The problem: 8 hours of data entry, every single day
The operator ran a mid-size freight brokerage serving US regional carriers. Their dispatcher workflow looked like this: pull data from three separate platforms, manually reconcile shipment records, populate weight tables in a spreadsheet, then push updates to their TMS. Repeat, all day.
Eight hours of dispatching. Six of that was data entry. Two people doing the same thing, every day, seven days a week.
They weren't slow workers. The process itself was broken.
What we built
The AI automatizacije solution had three components:
- Real-time data sync: A custom integration layer that pulls from all three source platforms and reconciles records automatically, using fuzzy matching to handle inconsistent carrier IDs.
- Automated weight table population: A rules engine that reads shipment specs, applies freight class logic, and writes directly to the TMS — no human in the loop.
- Exception routing: Anything the system can't match with 95%+ confidence gets flagged and routed to a human review queue. Everything else goes through automatically.
Build time: 7 days. Zero downtime during cutover.
The result
Dispatcher manual work dropped from 480 minutes per day to under 24 minutes — a 95% reduction. The two dispatchers now spend their time on carrier relationships and exception handling, not spreadsheets.
Monthly labor cost savings were significant. But the bigger win was accuracy: the automated system processes data with a 0.2% error rate, down from an estimated 3-4% manual error rate that was quietly bleeding money through mis-billed shipments.
What made this possible
Standard RPA would have broken on the first platform update. This was built on a flexible AI automation layer that adapts to minor schema changes without a full rebuild. The system has been running for six months without a human-triggered intervention.
This is what AI automatizacije looks like when it's built around the actual business logic — not just "automate the clicks."
Is your dispatcher workflow a candidate?
If your team is manually moving data between platforms, populating tables, or reconciling records — this is almost certainly automatable within 7–14 days. The ROI math is straightforward: hours saved × fully-loaded hourly rate × 250 working days.