When a person types an invoice into a system they are doing two things: entering data, and noticing when something is odd. Automating the entry removes both. The first is the point; the second is a loss that has to be replaced deliberately, and the replacement is a short list of validation rules that run before anything downstream trusts the values.
What the person was catching incidentally
An invoice from a supplier you do not use. An amount that makes no sense for that supplier. A date in the future. A duplicate they half-remembered from last week. None of these are extraction problems, and all were being caught by somebody who had to look at the document long enough to type it.
The rules that replace it
Supplier exists in your records. Invoice number not seen before for that supplier. Lines sum to the net and net plus tax equals the total. Date within a plausible window. Bank details match what you hold. Five cheap checks, run automatically, covering most of what the incidental attention was covering.
And a queue for uncertainty
Whatever passes validation but was extracted with low confidence should reach a person, on a screen showing the document beside the field. That queue is where the remaining human attention belongs. Designing it well is what makes automated entry a saving rather than a transfer of work to a less structured place.
Questions people ask about automated data entry
How many rules are enough?
The five above cover most of it. Add rules where your own corrections cluster, which you will only know by recording them for a month.
Should rules block or warn?
Duplicates and bank detail mismatches block; arithmetic and plausibility warn, since those are usually extraction errors rather than problems with the document.
Does this apply to other documents?
To anything captured automatically. The rules differ; the principle that automation removes incidental noticing does not.