What 3 Published n8n Case Studies Reveal About Realistic Automation ROI

Why This Case Study Matters

Vendor case studies are easy to dismiss as marketing. But when you line up several independently published n8n deployments side by side — different industries, different team sizes, written up separately on Medium and n8n's own case study library — a consistent shape emerges in what gets automated first and why the ROI lands where it does.

Three Published Deployments

Delivery Hero reports saving 200 hours per month from a single workflow — internal reporting suggests this came from consolidating a recurring, multi-system data-pull-and-report task that previously required manual cross-referencing across tools.

Unbabel, a language-ops company, reduced manual operational work by 51% by connecting internal tools, client systems, and QA processes through n8n — replacing what had been a set of disconnected manual handoffs between systems that didn't natively talk to each other.

Koralplay automated 70% of payment support tickets, saving 40+ hours weekly and reporting a 25:1 ROI — a number achievable specifically because payment support tickets tend to be highly templated (refund status, failed payment retries, invoice requests).

The Research Takeaway

Across all three, the automated task shares three traits: high volume, low judgment variance, and multi-system data stitching. None of these teams started by automating their hardest or highest-stakes decision — they started with the workflow that was (a) repeated often enough to matter, (b) mostly rule-based, and (c) currently done manually because it required logging into 3+ separate systems, not because it required expert judgment.

That's the same prioritization logic RudraAI applies when scoping a new automation engagement: find the highest-volume, most system-fragmented manual process first — it's usually where the ROI shows up fastest, as in our own Appointment Booking Automation and Email Marketing Automation case studies.