Start with the process that is repeated most often and is most error-prone, not the most visible one. Calculate the ROI: hours saved times hourly cost versus the build cost. A process that costs ten hours a week often earns back an investment of a few thousand euros within months. Automate the boring, repetitive tasks first, such as retyping data and manual reports. At NedDev, we start with a process analysis before we write a line of code.
A bookkeeper who spends two hours every Monday morning retyping data between two systems. That is not a small inconvenience: that is well over a hundred hours a year, plus the errors that manual retyping inevitably brings, plus the irritation of work a machine would do without complaining. This kind of task is exactly where automation delivers the most. Not the glamour, but the boring, predictable repetition.
The mistake most companies make: they automate what is most visible or most sexy, instead of what costs the most. The best starting point meets three characteristics at once:
Retyping data between systems, compiling manual reports, creating and sending invoices, following up with customers via reminders, updating inventory: those are the classic candidates. A process that costs five minutes once a month, you leave well alone, because the build and maintenance costs never catch up with that time. A process that swallows an hour a day is gold worth tackling.
Automation is an investment, so calculate it up front. The sum is simple: hours saved per year times the hourly cost, set against the build and maintenance costs. A process that costs ten hours a week at an hourly rate of 40 euros costs you well over 20,000 euros a year in labor. An automation of a few thousand euros then pays for itself within months, and everything after that is pure profit that continues year after year.
Do not forget the hidden gain, because it is often larger than the direct hours saved. Besides the time saved you win fewer errors, a faster turnaround for the customer, and employees who spend their hours on work that requires judgment and creativity instead of retyping. Those benefits are harder to capture in a single number, but they count fully and are often exactly the reason a team enthusiastically cooperates with the automation.
At ClaimHandler we automated case handling for damage experts: documents are automatically linked to the right case, deeds signed via a digital flow, and the status of each case keeps up without anyone having to track it manually. What first cost hours of manual work per case now largely happens by itself, while the handler stays in control.
At Lexi an AI assistant answers questions about collective labor agreements that an employee would otherwise have to look up one by one in long documents. And IndexNu automates bookkeeping processing that previously happened entirely by hand. In each of these cases the same underlying logic applies: a repetitive, rule-based task became software, while the human kept the judgment and the exceptions.
We write no line of code before we truly understand the process. A good automation starts with mapping out what exactly happens now, step by step, including all the exceptions people carry in their heads but have written down nowhere. It is precisely those exceptions that determine whether an automation runs smoothly or becomes a new problem itself. A few principles we always apply:
During that analysis, pay close attention to the handoffs between people and systems, because that is usually where the biggest hidden time waste sits. A document that gets moved from email to a folder to a spreadsheet to another system loses time, and sometimes data, at every handoff. It is precisely those chains of manual handoffs that are rewarding to automate, because the gain stacks up across multiple steps. Do not start by automating one isolated action, but look at the entire path a piece of work travels from beginning to end. Often you then discover that half the steps were not needed at all, separate from automation.
We build automation on Laravel 12 with Filament for the back end, connected to AI via Claude, GPT, or Mistral where judgment or language understanding is genuinely needed. A first automation project sits, depending on the scope, in the range of a few thousand to several tens of thousands of euros. Want to know which process in your organization pays off fastest? Take a look at our automation service. We always start with a process analysis that makes the ROI clear up front, so you know what you are getting into. The nice thing about well-chosen automation is that the first successful case almost always finances the next: the hours saved and the trust built make the step to the next process easier. That way you build, in steps, an organization that leaves the boring work to software and frees people up for the work where they truly make the difference.
The process that recurs most often, is rule-based, and is error-prone. Think of retyping data between systems, manual reports, or invoice follow-up. Do not automate what is most visible, but what costs the most time and errors. A daily task of an hour delivers far more than a monthly one of five minutes.
Multiply the hours saved per year by the hourly cost and set that against the build cost. A process of ten hours a week at 40 euros an hour costs well over 20,000 euros a year in labor. An automation of a few thousand euros then pays for itself within months, separate from the gain in fewer errors and faster turnaround.
No. Automate the rule, not the exception. The repetitive, rule-based steps become software, while rare edge cases that require judgment stay with a human. If you try to automate every exception, the system becomes needlessly complex and expensive. Start small, automate one process well, and expand after that.