Manual shipment creation is holding you back: Why AI agents are now essential

Learn how leading logistics teams are eliminating errors and delays with AI agents. 

Global shipping is a high-stakes operation where every delay adds costs and disrupts supply chains. While congestion and weather often take the blame, a significant number of delays stem from preventable inefficiencies in shipment creation. Missing or inaccurate documentation, disconnected systems, and manual data entry errors can stall shipments before they even leave the dock. 

Conventional Transportation Management Systems (TMS) have streamlined logistics but still rely on rigid workflows that struggle to adapt to real-world complexities. Siloed data, compliance risks, and outdated processes continue to slow down operations. 

AI agents are redefining shipment creation by enabling:

• Automated documentation and data validation to eliminate errors
• Real-time compliance checks to prevent costly penalties
• Intelligent shipment planning to optimize routes and costs

In this deep dive, we’ll explore how AI-driven shipment creation is reducing delays and transforming global logistics.

Challenges in international shipment creation and TMS limitations

Creating a shipment might seem straightforward, but in global supply chains, every step involves countless decisions. From selecting the right carriers and ensuring compliance to managing costs and reducing delays, the process is anything but simple. Yet, many logistics teams still rely on rigid systems that weren’t built for today’s fast-moving market. Here’s where they fall short:

1. Increasing complexity of shipment specifications

As global trade expands, shipment specifications are becoming increasingly intricate. Different transportation modes—air, sea, and land—have unique requirements for packaging, documentation, and handling. A pharmaceutical shipment requires temperature-controlled packaging and specific labeling, while automotive shipments may involve oversized parts needing specialized handling. Legacy TMS systems rely on rigid templates that fail to accommodate evolving shipment needs. In multimodal transport, for example, failing to configure reefer containers for temperature-sensitive goods can result in regulatory non-compliance or product damage, causing costly delays. The lack of dynamic adaptability in conventional systems makes managing such complexities a significant challenge.  

2. Data fragmentation and validation gaps  

Accurate shipment creation requires structured, real-time data integration across multiple sources—emails, invoices, purchase orders, and carrier systems. However, conventional TMS platforms rely on manual entry and validation, leading to errors in dimensions, weights, HS codes, and shipping instructions. A McKinsey report found that poor data validation contributes to a 10-20% increase in operational costs for logistics providers. Without automated checks, discrepancies in shipment details result in miscommunication, delays, and compliance holds. In December 2024, U.S. Customs and Border Protection officers at the Eagle Pass Camino Real International Bridge seized $1.9 million worth of undeclared goods due to invoice misclassification. Without intelligent validation, businesses risk repeated non-compliance, financial losses, and disrupted shipments. 

These challenges intensify during peak shipping periods when conventional systems struggle to scale dynamically. Manual verification slows down shipment processing, increasing bottlenecks and missed deliveries. In 2022, peak-season shipping delays cost U.S. retailers over $5 billion in lost sales. An adaptive system that cross-references past shipment patterns detects anomalies, and enforces real-time compliance checks is essential to prevent costly errors and improve shipment accuracy.

3. Evolving trade regulations and compliance

With international trade laws changing frequently, keeping up with compliance can be a challenge for logistics teams. New tariffs, shifting customs duties, and evolving Harmonized System (HS) codes directly impact the shipment process. For example, as of January 2022, new HS provisions reclassified unmanned aircraft (drones) and certain chemicals under distinct categories aligned with the Chemical Weapons Convention (CWC). Shipments of these goods now require additional scrutiny and specific documentation for cross-border movement. Conventional TMS systems often fail to track these regulatory changes in real-time, leaving companies reliant on manual processes to ensure compliance. This not only increases the risk of shipment delays and penalties but also adds to operational complexity in navigating the ever-changing landscape of international trade laws.  

This lack of integration with up-to-date regulatory data puts businesses at risk of costly fines or shipment delays. For example, a shipment of medical supplies may be delayed at customs if the system fails to detect an updated regulation regarding product classification or safety certification. This creates an unnecessary burden on logistics teams, who are forced to manually check for compliance on every shipment, a time-consuming and error-prone process.

4. Lack of real-time integration

Conventional TMS platforms rely on static shipment requirements, requiring manual updates to accommodate changes in regulations, carrier requirements, or customer specifications. Without real-time integration with external data sources—such as updated HS codes, compliance mandates, or carrier capacity constraints—logistics teams must manually verify and input data, increasing the risk of outdated or incorrect shipment details. This IT dependency slows down shipment creation, leading to errors like incorrect documentation, misclassified goods, or overlooked compliance requirements. For example, if a new regulatory update mandates additional documentation for hazardous materials, but the system isn’t updated in time, shipments can be rejected at customs, causing costly delays. 

Unstructured communication between stakeholders further compounds these challenges. Shipment details are often exchanged via emails, spreadsheets, and phone calls, leading to discrepancies in weight, dimensions, or consignee information. When these inconsistencies go unnoticed, they result in rejected bookings, last-minute corrections, or reshipments—driving up costs and eroding efficiency. AI-driven systems address these issues by dynamically integrating external data, validating shipment details in real-time, and automating compliance checks—ensuring that shipments move smoothly without last-minute disruptions.

How AI agents streamline and optimize shipment creation for better outcomes

AI agents are autonomous decision-makers that go beyond traditional machine learning models. They possess advanced reasoning capabilities, dynamically assessing complex scenarios, identifying optimal solutions, and executing decisions in real time. Unlike static systems or rule-based automation, AI agents adapt to evolving conditions, leveraging historical patterns and real-time data to refine strategies continuously. When embedded in a TMS, they transform shipment creation from a manual, error-prone process into a self-optimizing, intelligent workflow—validating data, anticipating risks, and ensuring compliance while reducing manual intervention.

1. Efficient data extraction and pre-validation

Shipment creation begins with accurate and complete data. However, logistics teams often deal with multiple formats and inconsistent data sources—emails, PDFs, spreadsheets, and supplier portals—making manual extraction time-consuming and error-prone. AI agents automate this process by parsing unstructured data from emails, invoices, and order confirmations, extracting key shipment details such as product descriptions, dimensions, weight, special handling requirements, and carrier instructions. Additionally, AI identifies missing or inconsistent information by cross-referencing internal shipment records and supplier databases. 

For instance, if a supplier provides shipment details in an unstructured email format, AI detects missing weight and package dimensions, cross-references past shipments for similar SKUs, and suggests estimated values based on historical trends. If necessary, it flags gaps for manual review before finalizing the shipment. By automating data capture and pre-validation, AI eliminates manual data entry bottlenecks, allowing shipments to start on a clean and accurate foundation.

2. Seamless compliance and documentation

Ensuring that shipments meet regulatory and trade compliance standards is one of the most complex aspects of logistics. AI agents integrate compliance directly into shipment creation, eliminating last-minute surprises. AI automatically classifies products using HS codes, verifies them against trade regulations, and checks for restricted commodities, hazardous materials, and embargoed goods. It also ensures all required documentation is complete and accurate before submission, reducing customs delays. 

For instance, a shipment containing electronic goods destined for European markets must comply with RoHS and REACH regulations. AI verifies the SKU’s compliance status, auto-fills missing declarations, and alerts the team if specific certifications are needed. Additionally, AI prevents rejections by recognizing carrier-specific compliance preferences. If a freight provider has stricter packaging requirements for dangerous goods, AI ensures all necessary labels and documents are in place before dispatch. By integrating compliance into the shipment creation workflow, AI minimizes delays, rework, and financial penalties, enabling smoother cross-border shipments.

3. Continuous learning and adaptation

One of AI agents' unique strengths is their ability to learn from every shipment. As they process more data, they improve over time, adapting to new formats, identifying recurring errors, and refining their validation methods. For example, if a supplier repeatedly misclassifies lithium-ion batteries under a generic electronics category, an AI agent can detect this pattern, flag the incorrect classification, and ensure the correct hazardous materials declaration is applied. This proactive adjustment prevents compliance violations, fines, or customs rejections that could delay shipments. 

AI agents also recognize carrier-specific shipment requirements and automatically adjust documentation workflows. If a particular carrier consistently rejects shipments due to missing pallet configurations or incorrect temperature-control instructions, the AI agent learns from past rejections and prompts the logistics team to include the required details before submission. Over time, this adaptability reduces manual intervention, improves first-time accuracy, and ensures shipments meet carrier and regulatory expectations without unnecessary delays or costly corrections.

4. Zero-touch processing for speed and scale

In the past, creating shipments often required significant manual intervention, even for repeat shipments. This led to bottlenecks, with employees bogged down in tedious data entry and validation. AI agents remove the need for manual handling altogether. Through zero-touch processing, AI agents can fully automate shipment creation from start to finish, dramatically reducing the time it takes to create a booking. 

Zero-touch processing is particularly valuable when handling high volumes of shipments. Instead of manually managing individual shipments, logistics teams can rely on AI agents to handle a far greater number of shipments without scaling up headcount. This process allows companies to manage more shipments with the same or fewer resources, enabling greater efficiency across the board. In addition, zero-touch processing ensures that every shipment is processed in the same standardized, error-free way, reducing the variability that can result from human intervention. 

5. Predictive error prevention and intelligent data validation 

Preventing errors before they escalate is critical to maintaining smooth logistics operations. AI agents don’t just verify information—they proactively detect, flag, and correct errors using advanced pattern recognition, context-aware validations, and intelligent adjustments based on past shipment rejections. This prevents recurring mistakes, enhances shipment accuracy, and improves operational efficiency. 

For instance, if a supplier misclassifies lithium-ion batteries under general electronics, AI detects the error, assigns the correct hazardous materials declaration, and ensures all regulatory documents are included upfront, avoiding customs delays and penalties. Similarly, if a carrier frequently rejects shipments due to improper pallet stacking, AI learns from past rejections and proactively suggests the correct configuration before booking. By continuously refining its accuracy through real-world shipment data, AI minimizes rework, reduces compliance risks, and strengthens supply chain resilience—ensuring seamless, on-time deliveries with fewer disruptions.

6. Perfect audit trails and compliance assurance

With growing scrutiny around supply chain transparency and accountability, maintaining accurate audit trails has become increasingly important. AI agents ensure that every step of the shipment creation process is automatically documented. From initial booking communications to data validation, changes, and final shipment creation, AI agents capture all interactions in a transparent and accessible audit trail. 

This documentation serves as a safeguard against potential compliance issues, enabling businesses to quickly verify the accuracy of their shipments and respond to any regulatory inquiries. Moreover, because every action is logged and stored, AI agents provide instant access to an accurate record of any shipment, enabling real-time compliance verification. In case of any discrepancies or audits, this detailed trail allows businesses to quickly resolve issues and demonstrate full compliance, giving them peace of mind and protecting them from any potential legal or financial repercussions.  

Seamless shipment creation with Pando's intelligence

At Pando, we’ve worked closely with global logistics teams and seen firsthand the challenges of shipment creation—fragmented data, manual compliance checks, and limited visibility. These aren’t just inefficiencies—they’re barriers to growth and operational success.

• Audit your current process metrics and pain points to identify where AI can deliver the most immediate value for your operations.
• Standardize your data and documentation across systems to create a robust foundation for AI implementation.
• Launch a focused pilot program on specific routes or shipment types to demonstrate value and refine your approach before scaling.

At Pando, our AI agents automate data extraction, validate compliance in real-time, and continuously learn from past shipments to drive efficiency and precision. They don’t just help you manage risks—they help you prevent them. And the best part? They do it all in real-time, empowering your team to make faster, smarter decisions. 

With Pando, you’re not just adopting AI—you’re transforming your logistics ecosystem into something smarter, more efficient, and better equipped for the future. Because the future of shipment creation isn’t just about moving goods—it’s about building a resilient, intelligent logistics network that evolves with your business.