# How to reduce dispatcher workload

Reducing dispatcher workload starts with separating repeatable preparation from decisions that require context. A dispatcher may spend the day copying load details, checking statuses, chasing missing documents, resolving late arrivals, and coordinating people. The most useful improvement is often a shorter path from signal to reviewed decision.

## Measure a representative week

For each load, record minutes spent on intake, assignment, status checks, exception handling, and post-delivery follow-up. Count rework caused by missing or wrong information. Include interruptions that do not fit neatly into a load. This baseline helps distinguish a real reduction from work shifted to another person.

## Then redesign one handoff

Take rate-confirmation entry. An assistant can prepare fields, but the dispatcher should see the source and verify exceptions. Or take status checks: an exception queue can replace repeated polling only if it catches meaningful changes and makes stale data clear. Test one narrow workflow with normal and messy loads before extending it.

## Questions to ask a vendor

- How much work is prepared automatically, and what still requires a person?
- Can the dispatcher edit or reject the proposed action?
- Does the system show a source and decision history?
- What happens when the data connection is delayed or a load falls outside the usual pattern?

RunaboutX's [AI Dispatcher](/features/ai-dispatcher/) and [AI Tracking](/features/ai-tracking/) pages describe the workflow examples to evaluate in a demo of the available product. Use the [TMS ROI calculator](/resources/calculators/tms-roi/) with your measured minutes rather than a vendor-provided savings assumption.

## What a California fleet reported

A California trucking fleet shared this feedback after using RunaboutX during an October 1–14, 2026 pilot:

> “RunaboutX has made our dispatchers significantly more efficient. With fewer repetitive follow-ups and manual tasks, each dispatcher has more time to focus on what matters—keeping loads moving and customers happy. It’s made daily operations less stressful, improved team productivity, and helped us deliver a smoother, more reliable experience for our customers.”

**A California trucking fleet**
Pilot: October 1–14, 2026 · 14 days.

[Read the full pilot story](/resources/case-studies/fleet-pilot/) for the customer context. Use your own timed sample to understand how a change affects preparation, review, interruptions, and rework in your operation. Explore the available [AI TMS](/ai-tms/) or [book a demo](/#contact) with a representative load.

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Canonical: https://www.runaboutx.com/resources/learn/reduce-dispatcher-workload/
Last reviewed: 2026-10-14