How to automate data entry: three types and their costs
By Jacob Slot · Published · updated
To automate data entry, first split the job into three types: moving the same information between two systems, reading data that sits in a document, and collecting data that a person has to hand over. Each type has its own fix, and they do not cost the same. The split decides whether the project ends up cheap or expensive.
That is the short version. The long version is that almost everyone buys the wrong fix first, because they treat data entry as one job.
We see it with new clients every month. A company has bought a scanning tool for receipts and is disappointed, because an employee is still typing webshop orders into the accounting system by hand. The tool worked fine. It just solved a different kind of data entry from the one that was costing time.
What does it mean to automate data entry?
To automate data entry means that information gets into your system without a person typing it. The data moves by itself, because a rule says where it comes from, what it is called in the receiving system and what happens when something does not fit.
Two things often get the same name, and they are worth separating out. A faster entry screen is not automation, it is a better interface. A spreadsheet that two departments share is not automation either, it is a shared copy that someone still maintains by hand.
The difference matters to you as an owner. A tool only saves time when someone uses it correctly. A rule saves time every time, also in July, and also after the employee who used to do it has left.
It helps to know the Danish picture before you start. According to Statistics Denmark, the use of artificial intelligence in companies with more than ten employees rose from 15 percent in 2023 to 28 percent in 2024. But the most common uses were producing text or speech (18 percent) and text analysis (17 percent), while only about one in ten companies used AI for data analysis and to automate workflows. In other words, most companies use AI to write text, not to remove the typing. The job that actually costs money is still wide open.
The three types of data entry you can remove
Every data entry job in an ordinary Danish company falls into one of three boxes. Find yours, and you have found your fix at the same time.
The middle step is the one no tool vendor sells you, and it decides whether the other two work.
1. Transfer: the same information in two places
This is the most common type and the cheapest to remove. An employee types something into system B that already sits in system A: an order from the webshop into the accounts, a new customer from the CRM into case management, hours from an app into a spreadsheet.
The mark of this type is that no new information is created in the work. No decision is made, and nothing new is read. The information is simply moved, because two systems were never connected.
The fix is an integration. If both systems have an API, and almost all of them do in 2026, this is a well-bounded piece of work with a predictable result. It is also the type where you can often get all the way without building anything yourself, because vendors have ready-made integrations for the most common Danish systems.
This is also where the hidden version of the job lives: the spreadsheet next to the system. When you ask why it exists, the answer is always that the system cannot do one particular thing. Write that one thing down. It is typically a rule that can be built in a day.
2. Extraction: data that sits in a document
The second type is the one people associate with AI. The information only exists in a document: a supplier invoice in the inbox, a delivery note, a signed contract, a handwritten work slip from a technician.
Here there is real reading to do, and the documents look different every time. That is why plain rules fall short, and it is the only one of the three types where a model adds clear value. A flow picks up the document, reads the supplier, amount, date and perhaps the order number, matches it against a purchase order and sends it for approval to the right person.
Two things decide whether it works. The first is the check: an extraction must always be possible to reject, and doubtful cases must land with a person, with a reason, never silently. The second is volume. A handful of documents a month cannot carry a build, however annoying they are.
We have described the full chain for purchase and sales documents in our guide to automating invoicing. If the documents also have to be stored digitally, you will find the current requirements in the Danish bookkeeping act at the Danish Business Authority.
3. Collection: data a person has to hand over
The third type is the one that gets forgotten, even though it is the easiest. The information does not exist yet. A customer has to provide something, a technician has to log a job, a colleague has to order something.
Today it arrives as an email, a phone message or a message on Messenger, and then someone types it in again. That is not a data entry job. It is a job where there is no place to hand over the data.
The fix is rarely AI. It is a form, a link or a button in an app that writes straight into the system and checks the details as they are handed in. If you invoice the public sector, the same principle applies to recipient details, which must be validated before the invoice is sent through Nemhandel.
This type typically gives the biggest relief per krone, because you remove both the typing and the round of questions that used to follow.
What manual data entry costs you
No Danish study credibly says how many hours a company spends typing data. So you need to measure it yourself, and it takes less than you think.
Set up a sheet for one week and ask the employee who does the work to fill in four lines.
| Line | How to find the number |
|---|---|
| How many times | Number of times the task was done in a week |
| How long each time | Time three of them and use the average |
| How many errors | Number of times something had to be corrected afterwards |
| Who does it | Name and role, so you know the right hourly rate |
Multiply the first two numbers and add the pay. If you want the sum done for you, use our ROI calculator with your own numbers.
The third line is the one people underestimate. An error in a typed field is cheap in minutes and expensive in consequence, because it is found late: a wrong account number, an order with the wrong item number, an address that sends a parcel back. It is not the time at the keyboard that costs, it is the cleanup afterwards.
The third line is also your best internal argument. An employee who has to hand over their typing to a system will almost always point to the errors before they point to the time.
How to choose a fix for each type
Once you know which type you have, the choice almost makes itself. Here the three are set side by side.
| Type | Typical fix | Works when | Falls apart when |
|---|---|---|---|
| Transfer between systems | Integration via API | Both systems are open and the fields match one to one | One system is closed, or the same field means two things |
| Extraction from documents | Model plus a check | Documents vary, but the fields are the same | Nobody looks at the doubtful extractions |
| Collection from people | Form or app straight into the system | You decide how the data is handed in | The sender is an authority or a large customer with its own format |
The order is worth holding on to: use the simplest thing that solves the step, and save the model for the step that actually varies. It is the same logic we use in our method for workflow automation, and it exists because a model put on a job with fixed rules is both more expensive to run and harder to trust.
If the entry has several sources, because a report has to be assembled from three systems, it is really type 1 in the plural. That job has its own guide in automatic reporting.
What it costs to automate data entry
The price follows the type, not the number of fields. An integration between two open systems is the cheapest, a document extraction with an approval step is the most expensive, and a form sits in the middle.
Key numbers
15 hours
freed up per week for a typical client
$1,500
starting price for a scoped build, one-time fee
A scoped build starts from $1,500 as a one-time fee, and that is deliberate. An automation is not a job position and not another subscription: it is built once and then it runs. You own the source code, the documentation and the access, so on the day the work is done you could in principle never talk to us again, and everything would still run. How that works in practice is in our process, and the full price ranges are in our walkthrough of AI automation pricing.
On timing, a scoped data entry job is one of the quickest things to get running. Expect about three weeks from mapping to a running flow, and a measurable result within 90 days. If we cannot show it, we keep working at no extra charge.
If you want the advice partly covered, SMV:Digital's advisory grant can cover 50% of the advisory cost, up to 150,000 DKK, for a scoped digitalisation project. We have built 53 automations for Danish companies, and at Markeity they save 20,000 DKK every month on systems we built once.
When you should not automate data entry
Do not do it if the job runs a few times a month and takes a quarter of an hour. The sums do not add up however annoying it is, and the honest advice is to spend the money elsewhere in the business.
Do not do it if the field needs a judgment call every time. If an employee has to decide which category an expense belongs to, and the answer differs by customer, that is not data entry, it is a decision. Automate everything around it and leave the decision alone.
Do not do it if you do not agree on what the fields mean. Two departments using the same field for two things is not a technical problem. If you automate it, you lock the disagreement in and make it faster.
And do not do it if the system is being replaced within a year anyway. Then you are building an integration to something that is going away. Wait, or ask for the data entry to become part of the requirements for the new system.
FAQ about automating data entry
Can AI take over all data entry?
No, and that is not the goal either. AI is the right fix when the data sits in a document that looks different every time. When the data already exists in another system, an ordinary integration is cheaper, faster and more reliable, because it cannot guess wrong.
The practical recipe is a mix: integrations handle the fixed steps, and a model handles the one step where something has to be read. A person approves whatever the model is unsure about.
What is OCR, and is it enough?
OCR stands for optical character recognition and is the technique that recognises text in an image or a scanned PDF. It makes a document searchable, but it does not know which amount is the total and which is the VAT.
So OCR alone is rarely enough. You also need a layer that understands the fields and can match them against something you already know, and a place where doubtful cases land. That is the part that decides whether you can trust the result.
How long does it take to automate data entry?
A single integration between two open systems can be running within days. A document extraction with an approval step takes longer, typically about three weeks from mapping to running, because it has to run with a check for a while before the check can be loosened.
If it takes much longer, it is almost always because the job covers several of the three types at once. Then it needs to be split up, so that something goes live along the way.
Do we have to switch systems to get started?
No, and leaving the system in place is almost always the right order. Most common Danish accounting, case and stock systems have open APIs, and a flow can write straight into what you already pay for.
Only switch systems if the current one really lacks something you need. Replacing a system is a project in itself, and it should not sit on top of the automation that was meant to save you time.
What happens to the employee who types today?
In practice the answer is almost always that she gets back the tasks she was originally hired to do. Data entry is rarely a whole job, it is the half hour that sits in the way of the rest of the day.
It is worth involving her from the start, and not only for the sake of morale. She is the only one who knows where the exceptions are, and the exceptions decide whether the flow holds.
If you want to automate data entry, start by sorting the job into the three types and count for a week. That sorting is the whole difference between a tool that sits unused and a flow that is still running in two years.