Automating 120 Hours of Manual Work: What I Learned

The situation

A mid-sized retailer was selling through both their Shopify store and a wholesale channel. Every time certain wholesale orders came through, someone on the team would open Shopify, cross-reference the order against their pricing spreadsheet, manually create an invoice in Xero, and email it to the client.

Every single day.

By their estimate, this took about 30 minutes each morning. Multiply that by five days a week, 50 weeks a year, and you get 125 hours. (You can run the same sum on your own tasks with the manual work calculator.) More than three full work weeks, spent copying numbers between two screens.

What surprised me in discovery

When I sat down and actually mapped the process, the 30 minutes was generous. On busy days, such as after a campaign or at month end, it was closer to an hour. And the error rate was higher than anyone wanted to admit. Mismatched line items, wrong tax codes, invoices sent to the wrong contact. Each mistake spawned a correction chain that ate even more time.

The other surprise: the “rules” for which orders got invoices weren’t documented anywhere. They lived in one person’s head. If she was sick, the invoices didn’t go out.

The build

The automation we built (the full write-up is in the Xero and Shopify case study) was simple in concept, the kind of thing that sounds obvious in hindsight:

1. Shopify sends an automatic message (a “webhook”) the moment a qualifying order is created
2. A small program catches that message, applies the pricing rules, and arranges the details the way Xero expects them
3. The invoice is created in Xero through its connection for other software (its “API”), with the correct contact, line items, tax codes, and due dates
4. If an invoice fails to create, the team gets a Slack alert with the order details

The whole thing runs without anyone touching it. Instead of building invoices one by one each morning, the team only steps in when an alert comes through.

What I’d do differently

I’d spend more time on the error-handling layer upfront. We built the happy path first and bolted on error handling after launch. It worked, but the error messages weren’t as helpful as they could have been in those first couple of weeks. Now I build the error states before the success states, it’s a better order of operations.

The broader lesson

Most manual processes that involve “take data from system A, transform it slightly, put it in system B” are automatable. Not just in theory, but in practice. An automatic message from Shopify, a small program, and a couple of requests to Xero. That’s it.

The hard part isn’t the technology. It’s the discovery: sitting down, mapping the actual process (not the one in the documentation, the one people actually follow), identifying the rules and edge cases, and designing something that handles them reliably.

That’s the work I enjoy most, and it’s where the real value is. I’ve written up what an operations-mapping session actually looks like, and how to work out what a manual task is really costing you. The same approach, applied to a much bigger system, is the SAP to Shopify integration for Bont Cycling.

This is the core of my workflow automation and integration work.

Got a process that’s eating hours every week? Let’s talk about it.