“Where's my shipment?" sounds like a simple question. But for logistics teams, answering it can take hours every day–calling carriers, checking portals and updating customers. This is the reality of manual track and trace, one of the most vital yet time-intensive tasks in shipping operations.

What Track and Trace Actually Means

Track and trace refers to monitoring and reporting a shipment's movement from origin to final destination. While the terms are used interchangeably, they describe different activities:

• Tracking is the ongoing process of monitoring a shipment location and status throughout its journey in real time.
• Tracing involves reviewing a shipment's movement history to understand where specific events occurred, like loaded, unloaded, delayed.

Both activities usually happen together, creating a complete picture of shipment status and history that feeds into proof of delivery (POD) documentation and exception management workflows.

The Role of Your Transportation Management System

A transportation management system (TMS) is software that helps companies plan, execute and optimize the movement of goods. Think of it as the central hub for all shipment data—carrier rates, delivery schedules, tracking updates, PODs and performance history.

For track and trace specifically, a TMS holds the context that makes monitoring meaningful: most commonly used carriers, customer service level commitments, typically occurring exceptions and delays impact downstream operations. Without centralized data, tracking becomes a series of disconnected status updates rather than actionable intelligence.

The Hidden Cost of Manual Tracking

Tracking enhances customer experience, tracing is essential for issue resolution and operational efficiency. Together, they're critical for improved visibility, enhanced productivity, better customer service and risk mitigation. But when these tasks are done manually, they can cost up to $25 per shipment in staff time alone.

Where AI Can Help

AI agents can monitor shipments 24/7 in real-time to instantly detect issues, initiating resolutions before they become problems with carriers and drivers.

But not all AI implementations are created equal. “For simple, low-risk use cases, standalone AI tools can work just fine,” explains Greg Price, CEO and Co-founder of Shipwell. “But when you’re talking about core workflows like shipment monitoring, exception management and planning, these kinds of decisions require AI agents to understand the bigger picture. Even analysts agree that integration with a TMS provides the data fidelity and operational context that turns AI into a reliable tool your team can trust and verify.”

Airlite Plastics' 98% Automation Success

Airlite Plastics implemented AI for track and trace under the leadership of Jeremy Forster, Senior Director of Supply Chain–achieving near-complete automation in one of logistics’ most manual, interruption-heavy workflows.

Rather than building a standalone AI agent, Forster joined the beta for the Shipwell platform’s Track & Trace AI Worker. Because the organization already runs on Shipwell, the worker had native access to shipment data, business rules and historical context–delivering higher data quality and more dependable automation than a disconnected solution could provide.

Forster configured outreach settings to match the way his team currently operates. To build confidence in their new digital teammate, they ran the AI worker in parallel to their traditional workflow and reviewed messages drafted to carriers before sending. Within one week, the Track & Trace AI Worker reached 98% compliance. With that validation, the team stepped away from manual monitoring and redirected their time toward higher-value tasks.

"The ROI was immediate – the Track and Trace AI worker is already handling about 98% of the tracking updates we used to do manually,” said Jeremy Forster, Senior Director of Supply Chain at Airlite Plastics. “We've practically eliminated an hour a day of sending out carrier emails and uncovering delivery details for invoicing."

Over the course of the beta, Airlite experienced measurable operational gains:

• 48% of monitored shipments required action, all of which were fully automated
• 1 hour saved per day per employee through autonomous shipment monitoring (~15% yearly)
  • 30 minutes per day eliminated from manual carrier tracking emails
  • 30 minutes per day saved by proactively collecting delivery confirmations for invoice verification

Getting Started Without Risk

Logistics teams evaluating AI for track and trace don't need to commit to an all-or-nothing rollout, and in fact, shouldn’t. The approach that works best begins with a limited pilot, like monitoring one or two high-volume carriers or those with frequent tracking gaps. Only after validating performance with real shipments, real carriers and real outcomes should organizations expand.

Shipwell's Track & Trace AI Worker adapts to each team's pace for modes monitored, actions taken and levels of communication. Whether starting with two carriers or twenty, teams control the rollout, verify the results through detailed activity logs and decide when to scale. This allows trust to build gradually, without disrupting existing processes.

If track and trace is consuming valuable hours that could be spent on strategic work, AI can reclaim that time. The technology exists. The results are proven. The question is whether your team is ready to stop chasing shipments and start solving bigger problems.