AI automation
Document Automation: From Invoices and Emails to Clean Data
Most back-office time is spent re-typing information that already exists in a document. Automation can read it, but a person should confirm it.
The re-typing problem
Much administrative work is moving information from one place to another: a supplier invoice into an accounting sheet, a customer email into an order list, a delivery note into a stock file. The information already exists. The effort is in copying it, and every copy is a chance for a typing error.
What automation does well
Modern document processing can read a PDF or an email, find the fields you care about, such as supplier, date, line items and totals, and place them in a structured table. Because the output has a fixed shape, it can be checked automatically: do the line items add up to the total, does the date make sense, is the supplier already known?
Keep a review step
Extraction is probabilistic, so the sensible design is to stage results for review instead of writing them straight into your records. Entries that pass the automatic checks can be approved in bulk, and unusual ones are flagged for a person. This follows the general approach in the NIST AI Risk Management Framework, a voluntary framework that treats AI as a risk to be managed and measured rather than something to trust blindly.
What it looks like in practice
A good first project is narrow: one document type, one destination, one reviewer. At MayTech Labs we build this kind of automation as a small pipeline plus a simple review screen, so the time saved is visible from the first week.
