What is a sufficiency decision and why does it matter for AI data?

In a real business, a case rarely arrives complete. A loan application lands with payslips but no bank statements. A claim arrives with an invoice but no proof of ownership. A utility connection request lacks a site authority. Someone has to decide whether what has been supplied is enough to proceed, name exactly what is missing, and ask for it in a way the other party can act on.

That decision has several parts:

- reading each document and working out what it is - checking the set against a policy for the case type - spotting documents that are present but unusable, such as expired, unsigned or covering the wrong period - recording a request with a due point and a consequence if nothing comes back

For AI training and evaluation this matters because models tend to fail in two directions. They proceed on thin evidence, or they ask for everything and stall the case. Both failures can only be measured if the data holds a policy, a document set and a labelled outcome together.

For a reseller, this is a clear category to describe to your own customers: a dataset where the labels are decisions and requests. Your customers can test whether a model separates enough from not enough, and whether its requests are specific.

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