AI and machine learning can automate bill of lading (BoL) ingestion, data extraction, and validation, reducing manual handling and rework. In this case study, WNS Malkom helped a leading North American less-than-truckload (LTL) carrier improve freight documentation workflows and service quality.

Carriers can use automated document intake, configurable workflows, and real-time monitoring to manage irregular document arrivals and demand spikes. A scalable approach helps maintain processing throughput, reduce bottlenecks, and support timely deliveries without relying exclusively on manual capacity.p>

AI-driven extraction combined with business-rule validation can identify missing, inconsistent, or incorrect information before it reaches downstream systems. Human review can be retained for low-confidence cases and exceptions, helping balance automation speed with data quality and operational control.

Real-time dashboards and monitoring can provide visibility into document volumes, processing status, turnaround time, errors, and outstanding exceptions. These insights help operations leaders identify bottlenecks, allocate resources, and take corrective action before documentation delays affect deliveries.

Decision-makers should track average handling time (AHT), processing cost per document, extraction accuracy, rework rates, throughput, and service-level performance. Establishing baselines before implementation helps quantify business value and prioritize further automation. WNS (Part of Capgemini) combines logistics expertise and AI-led automation to help organizations improve efficiency and service quality.