Automatic data extraction is a general capability with a specific economics, and most disappointment with it comes from applying it outside the band where it pays. Three conditions have to hold together. Checking your candidate documents against them, before comparing products, shortens both the list of documents worth automating and the evaluation considerably.
Frequency, structure, legibility
Enough documents that setup amortises. A defined set of fields in broadly predictable places. And a sender with a reason to be readable, which a supplier billing you has and a site writing a note in a hurry does not. Invoices meet all three, which is why they are the classic application.
Where one condition is missing
Capture at source instead. A short structured form completed by whoever receives a delivery produces cleaner data than any attempt to read what they scribbled on a clipboard. This is not a failure of extraction; it is choosing the right tool for a document type that was never going to suit it.
Validation is not optional
Extracted values checked against what you already know: supplier exists, reference is new, arithmetic works, date is plausible, bank details match. These rules are usually the caller's responsibility rather than the extraction product's, which is easy to overlook and is what turns fast extraction into fast wrong data.
And design the review queue first
Whatever carries low confidence needs a person, on a screen showing the document beside the field, with correction in one keystroke. That queue is where your team spends its time, and building it last is how extraction relocates work rather than removing it.
Questions people ask about automatic data extraction
How do we judge a trial?
By the human minutes still required per document afterwards on your own worst inputs, rather than by an accuracy figure that averages easy fields with hard ones.
Does it work on handwriting?
Poorly, and it is better designed around than hoped for. Capture handwritten notes as photographs at the point of signature and record the data separately.
Should rules block or warn?
Duplicates and bank detail mismatches block. Arithmetic and plausibility warn, because those usually indicate an extraction problem rather than a problem with the document.
Where does most of the setup effort go?
Usually into the validation rules and the connections to systems downstream rather than into the extraction itself, which is largely configured out of the box in modern products. Budget accordingly.