers itself. An output with a strong, current record earns a light touch. An output from a model never scored on this question earns a heavy one, or does not travel at all.
Scoring also flips the burden. Today a person must justify distrusting an output that looks finished; scored, the output must justify being trusted, on the page.
Where the score belongs
The score cannot live in the board pack, because by the time the pack is assembled the error has already travelled. It has to attach where outputs are born and move with them. That makes this an infrastructure problem, not a policy one. Outputs, decisions and outcomes have to flow through something that binds them together durably, so that when an outcome lands, the model that called it wrong inherits the miss.
That binding is what one event backbone exists to provide. A memo telling staff to check AI outputs adds effort and no information. A backbone that scores outputs before they travel puts the information exactly where the checking decision gets made, so errors arrive at a threshold, with an owner, while they are still cheap. Prophesee scores on the backbone by construction. Every output carries the record it has earned, and no amount of after-the-fact checking can imitate that. Publishing the record then becomes a choice rather than a scramble. Start here.