Manual Data Entry: When the System Reads the Document

Automating data entry means the system reads the document you receive, such as a delivery note, an application form or a customer email, extracts its fields, checks them against what it already knows, and sends anything uncertain to an employee for review instead of that employee typing every field by hand. In part 3 of the series you built a clear approval path. This fourth part of 7 gives you 4 stages a document passes through, 3 possible outcomes for each document, and a one-week exercise to measure how much manual entry you really do.

Where manual data entry hides

Nobody calls this work by its name, because it is spread across many departments and each piece looks small. But look for any document that arrives from outside and is then typed into a program by an employee, and you will find it in places like these:

• Supplier documents: quotations, item lists, certificates of conformity and confirmed supply orders, arriving as a PDF or a photo, whose figures are then copied into the system.
• Delivery notes: a paper signed by the recipient comes back with the driver, then someone keys in the quantities and the date. We explained why that paper matters in Proof of Delivery: The Invoice Starts at Sign-off.
• Application forms: a new customer account form, a job application or a service request, filled in on paper or in a file and then re-keyed.
• Customer emails: an order written in the body of a message or in an attachment, which an employee reads and then turns into an order in the system.

There is nothing wrong with the team working this way today; these employees are the ones who know where documents differ and where senders make mistakes. The problem is not them. It is that every field is written twice: once by the sender and once by you.

How the system reads a document: 4 stages

Machine reading is not magic. It is a clear chain of steps you can explain to your team in minutes:

• Receiving: the document arrives in one known place, such as a dedicated mailbox, a folder or a photo from a mobile app, instead of scattering across employees' phones.
• Extraction: optical character recognition (OCR) turns the image into text, then an AI model identifies the fields you need: supplier name, date, items, quantities. The difference is that OCR reads the characters, while the model understands which characters belong to which field even when the layout changes.
• Validation: the system compares what it extracted with what it already knows. Is the supplier registered? Is the item in your item list? Does the quantity on the delivery note match the supply order?
• Routing: if the document passes validation it enters the system; if not, it goes to a person for review, as the next section explains.

If your documents are scattered today between email, folders and paper, start with the receiving stage before any AI. You will find how to organise it in Document Management and Digital Archiving.

Confidence and the review queue: where the human stays

When the model extracts a field, it also gives it a confidence score, an estimate of how sure it is that it read the field correctly. A faint print, a stamp over a number or an unusual table lowers that score. No confidence score means certainty, so we do not rely on anyone's general accuracy figure. Instead the decision rests on 3 possible outcomes for each document:

• It goes straight in: when confidence is above the threshold your business has set and the fields pass every validation rule.
• It goes to the review queue: when confidence is low on at least one field, or when something does not match what the system knows. A review queue is a task list where the employee sees the original document beside the extracted fields and corrects only the doubtful field instead of typing the whole document.
• It is returned or rejected: when the document is incomplete, unreadable or from an unknown sender, and the sender is asked for a clearer copy.

Start with a high threshold that sends most documents to review in the first weeks, then lower it gradually as you see for yourself, in the correction log, where the system gets it right and where it does not. Every correction the reviewer makes stays in the log, so the person in charge knows which document type needs more rules.

What you do not leave to the system alone

Machine reading removes repetition, but some decisions always stay with a person: a document from a supplier you have never dealt with, an amount above an approval limit, a quantity that does not match the supply order. These are exceptions, meaning cases where the rule does not apply and a decision is needed. For other processes AI can run today while people stay in charge, see 7 Processes AI Can Run in Your Company Today.

Many of these documents carry personal data, such as job applications and new customer account forms. The Personal Data Protection Law published on the SDAIA site states in Article 19 that the controller must take the organizational, administrative and technical measures and means needed to protect personal data. Article 29 sets conditions for transferring personal data outside the Kingdom, which matters if your reading tool runs on servers abroad. Article 31 requires the controller to keep records of personal data processing activities for a period set by the regulations. Ask where documents are processed before choosing any tool, and confirm the details with SDAIA or a lawyer; this is not legal advice.

The Origami view

We are a Saudi technology company that builds systems, apps, integrations and AI solutions. When we build document reading, we start with one frequent document type and collect real samples of it with the employees who key it in today, because they are the first to know where its layout varies and where senders make mistakes.

We design the review queue before the extraction itself: who reviews, which threshold sends a document there, and where each correction is kept. The value is not that the system reads everything; it is that it knows when it is not sure. You will find the details of what we build on our services page.

Exercise: one week of one document type

Choose one document type that recurs in your business, such as delivery notes or new customer forms, and over one working week record:

• How many documents arrived of that type during the week, and through which channel: email, WhatsApp or paper.
• How many minutes each one takes to key in, timing at least 10 documents with your phone's stopwatch.
• How many fields the employee types for each document.
• How many documents needed a correction after entry, and why.
• Whether the document carries personal data, and where it is kept after entry.

Multiply the number of documents by the average minutes and you know how many hours a week go to this one type. That figure, measured by you, is the basis of the automation decision, and it is more honest than any general savings percentage.

In the next part

Once the system reads the document, one question remains: where does the data go next? Part 5 of the series, Connecting Your Systems: The End of Copy and Paste, explains how each piece of information gets one system that owns it, and how it moves between programs without anyone re-typing it.