The Setup
We have a specific client — an e-commerce company doing about $15 million annually. They process thousands of orders every day across multiple channels: Shopify, Amazon, their own website.
Every order creates an entry in three different systems. The integration between these systems was broken. So someone had to manually enter order data into the accounting system. Every single order.
One person, 30 hours a week, just data entry. At $50/hour fully-loaded, that's $1,500 per week or $78,000 per year.
The client wanted to hire a second person to handle the volume. Instead, we pitched: "What if we build an agent to do this?"
Why This Was Perfect
The task was: Pull order from Shopify, match it to Amazon, find it in the legacy system, pull the relevant data, enter it into accounting software, update inventory status.
That's 100% rules-based. Highly repetitive. High volume. Clear success metric: Orders processed accurately.
This was the lowest-hanging fruit imaginable.
What We Built
An agent that:
1. Checks for new orders every hour
2. Pulls from Shopify/Amazon
3. Matches orders across systems
4. Extracts relevant data
5. Enters into accounting system
6. Updates inventory
7. Flags anything that doesn't match (human review)
Build cost: $8,000. Development time: 3 weeks.
The Results
Agent handles 95% of orders automatically. 5% have matching issues or data inconsistencies and need human review.
That person still works 8 hours a week reviewing the 5% and handling exceptions. The other 22 hours per week? Freed up.
Savings: 22 hours/week x $50/hour = $1,100 per week = $57,200 per year.
Project cost: $8,000. Annual savings: $57,200. ROI: 615%.
Payback: 10 days.
Why 2 Weeks?
The agent went live on a Monday. By the following Monday, we had solid data that it was working correctly. We calculated the time savings. $8,000 was paid for in less than 2 weeks of labor savings.
Usually projects take longer because the ROI is more subtle. "We save 6 hours a week across 4 people." The benefit is distributed and harder to measure. This one was direct: One person's job became mostly monitoring instead of execution.
The Client's Next Question
"How many other processes like this do we have?" Suddenly they're thinking about what else is automatable.
That's how you build a book of AI work. One win leads to the next conversation.
What This Teaches
Pick problems where the volume is high and the rules are clear. Data entry, matching, categorization, basic calculations — these are gold.
If you find something where one person is doing the same task thousands of times a month, that's your ROI goldmine. That's where the 2-week payback lives.