The 6-Week Deployment That Replaced a 3-Person Reporting Function

A 220-person logistics company was running 14 recurring reports a week. All manual. Three analysts buried in spreadsheets, stakeholders waiting days for numbers that were already stale by the time they arrived.

We mapped the whole operation in two weeks. Built and deployed in four. Here is exactly what we did and what changed.

Weeks 1 and 2: Map before you build

Before touching a single system, we audited every report. Who ordered it. Who actually read it. What decision it was meant to support. What data it pulled and how much manual cleaning happened before it went out.

What we found: four of the fourteen reports were produced weekly but only reviewed monthly. Six pulled from the same underlying dataset but formatted differently for different departments. Two required genuine judgment. The rest were mechanical repetition.

Most companies skip this step. They automate what exists instead of questioning whether it should exist. We do not skip it.

Weeks 3 and 4: The build

We built a centralised reporting infrastructure connected to their WMS, TMS, and finance platform. Data normalisation happened at ingestion. Reports were generated, formatted, and distributed automatically on schedule — the right format to the right person without anyone touching it.

• 12 of 14 reports fully automated
• 2 reports kept human ownership for strategic commentary
• Report generation time dropped from 3.5 hours average to under 4 minutes
• All stakeholders moved to a single live dashboard
• Full historical archive built and indexed for instant retrieval

Weeks 5 and 6: Deployment

Deployment is where most vendors hand over a manual and disappear. We stayed. Every stakeholder who had been receiving manual reports was walked through the new system individually. We handled the edge cases, addressed the resistance, and remained available for two full weeks post-launch.

The three analysts were not replaced. Two moved onto strategic projects that had been sitting on the backlog for over a year because there was never capacity. One stepped into a data governance role the business had needed for a long time but could not justify building.

The real cost of waiting

At fully loaded cost, the reporting function was consuming roughly €180k a year in human capital to produce outputs a well-built system generates in minutes. The deployment paid for itself in under three months.

But the harder cost to measure was this: every week, leadership was making decisions on data that was three to five days old. At scale, that lag compounds. You cannot put a number on it but it is real and it is expensive.

Six weeks. Twelve automated reports. Three people freed to do work that actually moves the business.

That is what execution looks like.

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Ryon.ai builds AI systems for large companies that need results, not slide decks. If your reporting function is a bottleneck, we can fix it. ryon.ai