Explore options for bill of lading processing, invoice and freight audit automation, purchase orders, ASN processing, and tariff management.
Supply chain and logistics organizations have long explored emerging technologies to improve efficiency, reduce costs, and increase visibility across increasingly complex operations. From process automation and route optimization to predictive analytics and large language models, AI has steadily become embedded in the modern supply chain technology stack.
One area gaining significant momentum is document AI. While document processing technologies have existed for years, recent advances in machine learning, computer vision, and natural language processing have made it far easier — and more scalable and reliable, when supported by quality data and validation processes — to extract, understand, and act on information embedded in logistics and supply chain documents.
As with any AI capability, the effectiveness of document AI depends on factors such as data quality, governance, and appropriate human oversight for accuracy, compliance, and trust in outputs.
Top 5 use cases for document AI in supply chain and logistics
1. Bill of lading processing
Document AI automatically extracts and validates key shipment information from bills of lading, helping to reduce manual data entry and errors. This accelerates shipment reconciliation and improves visibility across carriers and partners while still allowing for human review of exceptions and discrepancies.
2. Invoice and freight audit automation
AI extracts line item data from freight and carrier invoices and matches it against contracted rates and shipment details. This helps organizations quickly identify discrepancies, reduce overpayments, and shorten payment cycles when integrated with existing audit controls and approval workflows.
3. Purchase order and ASN processing
Document AI streamlines purchase order, advance ship notice, and packing list processing by automatically matching quantities, SKUs, and delivery dates. This enables faster receiving, more accurate inventory updates, and fewer fulfillment issues in environments with consistent document volumes and standardized formats.
4. Customs and trade compliance documentation
AI automates the extraction and validation of data from customs and trade documents, improving consistency and completeness before submission. This reduces border delays, compliance risks, and manual rework for international shipments when paired with established compliance review and regulatory processes.
5. Contract and tariff management
Document AI identifies rates, accessorial charges, and key terms buried within carrier contracts and tariffs. This helps improve contract compliance, strengthens freight audits, and increases cost transparency across transportation spend while supporting — not replacing — contract management and legal review functions.
How document AI models are trained: Example for bills of lading
Training a document AI model to process bills of lading (BOLs) begins with building a strong set of real-world documents. Because BOLs vary widely by carrier, format, and region, organizations typically gather a representative sample including scanned documents, PDFs, and digital forms.
Key data elements — such as shipper and consignee information, shipment numbers, quantities, and dates — are then identified and labeled. This step helps the model learn what information matters and where it typically appears across different layouts.
Once the documents are labeled, the model is trained to recognize patterns in both the text and structure of BOLs. Over time, it learns how to extract critical information even when formats change or data appears in unexpected locations. The model is tested against new BOLs, refined based on errors, and continuously improved as additional documents are processed.
This iterative approach works well when logistics organizations establish clear performance thresholds, monitor results over time, and incorporate human in the loop review to manage exceptions and evolving document formats.
Interested in how to get started with AI?
If this article sparked ideas around document AI — or AI use cases more broadly — we’d welcome the opportunity to continue the conversation. Whether you already have a specific use case in mind or are still exploring where AI could deliver the most value, our team can help.
Our approach focuses on evaluating where AI is practical, sustainable, and aligned with broader operational, risk, and compliance objectives — not just where it’s technically possible. We’re happy to connect you with our data science and digital team to assess readiness, prioritize use cases, and identify models and technologies supporting your business goals.