Many companies have an end-of-month ritual: someone exports numbers from accounting, from the ERP, from the shop, copies everything into an Excel template, checks the formulas, formats the charts, and finally emails a PDF to management. Depending on the company this takes half a day to two days, every month, usually done by the same person. The report looks the same every month. That is exactly what makes it the most rewarding automation candidate we know.

Why the report takes so long even though it is routine

The time rarely goes into thinking. It goes into collecting: four systems, four export formats, four checks that the date range is right. Then reconciling, because the shop total does not match accounting, until it turns out one system counts cancellations differently. Then formatting, because the template breaks when there are twelve product categories instead of eleven. The pattern is widespread: according to a survey by insightsoftware and Hanover Research (2024) of 500 finance professionals in Europe and North America, three quarters of finance teams put at least five to six hours a week into recreating reports, up to 300 hours a year.

There is a quieter cost on top: the report depends on one person. When they are on vacation or sick, the report slips, or a stand-in takes twice as long and does not trust their own numbers.

What automation concretely means here

Automating does not mean an AI “writes” the report. It means building the three mechanical steps properly once, instead of repeating them by hand every month:

Connection: the source systems are hooked up once, via API where one exists, via automated export-import where not
Calculation logic: the formulas from the Excel template are translated once and documented, including the edge cases like credit notes and cancellations
Output: the finished report appears on demand or on a schedule, as a dashboard view, a PDF, or both

The side effect: the numbers become checkable

A hand-built report is hard to challenge and hard to verify at the same time. When a number looks odd, a hunt through exports and formula chains begins, often taking longer than the report itself.

In an automated report, the calculation path is fixed. Behind the revenue total sit the individual invoices; one click shows them. “This number can’t be right” turns into “show me the entries behind it”. In our experience this changes monthly meetings more than the saved hours do: people discuss actions instead of whether the spreadsheet is correct.

What it costs, honestly calculated

The range is wide, because it depends almost entirely on the source systems. Two modern systems with clean APIs and a manageable report: lower end of the range, a project of a few thousand euros. An older ERP without a documented interface, plus Excel logic grown over ten years of special cases: considerably more, and the biggest chunk is not the technology but excavating the rules that so far exist only in one person’s head.

The counter-calculation is simple. If the report costs a day and a half per month today, that is roughly €5,000 a year at €35 fully loaded cost per hour, plus the risk of the one irreplaceable person. An automation in the mid four-figure to low five-figure range typically pays for itself within one to three years. If the report already takes an hour a month, it does not pay off, and we will tell you so.

When you should leave it alone

There are three honest counter-arguments. First: nobody actually reads the report. Then automation is just a cheaper way to produce something pointless; cancel the report instead. Second: the report changes fundamentally every month because the business itself is shifting. Automation only pays once a format has settled. Third: the source system is being replaced in six months. Then build the connection once, after the switch.

A fourth case sounds like a counter-argument but is not: “our data is too messy.” The opposite is true. The automation attempt exposes exactly where the data is dirty and forces decisions that were overdue anyway.

The pragmatic way in

You do not have to automate the whole report at once. The most sensible start is the part that costs the most time or goes wrong most often, usually the revenue and open-invoices pages fed from accounting and ERP. The rest of the report keeps being built by hand for a while and migrates piece by piece.

That way you see a result within weeks, and if it turns out mid-project that one data source is more work than expected, it is a small course correction instead of a project crisis.