How AI Agents Are Solving Logistics’ Most Repetitive Task: Track and Trace

Why It Matters

Automating track and trace cuts operational costs, boosts visibility, and frees staff for higher‑value activities, reshaping logistics efficiency. The proven ROI accelerates broader AI adoption across supply‑chain functions.

Key Takeaways

• Manual track & trace costs up to $25 per shipment
• AI agents can automate 98% of tracking updates
• Integration with TMS provides contextual data for reliable AI
• Airlite saved one hour daily per employee
• Pilot deployments mitigate risk before full rollout

Pulse Analysis

Track and trace remains a cornerstone of supply‑chain visibility, yet its manual execution drags down productivity and inflates costs. Shippers must constantly query carrier portals, reconcile status updates, and generate proof‑of‑delivery documents, a process that can consume hours per day and add $25 per shipment in labor. The friction not only hampers customer experience but also obscures exception management, making it harder to resolve delays or billing discrepancies. As e‑commerce volumes surge, the pressure to streamline these workflows has never been greater.

Artificial intelligence agents, when tightly coupled with a transportation management system (TMS), transform track and trace from a reactive chore into a proactive service. By ingesting real‑time carrier feeds, historical shipment patterns, and company‑specific service‑level agreements, AI workers can flag anomalies, draft carrier communications, and update status dashboards without human prompting. Shipwell’s Track & Trace AI Worker exemplifies this approach: leveraging native TMS data, it achieved 98% automation for Airlite Plastics, eliminating an hour of daily manual effort and delivering near‑instant exception resolution. The integration ensures data fidelity, reduces false alerts, and builds trust among logistics teams.

Adopting AI for track and trace need not be an all‑or‑nothing gamble. A phased pilot—targeting high‑volume carriers or routes with frequent gaps—allows firms to measure accuracy, monitor compliance, and refine business rules before scaling. Detailed activity logs provide transparency, while parallel runs maintain service continuity. As more shippers demonstrate tangible savings and improved customer satisfaction, AI‑driven track and trace is poised to become a standard component of modern logistics stacks, freeing resources for strategic initiatives such as network optimization and predictive demand planning.