The Checking Problem: What must be true before AI ships in a regulated firm
Enterprise AI programmes stall at a rate that is widely quoted and poorly explained. This paper measures the mechanism. Six document-heavy workflows of the kind performed daily in regulated financial services were run across four model families and three tool configurations, three times each, producing 5,093 scored output elements across 72 configurations. Each configuration was assessed twice: against a demonstration bar, being a single correct run on a single case, and against a production bar requiring sustained accuracy, reproducibility across repeats, verifiable attribution, and a confidence signal that carries information. 57 of 72 configurations cleared the demonstration bar and 32...
| Authors | Prerit Ahuja |
|---|---|
| Primary category | cs.CL |
| Published | 25 July 2026 |
| Last updated at source | 25 July 2026 |
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